BEIJING AIR POLLUTION PREDICTION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge,ElasticNet\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
    "from sklearn.model_selection import KFold,GridSearchCV,cross_val_score\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import RFE\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns= None\n",
    "\n",
    "#plt.rcParams(figure.figsize) = (15,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REF_NO</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   REF_NO  year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "0       1  2013      3    1     0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7   \n",
       "1       2  2013      3    1     1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1   \n",
       "2       3  2013      3    1     2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1   \n",
       "3       4  2013      3    1     3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4       5  2013      3    1     4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "0  1023.0 -18.8   0.0  NNW   4.4  Aotizhongxin  \n",
       "1  1023.2 -18.2   0.0    N   4.7  Aotizhongxin  \n",
       "2  1023.5 -18.2   0.0  NNW   5.6  Aotizhongxin  \n",
       "3  1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4  1025.2 -19.5   0.0    N   2.0  Aotizhongxin  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading imput data:\n",
    "\n",
    "df_train = pd.read_csv(\"beijing_train.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 403776 entries, 0 to 403775\n",
      "Data columns (total 18 columns):\n",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   REF_NO   403776 non-null  int64  \n",
      " 1   year     403776 non-null  int64  \n",
      " 2   month    403776 non-null  int64  \n",
      " 3   day      403776 non-null  int64  \n",
      " 4   hour     403776 non-null  int64  \n",
      " 5   PM2.5    395301 non-null  float64\n",
      " 6   PM10     397554 non-null  float64\n",
      " 7   SO2      395000 non-null  float64\n",
      " 8   NO2      391917 non-null  float64\n",
      " 9   CO       383515 non-null  float64\n",
      " 10  O3       390769 non-null  float64\n",
      " 11  TEMP     403512 non-null  float64\n",
      " 12  PRES     403511 non-null  float64\n",
      " 13  DEWP     403507 non-null  float64\n",
      " 14  RAIN     403515 non-null  float64\n",
      " 15  wd       402387 non-null  object \n",
      " 16  WSPM     403538 non-null  float64\n",
      " 17  station  403776 non-null  object \n",
      "dtypes: float64(11), int64(5), object(2)\n",
      "memory usage: 55.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NULLS ARE PRESENT IN FEW COLUMNS.\n",
    "### DATATYPE OF YEAR,MONTH,DAY CAN BE CONVERTED TO OBJECT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>REF_NO</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33649</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.7</td>\n",
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       "      <td>-6.1</td>\n",
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       "      <td>ENE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33650</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>497.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33651</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>469.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>1022.5</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33652</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>425.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>1022.5</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33653</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>468.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1021.9</td>\n",
       "      <td>-5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   REF_NO  year  month  day  hour   PM10   SO2    NO2      CO    O3  TEMP  \\\n",
       "0   33649  2017      1    1     0  496.0   8.0  121.0  6100.0   4.0  -4.7   \n",
       "1   33650  2017      1    1     1  497.0   7.0  113.0  6300.0   4.0  -5.2   \n",
       "2   33651  2017      1    1     2  469.0   7.0  111.0  6200.0   4.0  -5.4   \n",
       "3   33652  2017      1    1     3  425.0   6.0  109.0  5900.0   3.0  -5.2   \n",
       "4   33653  2017      1    1     4  468.0  10.0  123.0  7700.0  16.0  -5.0   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "0  1022.1  -6.1   0.0  ENE   1.0  Aotizhongxin  \n",
       "1  1022.3  -6.4   0.0    E   0.7  Aotizhongxin  \n",
       "2  1022.5  -6.5   0.0  ENE   1.1  Aotizhongxin  \n",
       "3  1022.5  -6.2   0.0    E   1.1  Aotizhongxin  \n",
       "4  1021.9  -5.8   0.0  ENE   1.0  Aotizhongxin  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"beijing_test.csv\")\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16728 entries, 0 to 16727\n",
      "Data columns (total 17 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   REF_NO   16728 non-null  int64  \n",
      " 1   year     16728 non-null  int64  \n",
      " 2   month    16728 non-null  int64  \n",
      " 3   day      16728 non-null  int64  \n",
      " 4   hour     16728 non-null  int64  \n",
      " 5   PM10     16728 non-null  float64\n",
      " 6   SO2      16728 non-null  float64\n",
      " 7   NO2      16728 non-null  float64\n",
      " 8   CO       16728 non-null  float64\n",
      " 9   O3       16728 non-null  float64\n",
      " 10  TEMP     16728 non-null  float64\n",
      " 11  PRES     16728 non-null  float64\n",
      " 12  DEWP     16728 non-null  float64\n",
      " 13  RAIN     16728 non-null  float64\n",
      " 14  wd       16728 non-null  object \n",
      " 15  WSPM     16728 non-null  float64\n",
      " 16  station  16728 non-null  object \n",
      "dtypes: float64(10), int64(5), object(2)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NO NULLS IN TEST DATASET\n",
    "### DATATYPE OF YEAR,MONTH,DAY,HOUR can be converted to OBJECT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning correct datatype for columns: year,month,day from int64 to object\n",
    "\n",
    "#train and test dataset\n",
    "\n",
    "\n",
    "df_train['year'] = df_train['year'].astype(np.object)\n",
    "df_train['month'] = df_train['month'].astype(np.object)\n",
    "df_train['day'] = df_train['day'].astype(np.object)\n",
    "df_train['hour'] = df_train['hour'].astype(np.object)\n",
    "\n",
    "df_test['year'] = df_test['year'].astype(np.object)\n",
    "df_test['month'] = df_test['month'].astype(np.object)\n",
    "df_test['day'] = df_test['day'].astype(np.object)\n",
    "df_test['hour'] = df_test['hour'].astype(np.object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 POINT SUMMARY OF TRAIN AND TEST DATA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REF_NO</th>\n",
       "      <td>403776.0</td>\n",
       "      <td>16824.500000</td>\n",
       "      <td>9713.352953</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>8412.75</td>\n",
       "      <td>16824.5</td>\n",
       "      <td>25236.25</td>\n",
       "      <td>33648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>395301.0</td>\n",
       "      <td>79.248275</td>\n",
       "      <td>79.146708</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>21.00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>397554.0</td>\n",
       "      <td>104.327897</td>\n",
       "      <td>90.136400</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>36.00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>145.00</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>395000.0</td>\n",
       "      <td>15.733060</td>\n",
       "      <td>21.739455</td>\n",
       "      <td>0.2856</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>391917.0</td>\n",
       "      <td>50.352785</td>\n",
       "      <td>34.771910</td>\n",
       "      <td>1.0265</td>\n",
       "      <td>23.00</td>\n",
       "      <td>43.0</td>\n",
       "      <td>71.00</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>383515.0</td>\n",
       "      <td>1214.843339</td>\n",
       "      <td>1124.285676</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>500.00</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>390769.0</td>\n",
       "      <td>58.119327</td>\n",
       "      <td>57.375966</td>\n",
       "      <td>0.2142</td>\n",
       "      <td>11.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>83.00</td>\n",
       "      <td>1071.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP</th>\n",
       "      <td>403512.0</td>\n",
       "      <td>14.088899</td>\n",
       "      <td>11.303534</td>\n",
       "      <td>-19.9000</td>\n",
       "      <td>4.00</td>\n",
       "      <td>15.4</td>\n",
       "      <td>23.50</td>\n",
       "      <td>41.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRES</th>\n",
       "      <td>403511.0</td>\n",
       "      <td>1010.282534</td>\n",
       "      <td>10.356778</td>\n",
       "      <td>982.4000</td>\n",
       "      <td>1002.00</td>\n",
       "      <td>1009.8</td>\n",
       "      <td>1018.30</td>\n",
       "      <td>1042.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEWP</th>\n",
       "      <td>403507.0</td>\n",
       "      <td>3.157291</td>\n",
       "      <td>13.617273</td>\n",
       "      <td>-43.4000</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>4.2</td>\n",
       "      <td>15.50</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIN</th>\n",
       "      <td>403515.0</td>\n",
       "      <td>0.067052</td>\n",
       "      <td>0.837845</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WSPM</th>\n",
       "      <td>403538.0</td>\n",
       "      <td>1.718380</td>\n",
       "      <td>1.237965</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count          mean          std       min      25%      50%  \\\n",
       "REF_NO  403776.0  16824.500000  9713.352953    1.0000  8412.75  16824.5   \n",
       "PM2.5   395301.0     79.248275    79.146708    2.0000    21.00     55.0   \n",
       "PM10    397554.0    104.327897    90.136400    2.0000    36.00     83.0   \n",
       "SO2     395000.0     15.733060    21.739455    0.2856     2.00      7.0   \n",
       "NO2     391917.0     50.352785    34.771910    1.0265    23.00     43.0   \n",
       "CO      383515.0   1214.843339  1124.285676  100.0000   500.00    900.0   \n",
       "O3      390769.0     58.119327    57.375966    0.2142    11.00     45.0   \n",
       "TEMP    403512.0     14.088899    11.303534  -19.9000     4.00     15.4   \n",
       "PRES    403511.0   1010.282534    10.356778  982.4000  1002.00   1009.8   \n",
       "DEWP    403507.0      3.157291    13.617273  -43.4000    -8.00      4.2   \n",
       "RAIN    403515.0      0.067052     0.837845    0.0000     0.00      0.0   \n",
       "WSPM    403538.0      1.718380     1.237965    0.0000     0.90      1.4   \n",
       "\n",
       "             75%      max  \n",
       "REF_NO  25236.25  33648.0  \n",
       "PM2.5     110.00    999.0  \n",
       "PM10      145.00    999.0  \n",
       "SO2        19.00    500.0  \n",
       "NO2        71.00    290.0  \n",
       "CO       1500.00  10000.0  \n",
       "O3         83.00   1071.0  \n",
       "TEMP       23.50     41.6  \n",
       "PRES     1018.30   1042.8  \n",
       "DEWP       15.50     29.1  \n",
       "RAIN        0.00     72.5  \n",
       "WSPM        2.20     13.2  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>403776</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>105408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>403776</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>35712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>403776</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>13248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>403776</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>16824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd</th>\n",
       "      <td>402387</td>\n",
       "      <td>16</td>\n",
       "      <td>NE</td>\n",
       "      <td>40049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>403776</td>\n",
       "      <td>12</td>\n",
       "      <td>Guanyuan</td>\n",
       "      <td>33648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count unique       top    freq\n",
       "year     403776      4      2016  105408\n",
       "month    403776     12        12   35712\n",
       "day      403776     31        16   13248\n",
       "hour     403776     24        23   16824\n",
       "wd       402387     16        NE   40049\n",
       "station  403776     12  Guanyuan   33648"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe(include= np.object).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REF_NO</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>34350.832676</td>\n",
       "      <td>406.651831</td>\n",
       "      <td>33649.000</td>\n",
       "      <td>33999.0</td>\n",
       "      <td>34349.0</td>\n",
       "      <td>34699.25</td>\n",
       "      <td>35064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>110.716643</td>\n",
       "      <td>123.370127</td>\n",
       "      <td>3.000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>153.00</td>\n",
       "      <td>994.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>18.130918</td>\n",
       "      <td>19.297973</td>\n",
       "      <td>1.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>57.198290</td>\n",
       "      <td>41.855035</td>\n",
       "      <td>2.000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>84.00</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>1584.660450</td>\n",
       "      <td>1740.982006</td>\n",
       "      <td>100.000</td>\n",
       "      <td>400.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>2100.00</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>39.935198</td>\n",
       "      <td>31.398917</td>\n",
       "      <td>1.000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>64.00</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>0.346082</td>\n",
       "      <td>4.706547</td>\n",
       "      <td>-13.425</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>2.90</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRES</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>1021.839977</td>\n",
       "      <td>6.272443</td>\n",
       "      <td>997.100</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>1026.25</td>\n",
       "      <td>1037.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEWP</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>-13.451417</td>\n",
       "      <td>6.377738</td>\n",
       "      <td>-31.700</td>\n",
       "      <td>-18.7</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-8.10</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIN</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>0.002469</td>\n",
       "      <td>0.035825</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WSPM</th>\n",
       "      <td>16728.0</td>\n",
       "      <td>2.003437</td>\n",
       "      <td>1.406874</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.60</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count          mean          std        min      25%      50%  \\\n",
       "REF_NO  16728.0  34350.832676   406.651831  33649.000  33999.0  34349.0   \n",
       "PM10    16728.0    110.716643   123.370127      3.000     24.0     68.0   \n",
       "SO2     16728.0     18.130918    19.297973      1.000      5.0     12.0   \n",
       "NO2     16728.0     57.198290    41.855035      2.000     21.0     51.0   \n",
       "CO      16728.0   1584.660450  1740.982006    100.000    400.0    900.0   \n",
       "O3      16728.0     39.935198    31.398917      1.000      9.0     38.0   \n",
       "TEMP    16728.0      0.346082     4.706547    -13.425     -2.8     -0.2   \n",
       "PRES    16728.0   1021.839977     6.272443    997.100   1018.0   1022.0   \n",
       "DEWP    16728.0    -13.451417     6.377738    -31.700    -18.7    -13.0   \n",
       "RAIN    16728.0      0.002469     0.035825      0.000      0.0      0.0   \n",
       "WSPM    16728.0      2.003437     1.406874      0.000      1.1      1.6   \n",
       "\n",
       "             75%      max  \n",
       "REF_NO  34699.25  35064.0  \n",
       "PM10      153.00    994.0  \n",
       "SO2        25.00    300.0  \n",
       "NO2        84.00    276.0  \n",
       "CO       2100.00  10000.0  \n",
       "O3         64.00    500.0  \n",
       "TEMP        2.90     16.2  \n",
       "PRES     1026.25   1037.6  \n",
       "DEWP       -8.10      0.9  \n",
       "RAIN        0.00      1.1  \n",
       "WSPM        2.60     12.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>16728</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>16728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>16728</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>16728</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>16728</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd</th>\n",
       "      <td>16728</td>\n",
       "      <td>16</td>\n",
       "      <td>NE</td>\n",
       "      <td>3699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>16728</td>\n",
       "      <td>12</td>\n",
       "      <td>Dingling</td>\n",
       "      <td>1407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count unique       top   freq\n",
       "year     16728      1      2017  16728\n",
       "month    16728      2         1   8886\n",
       "day      16728     31         8    576\n",
       "hour     16728     24         9    707\n",
       "wd       16728     16        NE   3699\n",
       "station  16728     12  Dingling   1407"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe(include= np.object).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TEST DATA CONTAINS DATA FROM YEAR 2017 ONLY. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NULL PROFILING:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REF_NO     0.000000\n",
       "PM2.5      1.974287\n",
       "PM10       1.816482\n",
       "SO2        3.007737\n",
       "NO2        1.052701\n",
       "CO         2.560662\n",
       "O3         1.635164\n",
       "TEMP      -0.168698\n",
       "PRES       0.151948\n",
       "DEWP      -0.250022\n",
       "RAIN      29.440245\n",
       "WSPM       1.625270\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### CHECKING SKEW OF NUMERICAL COLUMNS: TRAIN DATASET\n",
    "\n",
    "df_train_num = df_train.select_dtypes(np.number)\n",
    "df_train_num.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SKEW OF TRAIN DATASET\n",
    "#### COLUMN : RAIN is very highly positively skewed.\n",
    "#### COLUMN: SO2 AND CO are also positively skewed\n",
    "#### COLUMN: PM2.5,PM10,NO2,O3,WSPM are moderately positively skewed.\n",
    "#### COLUMN: TEMP and DEWPT are slightly negatively skewed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "df_train_num.hist()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Null count</th>\n",
       "      <th>NUll%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REF_NO</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WSPM</th>\n",
       "      <td>238</td>\n",
       "      <td>0.058944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIN</th>\n",
       "      <td>261</td>\n",
       "      <td>0.064640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP</th>\n",
       "      <td>264</td>\n",
       "      <td>0.065383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRES</th>\n",
       "      <td>265</td>\n",
       "      <td>0.065630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEWP</th>\n",
       "      <td>269</td>\n",
       "      <td>0.066621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd</th>\n",
       "      <td>1389</td>\n",
       "      <td>0.344003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>6222</td>\n",
       "      <td>1.540953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>8475</td>\n",
       "      <td>2.098936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>8776</td>\n",
       "      <td>2.173482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>11859</td>\n",
       "      <td>2.937024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>13007</td>\n",
       "      <td>3.221341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>20261</td>\n",
       "      <td>5.017881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Null count     NUll%\n",
       "REF_NO            0  0.000000\n",
       "hour              0  0.000000\n",
       "day               0  0.000000\n",
       "station           0  0.000000\n",
       "year              0  0.000000\n",
       "month             0  0.000000\n",
       "WSPM            238  0.058944\n",
       "RAIN            261  0.064640\n",
       "TEMP            264  0.065383\n",
       "PRES            265  0.065630\n",
       "DEWP            269  0.066621\n",
       "wd             1389  0.344003\n",
       "PM10           6222  1.540953\n",
       "PM2.5          8475  2.098936\n",
       "SO2            8776  2.173482\n",
       "NO2           11859  2.937024\n",
       "O3            13007  3.221341\n",
       "CO            20261  5.017881"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null profiling: COLUMWISE: TRAIN DATA\n",
    "\n",
    "null = pd.concat([df_train.isnull().sum(),df_train.isnull().mean()*100],axis=1)\n",
    "null.columns = ['Null count','NUll%']\n",
    "null.sort_values(by = 'Null count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows with specified no.of nulls in it: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{1: 26583,\n",
       " 2: 4524,\n",
       " 3: 429,\n",
       " 4: 814,\n",
       " 5: 238,\n",
       " 6: 4959,\n",
       " 7: 24,\n",
       " 8: 0,\n",
       " 9: 0,\n",
       " 10: 0,\n",
       " 11: 0,\n",
       " 12: 0,\n",
       " 13: 0,\n",
       " 14: 0,\n",
       " 15: 0,\n",
       " 16: 0}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Row wise null profiling:\n",
    "count = {}\n",
    "for i in range(1,17):\n",
    "    \n",
    "    count[i] = len(df_train[df_train.isnull().sum(axis=1) == i].index)\n",
    "\n",
    "print(\"No of rows with specified no.of nulls in it: \")\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Null count</th>\n",
       "      <th>NUll%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REF_NO</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMP</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRES</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEWP</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAIN</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WSPM</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Null count  NUll%\n",
       "REF_NO            0    0.0\n",
       "year              0    0.0\n",
       "month             0    0.0\n",
       "day               0    0.0\n",
       "hour              0    0.0\n",
       "PM10              0    0.0\n",
       "SO2               0    0.0\n",
       "NO2               0    0.0\n",
       "CO                0    0.0\n",
       "O3                0    0.0\n",
       "TEMP              0    0.0\n",
       "PRES              0    0.0\n",
       "DEWP              0    0.0\n",
       "RAIN              0    0.0\n",
       "wd                0    0.0\n",
       "WSPM              0    0.0\n",
       "station           0    0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Null profiling the test dataset:\n",
    "null_test = pd.concat([df_test.isnull().sum(),df_test.isnull().mean()*100],axis=1)\n",
    "null_test.columns = ['Null count','NUll%']\n",
    "null_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing nulls with corresponding median values:\n",
    "\n",
    "for i in df_train_num.columns:\n",
    "    df_train[i].fillna(df_train[i].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['wd'].fillna(df_train['wd'].mode()[0],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REF_NO     0\n",
       "year       0\n",
       "month      0\n",
       "day        0\n",
       "hour       0\n",
       "PM2.5      0\n",
       "PM10       0\n",
       "SO2        0\n",
       "NO2        0\n",
       "CO         0\n",
       "O3         0\n",
       "TEMP       0\n",
       "PRES       0\n",
       "DEWP       0\n",
       "RAIN       0\n",
       "wd         0\n",
       "WSPM       0\n",
       "station    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REF_NO     0\n",
       "year       0\n",
       "month      0\n",
       "day        0\n",
       "hour       0\n",
       "PM10       0\n",
       "SO2        0\n",
       "NO2        0\n",
       "CO         0\n",
       "O3         0\n",
       "TEMP       0\n",
       "PRES       0\n",
       "DEWP       0\n",
       "RAIN       0\n",
       "wd         0\n",
       "WSPM       0\n",
       "station    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Dataset has no null values in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REF_NO</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>WSPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   REF_NO  PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  DEWP  RAIN  \\\n",
       "0       1    4.0   4.0   4.0   7.0  300.0  77.0  -0.7  1023.0 -18.8   0.0   \n",
       "1       2    8.0   8.0   4.0   7.0  300.0  77.0  -1.1  1023.2 -18.2   0.0   \n",
       "2       3    7.0   7.0   5.0  10.0  300.0  73.0  -1.1  1023.5 -18.2   0.0   \n",
       "3       4    6.0   6.0  11.0  11.0  300.0  72.0  -1.4  1024.5 -19.4   0.0   \n",
       "4       5    3.0   3.0  12.0  12.0  300.0  72.0  -2.0  1025.2 -19.5   0.0   \n",
       "\n",
       "   WSPM  \n",
       "0   4.4  \n",
       "1   4.7  \n",
       "2   5.6  \n",
       "3   3.1  \n",
       "4   2.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_num = df_train.select_dtypes(include = np.number)\n",
    "df_train_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>wd</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NNW</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NW</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year month day hour   wd       station\n",
       "0  2013     3   1    0  NNW  Aotizhongxin\n",
       "1  2013     3   1    1    N  Aotizhongxin\n",
       "2  2013     3   1    2  NNW  Aotizhongxin\n",
       "3  2013     3   1    3   NW  Aotizhongxin\n",
       "4  2013     3   1    4    N  Aotizhongxin"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_cat = df_train.select_dtypes(include = np.object)\n",
    "df_train_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REF_NO</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   REF_NO  year month day hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "0       1  2013     3   1    0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7   \n",
       "1       2  2013     3   1    1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1   \n",
       "2       3  2013     3   1    2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1   \n",
       "3       4  2013     3   1    3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4       5  2013     3   1    4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "0  1023.0 -18.8   0.0  NNW   4.4  Aotizhongxin  \n",
       "1  1023.2 -18.2   0.0    N   4.7  Aotizhongxin  \n",
       "2  1023.5 -18.2   0.0  NNW   5.6  Aotizhongxin  \n",
       "3  1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4  1025.2 -19.5   0.0    N   2.0  Aotizhongxin  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
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       "      <td>Aotizhongxin</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>2.0</td>\n",
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      ],
      "text/plain": [
       "   REF_NO  year month day hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "0       1  2013     3   1    0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7   \n",
       "1       2  2013     3   1    1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1   \n",
       "2       3  2013     3   1    2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1   \n",
       "3       4  2013     3   1    3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4       5  2013     3   1    4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "0  1023.0 -18.8   0.0  NNW   4.4  Aotizhongxin  \n",
       "1  1023.2 -18.2   0.0    N   4.7  Aotizhongxin  \n",
       "2  1023.5 -18.2   0.0  NNW   5.6  Aotizhongxin  \n",
       "3  1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4  1025.2 -19.5   0.0    N   2.0  Aotizhongxin  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]),\n",
       " [Text(1, 0, 'REF_NO'),\n",
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       "  Text(12, 0, 'WSPM')])"
      ]
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     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### OUTLIERS CHECK:\n",
    "plt.figure(figsize = (15,8))\n",
    "df_train_num.boxplot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows in outliers:  110056\n"
     ]
    }
   ],
   "source": [
    "q1 = df_train.quantile(0.25)\n",
    "\n",
    "q3 = df_train.quantile(0.75)\n",
    "\n",
    "iqr = q3-q1\n",
    "\n",
    "upper = q3 + (1.5*iqr)\n",
    "lower = q1 - (1.5*iqr)\n",
    "\n",
    "df1 = df_train[((df_train > upper) | (df_train < lower)).any(axis=1)]\n",
    "\n",
    "print(\"No of rows in outliers: \",df1.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(293720, 18)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 1,08,366 rows belinging to outlier category.\n",
    "#So we remove the outliers.\n",
    "df_train = df_train[~((df_train > upper) | (df_train < lower)).any(axis=1)]\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
       " [Text(1, 0, 'REF_NO'),\n",
       "  Text(2, 0, 'PM10'),\n",
       "  Text(3, 0, 'SO2'),\n",
       "  Text(4, 0, 'NO2'),\n",
       "  Text(5, 0, 'CO'),\n",
       "  Text(6, 0, 'O3'),\n",
       "  Text(7, 0, 'TEMP'),\n",
       "  Text(8, 0, 'PRES'),\n",
       "  Text(9, 0, 'DEWP'),\n",
       "  Text(10, 0, 'RAIN'),\n",
       "  Text(11, 0, 'WSPM')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### OUTLIERS IN TEST DATASET:\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "df_test.boxplot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows in outliers:  3601\n"
     ]
    }
   ],
   "source": [
    "#Checking no. of rows in outliers category:\n",
    "\n",
    "q1 = df_test.quantile(0.25)\n",
    "\n",
    "q3 = df_test.quantile(0.75)\n",
    "\n",
    "iqr = q3-q1\n",
    "\n",
    "upper = q3 + (1.5*iqr)\n",
    "lower = q1 - (1.5*iqr)\n",
    "\n",
    "df1 = df_test[((df_test > upper) | (df_test < lower)).any(axis=1)]\n",
    "\n",
    "\n",
    "print(\"No of rows in outliers: \",df1.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13127, 17)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 3601 rows belonging to outlier category.So we remove the outliers.\n",
    "\n",
    "df_test = df_test[~((df_test > upper) | (df_test < lower)).any(axis=1)]\n",
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>wd</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NW</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>N</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>N</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>NNE</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>NNW</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year month day hour   wd       station\n",
       "3  2013     3   1    3   NW  Aotizhongxin\n",
       "4  2013     3   1    4    N  Aotizhongxin\n",
       "5  2013     3   1    5    N  Aotizhongxin\n",
       "6  2013     3   1    6  NNE  Aotizhongxin\n",
       "7  2013     3   1    7  NNW  Aotizhongxin"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Encoding cateogorical variables:\n",
    "\n",
    "df1_cat = df_train.select_dtypes(include = np.object)\n",
    "df1_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year [2013 2014 2015 2016]\n",
      "month [3 4 5 6 7 8 9 10 11 12 1 2]\n",
      "day [1 2 3 4 8 9 10 11 12 13 14 15 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n",
      " 31 5 6 7 16]\n",
      "hour [3 4 5 6 7 8 9 10 11 15 16 17 18 19 20 21 22 23 0 1 2 12 13 14]\n",
      "wd ['NW' 'N' 'NNE' 'NNW' 'ENE' 'E' 'NE' 'W' 'SSW' 'WSW' 'SE' 'WNW' 'SSE' 'S'\n",
      " 'SW' 'ESE']\n",
      "station ['Aotizhongxin' 'Changping' 'Dingling' 'Dongsi' 'Guanyuan' 'Gucheng'\n",
      " 'Huairou' 'Nongzhanguan' 'Shunyi' 'Tiantan' 'Wanliu' 'Wanshouxigong']\n"
     ]
    }
   ],
   "source": [
    "for i in df1_cat.columns:\n",
    "    print(i,df1_cat[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year_2014</th>\n",
       "      <th>year_2015</th>\n",
       "      <th>year_2016</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</th>\n",
       "      <th>month_4</th>\n",
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       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
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       "      <th>day_6</th>\n",
       "      <th>day_7</th>\n",
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       "      <th>day_21</th>\n",
       "      <th>day_22</th>\n",
       "      <th>day_23</th>\n",
       "      <th>day_24</th>\n",
       "      <th>day_25</th>\n",
       "      <th>day_26</th>\n",
       "      <th>day_27</th>\n",
       "      <th>day_28</th>\n",
       "      <th>day_29</th>\n",
       "      <th>day_30</th>\n",
       "      <th>day_31</th>\n",
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       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
       "      <th>hour_7</th>\n",
       "      <th>hour_8</th>\n",
       "      <th>hour_9</th>\n",
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       "      <th>hour_11</th>\n",
       "      <th>hour_12</th>\n",
       "      <th>hour_13</th>\n",
       "      <th>hour_14</th>\n",
       "      <th>hour_15</th>\n",
       "      <th>hour_16</th>\n",
       "      <th>hour_17</th>\n",
       "      <th>hour_18</th>\n",
       "      <th>hour_19</th>\n",
       "      <th>hour_20</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>wd_ENE</th>\n",
       "      <th>wd_ESE</th>\n",
       "      <th>wd_N</th>\n",
       "      <th>wd_NE</th>\n",
       "      <th>wd_NNE</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "      <th>station_Changping</th>\n",
       "      <th>station_Dingling</th>\n",
       "      <th>station_Dongsi</th>\n",
       "      <th>station_Guanyuan</th>\n",
       "      <th>station_Gucheng</th>\n",
       "      <th>station_Huairou</th>\n",
       "      <th>station_Nongzhanguan</th>\n",
       "      <th>station_Shunyi</th>\n",
       "      <th>station_Tiantan</th>\n",
       "      <th>station_Wanliu</th>\n",
       "      <th>station_Wanshouxigong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_2014  year_2015  year_2016  month_2  month_3  month_4  month_5  \\\n",
       "3          0          0          0        0        1        0        0   \n",
       "4          0          0          0        0        1        0        0   \n",
       "5          0          0          0        0        1        0        0   \n",
       "6          0          0          0        0        1        0        0   \n",
       "7          0          0          0        0        1        0        0   \n",
       "\n",
       "   month_6  month_7  month_8  month_9  month_10  month_11  month_12  day_2  \\\n",
       "3        0        0        0        0         0         0         0      0   \n",
       "4        0        0        0        0         0         0         0      0   \n",
       "5        0        0        0        0         0         0         0      0   \n",
       "6        0        0        0        0         0         0         0      0   \n",
       "7        0        0        0        0         0         0         0      0   \n",
       "\n",
       "   day_3  day_4  day_5  day_6  day_7  day_8  day_9  day_10  day_11  day_12  \\\n",
       "3      0      0      0      0      0      0      0       0       0       0   \n",
       "4      0      0      0      0      0      0      0       0       0       0   \n",
       "5      0      0      0      0      0      0      0       0       0       0   \n",
       "6      0      0      0      0      0      0      0       0       0       0   \n",
       "7      0      0      0      0      0      0      0       0       0       0   \n",
       "\n",
       "   day_13  day_14  day_15  day_16  day_17  day_18  day_19  day_20  day_21  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_22  day_23  day_24  day_25  day_26  day_27  day_28  day_29  day_30  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_31  hour_1  hour_2  hour_3  hour_4  hour_5  hour_6  hour_7  hour_8  \\\n",
       "3       0       0       0       1       0       0       0       0       0   \n",
       "4       0       0       0       0       1       0       0       0       0   \n",
       "5       0       0       0       0       0       1       0       0       0   \n",
       "6       0       0       0       0       0       0       1       0       0   \n",
       "7       0       0       0       0       0       0       0       1       0   \n",
       "\n",
       "   hour_9  hour_10  hour_11  hour_12  hour_13  hour_14  hour_15  hour_16  \\\n",
       "3       0        0        0        0        0        0        0        0   \n",
       "4       0        0        0        0        0        0        0        0   \n",
       "5       0        0        0        0        0        0        0        0   \n",
       "6       0        0        0        0        0        0        0        0   \n",
       "7       0        0        0        0        0        0        0        0   \n",
       "\n",
       "   hour_17  hour_18  hour_19  hour_20  hour_21  hour_22  hour_23  wd_ENE  \\\n",
       "3        0        0        0        0        0        0        0       0   \n",
       "4        0        0        0        0        0        0        0       0   \n",
       "5        0        0        0        0        0        0        0       0   \n",
       "6        0        0        0        0        0        0        0       0   \n",
       "7        0        0        0        0        0        0        0       0   \n",
       "\n",
       "   wd_ESE  wd_N  wd_NE  wd_NNE  wd_NNW  wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW  \\\n",
       "3       0     0      0       0       0      1     0      0       0       0   \n",
       "4       0     1      0       0       0      0     0      0       0       0   \n",
       "5       0     1      0       0       0      0     0      0       0       0   \n",
       "6       0     0      0       1       0      0     0      0       0       0   \n",
       "7       0     0      0       0       1      0     0      0       0       0   \n",
       "\n",
       "   wd_SW  wd_W  wd_WNW  wd_WSW  station_Changping  station_Dingling  \\\n",
       "3      0     0       0       0                  0                 0   \n",
       "4      0     0       0       0                  0                 0   \n",
       "5      0     0       0       0                  0                 0   \n",
       "6      0     0       0       0                  0                 0   \n",
       "7      0     0       0       0                  0                 0   \n",
       "\n",
       "   station_Dongsi  station_Guanyuan  station_Gucheng  station_Huairou  \\\n",
       "3               0                 0                0                0   \n",
       "4               0                 0                0                0   \n",
       "5               0                 0                0                0   \n",
       "6               0                 0                0                0   \n",
       "7               0                 0                0                0   \n",
       "\n",
       "   station_Nongzhanguan  station_Shunyi  station_Tiantan  station_Wanliu  \\\n",
       "3                     0               0                0               0   \n",
       "4                     0               0                0               0   \n",
       "5                     0               0                0               0   \n",
       "6                     0               0                0               0   \n",
       "7                     0               0                0               0   \n",
       "\n",
       "   station_Wanshouxigong  \n",
       "3                      0  \n",
       "4                      0  \n",
       "5                      0  \n",
       "6                      0  \n",
       "7                      0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_cat_encode = pd.get_dummies(df1_cat,drop_first= True)\n",
    "\n",
    "df1_cat_encode.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>REF_NO</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
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       "      <th>4</th>\n",
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       "      <td>4</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
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       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>1025.6</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>3.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>1026.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
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       "      <th>7</th>\n",
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      ],
      "text/plain": [
       "   REF_NO  year month day hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "3       4  2013     3   1    3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4       5  2013     3   1    4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "5       6  2013     3   1    5    5.0   5.0  18.0  18.0  400.0  66.0  -2.2   \n",
       "6       7  2013     3   1    6    3.0   3.0  18.0  32.0  500.0  50.0  -2.6   \n",
       "7       8  2013     3   1    7    3.0   6.0  19.0  41.0  500.0  43.0  -1.6   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "3  1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4  1025.2 -19.5   0.0    N   2.0  Aotizhongxin  \n",
       "5  1025.6 -19.6   0.0    N   3.7  Aotizhongxin  \n",
       "6  1026.5 -19.1   0.0  NNE   2.5  Aotizhongxin  \n",
       "7  1027.4 -19.1   0.0  NNW   3.8  Aotizhongxin  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
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       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
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      ],
      "text/plain": [
       "   PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  DEWP  RAIN\n",
       "3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4  1024.5 -19.4   0.0\n",
       "4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0  1025.2 -19.5   0.0\n",
       "5    5.0   5.0  18.0  18.0  400.0  66.0  -2.2  1025.6 -19.6   0.0\n",
       "6    3.0   3.0  18.0  32.0  500.0  50.0  -2.6  1026.5 -19.1   0.0\n",
       "7    3.0   6.0  19.0  41.0  500.0  43.0  -1.6  1027.4 -19.1   0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_train.iloc[:,[5,6,7,8,9,10,11,12,13,14]]\n",
    "\n",
    "col = X.columns\n",
    "\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X,df1_cat_encode],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>day_5</th>\n",
       "      <th>day_6</th>\n",
       "      <th>day_7</th>\n",
       "      <th>day_8</th>\n",
       "      <th>day_9</th>\n",
       "      <th>day_10</th>\n",
       "      <th>day_11</th>\n",
       "      <th>day_12</th>\n",
       "      <th>day_13</th>\n",
       "      <th>day_14</th>\n",
       "      <th>day_15</th>\n",
       "      <th>day_16</th>\n",
       "      <th>day_17</th>\n",
       "      <th>day_18</th>\n",
       "      <th>day_19</th>\n",
       "      <th>day_20</th>\n",
       "      <th>day_21</th>\n",
       "      <th>day_22</th>\n",
       "      <th>day_23</th>\n",
       "      <th>day_24</th>\n",
       "      <th>day_25</th>\n",
       "      <th>day_26</th>\n",
       "      <th>day_27</th>\n",
       "      <th>day_28</th>\n",
       "      <th>day_29</th>\n",
       "      <th>day_30</th>\n",
       "      <th>day_31</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
       "      <th>hour_7</th>\n",
       "      <th>hour_8</th>\n",
       "      <th>hour_9</th>\n",
       "      <th>hour_10</th>\n",
       "      <th>hour_11</th>\n",
       "      <th>hour_12</th>\n",
       "      <th>hour_13</th>\n",
       "      <th>hour_14</th>\n",
       "      <th>hour_15</th>\n",
       "      <th>hour_16</th>\n",
       "      <th>hour_17</th>\n",
       "      <th>hour_18</th>\n",
       "      <th>hour_19</th>\n",
       "      <th>hour_20</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>wd_ENE</th>\n",
       "      <th>wd_ESE</th>\n",
       "      <th>wd_N</th>\n",
       "      <th>wd_NE</th>\n",
       "      <th>wd_NNE</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "      <th>station_Changping</th>\n",
       "      <th>station_Dingling</th>\n",
       "      <th>station_Dongsi</th>\n",
       "      <th>station_Guanyuan</th>\n",
       "      <th>station_Gucheng</th>\n",
       "      <th>station_Huairou</th>\n",
       "      <th>station_Nongzhanguan</th>\n",
       "      <th>station_Shunyi</th>\n",
       "      <th>station_Tiantan</th>\n",
       "      <th>station_Wanliu</th>\n",
       "      <th>station_Wanshouxigong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>1026.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  DEWP  RAIN  year_2014  \\\n",
       "3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4  1024.5 -19.4   0.0          0   \n",
       "4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0  1025.2 -19.5   0.0          0   \n",
       "5    5.0   5.0  18.0  18.0  400.0  66.0  -2.2  1025.6 -19.6   0.0          0   \n",
       "6    3.0   3.0  18.0  32.0  500.0  50.0  -2.6  1026.5 -19.1   0.0          0   \n",
       "7    3.0   6.0  19.0  41.0  500.0  43.0  -1.6  1027.4 -19.1   0.0          0   \n",
       "\n",
       "   year_2015  year_2016  month_2  month_3  month_4  month_5  month_6  month_7  \\\n",
       "3          0          0        0        1        0        0        0        0   \n",
       "4          0          0        0        1        0        0        0        0   \n",
       "5          0          0        0        1        0        0        0        0   \n",
       "6          0          0        0        1        0        0        0        0   \n",
       "7          0          0        0        1        0        0        0        0   \n",
       "\n",
       "   month_8  month_9  month_10  month_11  month_12  day_2  day_3  day_4  day_5  \\\n",
       "3        0        0         0         0         0      0      0      0      0   \n",
       "4        0        0         0         0         0      0      0      0      0   \n",
       "5        0        0         0         0         0      0      0      0      0   \n",
       "6        0        0         0         0         0      0      0      0      0   \n",
       "7        0        0         0         0         0      0      0      0      0   \n",
       "\n",
       "   day_6  day_7  day_8  day_9  day_10  day_11  day_12  day_13  day_14  day_15  \\\n",
       "3      0      0      0      0       0       0       0       0       0       0   \n",
       "4      0      0      0      0       0       0       0       0       0       0   \n",
       "5      0      0      0      0       0       0       0       0       0       0   \n",
       "6      0      0      0      0       0       0       0       0       0       0   \n",
       "7      0      0      0      0       0       0       0       0       0       0   \n",
       "\n",
       "   day_16  day_17  day_18  day_19  day_20  day_21  day_22  day_23  day_24  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_25  day_26  day_27  day_28  day_29  day_30  day_31  hour_1  hour_2  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   hour_3  hour_4  hour_5  hour_6  hour_7  hour_8  hour_9  hour_10  hour_11  \\\n",
       "3       1       0       0       0       0       0       0        0        0   \n",
       "4       0       1       0       0       0       0       0        0        0   \n",
       "5       0       0       1       0       0       0       0        0        0   \n",
       "6       0       0       0       1       0       0       0        0        0   \n",
       "7       0       0       0       0       1       0       0        0        0   \n",
       "\n",
       "   hour_12  hour_13  hour_14  hour_15  hour_16  hour_17  hour_18  hour_19  \\\n",
       "3        0        0        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "6        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   hour_20  hour_21  hour_22  hour_23  wd_ENE  wd_ESE  wd_N  wd_NE  wd_NNE  \\\n",
       "3        0        0        0        0       0       0     0      0       0   \n",
       "4        0        0        0        0       0       0     1      0       0   \n",
       "5        0        0        0        0       0       0     1      0       0   \n",
       "6        0        0        0        0       0       0     0      0       1   \n",
       "7        0        0        0        0       0       0     0      0       0   \n",
       "\n",
       "   wd_NNW  wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW  wd_SW  wd_W  wd_WNW  wd_WSW  \\\n",
       "3       0      1     0      0       0       0      0     0       0       0   \n",
       "4       0      0     0      0       0       0      0     0       0       0   \n",
       "5       0      0     0      0       0       0      0     0       0       0   \n",
       "6       0      0     0      0       0       0      0     0       0       0   \n",
       "7       1      0     0      0       0       0      0     0       0       0   \n",
       "\n",
       "   station_Changping  station_Dingling  station_Dongsi  station_Guanyuan  \\\n",
       "3                  0                 0               0                 0   \n",
       "4                  0                 0               0                 0   \n",
       "5                  0                 0               0                 0   \n",
       "6                  0                 0               0                 0   \n",
       "7                  0                 0               0                 0   \n",
       "\n",
       "   station_Gucheng  station_Huairou  station_Nongzhanguan  station_Shunyi  \\\n",
       "3                0                0                     0               0   \n",
       "4                0                0                     0               0   \n",
       "5                0                0                     0               0   \n",
       "6                0                0                     0               0   \n",
       "7                0                0                     0               0   \n",
       "\n",
       "   station_Tiantan  station_Wanliu  station_Wanshouxigong  \n",
       "3                0               0                      0  \n",
       "4                0               0                      0  \n",
       "5                0               0                      0  \n",
       "6                0               0                      0  \n",
       "7                0               0                      0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    6.0\n",
       "4    3.0\n",
       "5    5.0\n",
       "6    3.0\n",
       "7    3.0\n",
       "Name: PM2.5, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = X['PM2.5']\n",
    "\n",
    "X.drop('PM2.5',axis=1,inplace=True)\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### BASE MODEL:\n",
    "\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205604, 103), (88116, 103), (205604,), (88116,))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X)\n",
    "\n",
    "### train_test_split:\n",
    "\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3,random_state = 111)\n",
    "\n",
    "xtrain.shape,xtest.shape,ytrain.shape,ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>PM2.5</td>      <th>  R-squared:         </th>  <td>   0.814</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.814</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   8891.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Nov 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:57:42</td>     <th>  Log-Likelihood:    </th> <td>-9.2537e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>205604</td>      <th>  AIC:               </th>  <td>1.851e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>205502</td>      <th>  BIC:               </th>  <td>1.852e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   101</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                 <td> -294.1469</td> <td>   11.031</td> <td>  -26.666</td> <td> 0.000</td> <td> -315.767</td> <td> -272.527</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PM10</th>                  <td>    0.5407</td> <td>    0.001</td> <td>  424.172</td> <td> 0.000</td> <td>    0.538</td> <td>    0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO2</th>                   <td>   -0.0448</td> <td>    0.007</td> <td>   -6.488</td> <td> 0.000</td> <td>   -0.058</td> <td>   -0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NO2</th>                   <td>   -0.0662</td> <td>    0.003</td> <td>  -21.649</td> <td> 0.000</td> <td>   -0.072</td> <td>   -0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CO</th>                    <td>    0.0243</td> <td>    0.000</td> <td>  176.311</td> <td> 0.000</td> <td>    0.024</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>O3</th>                    <td>    0.0724</td> <td>    0.002</td> <td>   39.429</td> <td> 0.000</td> <td>    0.069</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TEMP</th>                  <td>   -0.6326</td> <td>    0.016</td> <td>  -39.505</td> <td> 0.000</td> <td>   -0.664</td> <td>   -0.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PRES</th>                  <td>    0.3040</td> <td>    0.011</td> <td>   28.086</td> <td> 0.000</td> <td>    0.283</td> <td>    0.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DEWP</th>                  <td>    1.5922</td> <td>    0.011</td> <td>  144.414</td> <td> 0.000</td> <td>    1.571</td> <td>    1.614</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RAIN</th>                  <td> 3.771e-13</td> <td> 1.13e-14</td> <td>   33.439</td> <td> 0.000</td> <td> 3.55e-13</td> <td> 3.99e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year_2014</th>             <td>   -3.1621</td> <td>    0.146</td> <td>  -21.730</td> <td> 0.000</td> <td>   -3.447</td> <td>   -2.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year_2015</th>             <td>   -2.6958</td> <td>    0.148</td> <td>  -18.224</td> <td> 0.000</td> <td>   -2.986</td> <td>   -2.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year_2016</th>             <td>   -2.1743</td> <td>    0.148</td> <td>  -14.694</td> <td> 0.000</td> <td>   -2.464</td> <td>   -1.884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_2</th>               <td>    3.0117</td> <td>    0.305</td> <td>    9.866</td> <td> 0.000</td> <td>    2.413</td> <td>    3.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_3</th>               <td>   -5.2643</td> <td>    0.308</td> <td>  -17.068</td> <td> 0.000</td> <td>   -5.869</td> <td>   -4.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_4</th>               <td>  -10.0767</td> <td>    0.365</td> <td>  -27.615</td> <td> 0.000</td> <td>  -10.792</td> <td>   -9.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_5</th>               <td>  -21.1375</td> <td>    0.430</td> <td>  -49.187</td> <td> 0.000</td> <td>  -21.980</td> <td>  -20.295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_6</th>               <td>  -21.2474</td> <td>    0.495</td> <td>  -42.919</td> <td> 0.000</td> <td>  -22.218</td> <td>  -20.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_7</th>               <td>  -21.0889</td> <td>    0.533</td> <td>  -39.539</td> <td> 0.000</td> <td>  -22.134</td> <td>  -20.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_8</th>               <td>  -29.2819</td> <td>    0.527</td> <td>  -55.610</td> <td> 0.000</td> <td>  -30.314</td> <td>  -28.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_9</th>               <td>  -24.4659</td> <td>    0.463</td> <td>  -52.807</td> <td> 0.000</td> <td>  -25.374</td> <td>  -23.558</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_10</th>              <td>  -14.1036</td> <td>    0.381</td> <td>  -37.055</td> <td> 0.000</td> <td>  -14.850</td> <td>  -13.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_11</th>              <td>  -10.1322</td> <td>    0.308</td> <td>  -32.936</td> <td> 0.000</td> <td>  -10.735</td> <td>   -9.529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_12</th>              <td>   -4.5190</td> <td>    0.283</td> <td>  -15.946</td> <td> 0.000</td> <td>   -5.074</td> <td>   -3.964</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_2</th>                 <td>   -2.4032</td> <td>    0.376</td> <td>   -6.395</td> <td> 0.000</td> <td>   -3.140</td> <td>   -1.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_3</th>                 <td>   -4.1603</td> <td>    0.378</td> <td>  -11.015</td> <td> 0.000</td> <td>   -4.901</td> <td>   -3.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_4</th>                 <td>   -2.7616</td> <td>    0.379</td> <td>   -7.284</td> <td> 0.000</td> <td>   -3.505</td> <td>   -2.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_5</th>                 <td>   -2.5668</td> <td>    0.380</td> <td>   -6.750</td> <td> 0.000</td> <td>   -3.312</td> <td>   -1.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_6</th>                 <td>   -1.7490</td> <td>    0.379</td> <td>   -4.620</td> <td> 0.000</td> <td>   -2.491</td> <td>   -1.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_7</th>                 <td>   -2.3684</td> <td>    0.379</td> <td>   -6.246</td> <td> 0.000</td> <td>   -3.112</td> <td>   -1.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_8</th>                 <td>   -1.9168</td> <td>    0.376</td> <td>   -5.096</td> <td> 0.000</td> <td>   -2.654</td> <td>   -1.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_9</th>                 <td>   -3.0335</td> <td>    0.373</td> <td>   -8.133</td> <td> 0.000</td> <td>   -3.765</td> <td>   -2.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_10</th>                <td>   -4.8653</td> <td>    0.374</td> <td>  -13.005</td> <td> 0.000</td> <td>   -5.599</td> <td>   -4.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_11</th>                <td>   -3.9317</td> <td>    0.369</td> <td>  -10.659</td> <td> 0.000</td> <td>   -4.655</td> <td>   -3.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_12</th>                <td>   -2.6813</td> <td>    0.367</td> <td>   -7.313</td> <td> 0.000</td> <td>   -3.400</td> <td>   -1.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_13</th>                <td>   -3.0169</td> <td>    0.374</td> <td>   -8.067</td> <td> 0.000</td> <td>   -3.750</td> <td>   -2.284</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_14</th>                <td>   -4.7137</td> <td>    0.377</td> <td>  -12.496</td> <td> 0.000</td> <td>   -5.453</td> <td>   -3.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_15</th>                <td>   -8.0030</td> <td>    0.385</td> <td>  -20.803</td> <td> 0.000</td> <td>   -8.757</td> <td>   -7.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_16</th>                <td>   -5.1750</td> <td>    0.386</td> <td>  -13.399</td> <td> 0.000</td> <td>   -5.932</td> <td>   -4.418</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_17</th>                <td>   -6.4995</td> <td>    0.374</td> <td>  -17.376</td> <td> 0.000</td> <td>   -7.233</td> <td>   -5.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_18</th>                <td>   -2.3126</td> <td>    0.374</td> <td>   -6.189</td> <td> 0.000</td> <td>   -3.045</td> <td>   -1.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_19</th>                <td>   -4.5139</td> <td>    0.374</td> <td>  -12.057</td> <td> 0.000</td> <td>   -5.248</td> <td>   -3.780</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_20</th>                <td>   -4.8638</td> <td>    0.377</td> <td>  -12.901</td> <td> 0.000</td> <td>   -5.603</td> <td>   -4.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_21</th>                <td>   -3.3974</td> <td>    0.374</td> <td>   -9.096</td> <td> 0.000</td> <td>   -4.130</td> <td>   -2.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_22</th>                <td>   -6.5880</td> <td>    0.378</td> <td>  -17.439</td> <td> 0.000</td> <td>   -7.328</td> <td>   -5.848</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_23</th>                <td>   -5.1564</td> <td>    0.375</td> <td>  -13.763</td> <td> 0.000</td> <td>   -5.891</td> <td>   -4.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_24</th>                <td>   -5.6835</td> <td>    0.368</td> <td>  -15.439</td> <td> 0.000</td> <td>   -6.405</td> <td>   -4.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_25</th>                <td>   -2.5352</td> <td>    0.372</td> <td>   -6.816</td> <td> 0.000</td> <td>   -3.264</td> <td>   -1.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_26</th>                <td>   -4.8016</td> <td>    0.378</td> <td>  -12.719</td> <td> 0.000</td> <td>   -5.541</td> <td>   -4.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_27</th>                <td>   -4.8829</td> <td>    0.371</td> <td>  -13.158</td> <td> 0.000</td> <td>   -5.610</td> <td>   -4.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_28</th>                <td>   -4.5617</td> <td>    0.372</td> <td>  -12.270</td> <td> 0.000</td> <td>   -5.290</td> <td>   -3.833</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_29</th>                <td>   -4.1779</td> <td>    0.376</td> <td>  -11.112</td> <td> 0.000</td> <td>   -4.915</td> <td>   -3.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_30</th>                <td>   -3.2041</td> <td>    0.381</td> <td>   -8.411</td> <td> 0.000</td> <td>   -3.951</td> <td>   -2.457</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_31</th>                <td>   -3.3531</td> <td>    0.439</td> <td>   -7.635</td> <td> 0.000</td> <td>   -4.214</td> <td>   -2.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_1</th>                <td>    0.0446</td> <td>    0.322</td> <td>    0.138</td> <td> 0.890</td> <td>   -0.587</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_2</th>                <td>    1.9623</td> <td>    0.322</td> <td>    6.089</td> <td> 0.000</td> <td>    1.331</td> <td>    2.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_3</th>                <td>    3.1066</td> <td>    0.322</td> <td>    9.635</td> <td> 0.000</td> <td>    2.475</td> <td>    3.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_4</th>                <td>    4.4556</td> <td>    0.323</td> <td>   13.796</td> <td> 0.000</td> <td>    3.823</td> <td>    5.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_5</th>                <td>    3.7400</td> <td>    0.323</td> <td>   11.577</td> <td> 0.000</td> <td>    3.107</td> <td>    4.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_6</th>                <td>    2.3905</td> <td>    0.322</td> <td>    7.429</td> <td> 0.000</td> <td>    1.760</td> <td>    3.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_7</th>                <td>    1.1684</td> <td>    0.322</td> <td>    3.630</td> <td> 0.000</td> <td>    0.537</td> <td>    1.799</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_8</th>                <td>    0.0690</td> <td>    0.325</td> <td>    0.212</td> <td> 0.832</td> <td>   -0.568</td> <td>    0.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_9</th>                <td>    1.7709</td> <td>    0.331</td> <td>    5.348</td> <td> 0.000</td> <td>    1.122</td> <td>    2.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_10</th>               <td>    4.2083</td> <td>    0.336</td> <td>   12.506</td> <td> 0.000</td> <td>    3.549</td> <td>    4.868</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_11</th>               <td>    6.5985</td> <td>    0.341</td> <td>   19.365</td> <td> 0.000</td> <td>    5.931</td> <td>    7.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_12</th>               <td>    7.8970</td> <td>    0.348</td> <td>   22.669</td> <td> 0.000</td> <td>    7.214</td> <td>    8.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_13</th>               <td>    9.2828</td> <td>    0.357</td> <td>   26.030</td> <td> 0.000</td> <td>    8.584</td> <td>    9.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_14</th>               <td>    9.4909</td> <td>    0.363</td> <td>   26.115</td> <td> 0.000</td> <td>    8.779</td> <td>   10.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_15</th>               <td>    8.2707</td> <td>    0.365</td> <td>   22.632</td> <td> 0.000</td> <td>    7.554</td> <td>    8.987</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_16</th>               <td>    6.1002</td> <td>    0.362</td> <td>   16.850</td> <td> 0.000</td> <td>    5.391</td> <td>    6.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_17</th>               <td>    3.3960</td> <td>    0.355</td> <td>    9.558</td> <td> 0.000</td> <td>    2.700</td> <td>    4.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_18</th>               <td>    0.3248</td> <td>    0.347</td> <td>    0.937</td> <td> 0.349</td> <td>   -0.354</td> <td>    1.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_19</th>               <td>   -2.2969</td> <td>    0.338</td> <td>   -6.795</td> <td> 0.000</td> <td>   -2.959</td> <td>   -1.634</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_20</th>               <td>   -3.2837</td> <td>    0.332</td> <td>   -9.877</td> <td> 0.000</td> <td>   -3.935</td> <td>   -2.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_21</th>               <td>   -3.1780</td> <td>    0.328</td> <td>   -9.682</td> <td> 0.000</td> <td>   -3.821</td> <td>   -2.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_22</th>               <td>   -2.1856</td> <td>    0.326</td> <td>   -6.708</td> <td> 0.000</td> <td>   -2.824</td> <td>   -1.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_23</th>               <td>   -1.1980</td> <td>    0.324</td> <td>   -3.695</td> <td> 0.000</td> <td>   -1.833</td> <td>   -0.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_ENE</th>                <td>   -0.0153</td> <td>    0.247</td> <td>   -0.062</td> <td> 0.951</td> <td>   -0.499</td> <td>    0.468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_ESE</th>                <td>    0.4168</td> <td>    0.269</td> <td>    1.548</td> <td> 0.122</td> <td>   -0.111</td> <td>    0.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_N</th>                  <td>    1.2486</td> <td>    0.252</td> <td>    4.961</td> <td> 0.000</td> <td>    0.755</td> <td>    1.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NE</th>                 <td>    0.6091</td> <td>    0.235</td> <td>    2.595</td> <td> 0.009</td> <td>    0.149</td> <td>    1.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NNE</th>                <td>    0.6610</td> <td>    0.257</td> <td>    2.576</td> <td> 0.010</td> <td>    0.158</td> <td>    1.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NNW</th>                <td>    1.8569</td> <td>    0.270</td> <td>    6.890</td> <td> 0.000</td> <td>    1.329</td> <td>    2.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NW</th>                 <td>    1.2687</td> <td>    0.259</td> <td>    4.892</td> <td> 0.000</td> <td>    0.760</td> <td>    1.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_S</th>                  <td>   -0.0135</td> <td>    0.293</td> <td>   -0.046</td> <td> 0.963</td> <td>   -0.587</td> <td>    0.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SE</th>                 <td>   -0.2616</td> <td>    0.289</td> <td>   -0.906</td> <td> 0.365</td> <td>   -0.827</td> <td>    0.304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SSE</th>                <td>   -0.0684</td> <td>    0.300</td> <td>   -0.228</td> <td> 0.819</td> <td>   -0.656</td> <td>    0.519</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SSW</th>                <td>    0.0849</td> <td>    0.279</td> <td>    0.305</td> <td> 0.760</td> <td>   -0.461</td> <td>    0.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SW</th>                 <td>   -1.0332</td> <td>    0.265</td> <td>   -3.894</td> <td> 0.000</td> <td>   -1.553</td> <td>   -0.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_W</th>                  <td>   -0.6691</td> <td>    0.295</td> <td>   -2.267</td> <td> 0.023</td> <td>   -1.248</td> <td>   -0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_WNW</th>                <td>    0.6232</td> <td>    0.282</td> <td>    2.211</td> <td> 0.027</td> <td>    0.071</td> <td>    1.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_WSW</th>                <td>   -0.2656</td> <td>    0.282</td> <td>   -0.942</td> <td> 0.346</td> <td>   -0.818</td> <td>    0.287</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Changping</th>     <td>    2.5364</td> <td>    0.247</td> <td>   10.254</td> <td> 0.000</td> <td>    2.052</td> <td>    3.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Dingling</th>      <td>    5.8724</td> <td>    0.251</td> <td>   23.415</td> <td> 0.000</td> <td>    5.381</td> <td>    6.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Dongsi</th>        <td>    1.4240</td> <td>    0.241</td> <td>    5.919</td> <td> 0.000</td> <td>    0.952</td> <td>    1.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Guanyuan</th>      <td>   -0.0265</td> <td>    0.238</td> <td>   -0.112</td> <td> 0.911</td> <td>   -0.492</td> <td>    0.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Gucheng</th>       <td>   -4.5053</td> <td>    0.241</td> <td>  -18.706</td> <td> 0.000</td> <td>   -4.977</td> <td>   -4.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Huairou</th>       <td>    1.7981</td> <td>    0.251</td> <td>    7.169</td> <td> 0.000</td> <td>    1.307</td> <td>    2.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Nongzhanguan</th>  <td>    1.3004</td> <td>    0.242</td> <td>    5.380</td> <td> 0.000</td> <td>    0.827</td> <td>    1.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Shunyi</th>        <td>    3.2149</td> <td>    0.241</td> <td>   13.353</td> <td> 0.000</td> <td>    2.743</td> <td>    3.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Tiantan</th>       <td>    1.2205</td> <td>    0.240</td> <td>    5.094</td> <td> 0.000</td> <td>    0.751</td> <td>    1.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Wanliu</th>        <td>    0.4253</td> <td>    0.238</td> <td>    1.784</td> <td> 0.074</td> <td>   -0.042</td> <td>    0.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Wanshouxigong</th> <td>   -1.1074</td> <td>    0.240</td> <td>   -4.614</td> <td> 0.000</td> <td>   -1.578</td> <td>   -0.637</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>15077.358</td> <th>  Durbin-Watson:     </th> <td>   2.003</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>83191.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.047</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.115</td>   <th>  Cond. No.          </th> <td>2.25e+15</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 8.29e-20. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  PM2.5   R-squared:                       0.814\n",
       "Model:                            OLS   Adj. R-squared:                  0.814\n",
       "Method:                 Least Squares   F-statistic:                     8891.\n",
       "Date:                Mon, 08 Nov 2021   Prob (F-statistic):               0.00\n",
       "Time:                        21:57:42   Log-Likelihood:            -9.2537e+05\n",
       "No. Observations:              205604   AIC:                         1.851e+06\n",
       "Df Residuals:                  205502   BIC:                         1.852e+06\n",
       "Df Model:                         101                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "const                  -294.1469     11.031    -26.666      0.000    -315.767    -272.527\n",
       "PM10                      0.5407      0.001    424.172      0.000       0.538       0.543\n",
       "SO2                      -0.0448      0.007     -6.488      0.000      -0.058      -0.031\n",
       "NO2                      -0.0662      0.003    -21.649      0.000      -0.072      -0.060\n",
       "CO                        0.0243      0.000    176.311      0.000       0.024       0.025\n",
       "O3                        0.0724      0.002     39.429      0.000       0.069       0.076\n",
       "TEMP                     -0.6326      0.016    -39.505      0.000      -0.664      -0.601\n",
       "PRES                      0.3040      0.011     28.086      0.000       0.283       0.325\n",
       "DEWP                      1.5922      0.011    144.414      0.000       1.571       1.614\n",
       "RAIN                   3.771e-13   1.13e-14     33.439      0.000    3.55e-13    3.99e-13\n",
       "year_2014                -3.1621      0.146    -21.730      0.000      -3.447      -2.877\n",
       "year_2015                -2.6958      0.148    -18.224      0.000      -2.986      -2.406\n",
       "year_2016                -2.1743      0.148    -14.694      0.000      -2.464      -1.884\n",
       "month_2                   3.0117      0.305      9.866      0.000       2.413       3.610\n",
       "month_3                  -5.2643      0.308    -17.068      0.000      -5.869      -4.660\n",
       "month_4                 -10.0767      0.365    -27.615      0.000     -10.792      -9.362\n",
       "month_5                 -21.1375      0.430    -49.187      0.000     -21.980     -20.295\n",
       "month_6                 -21.2474      0.495    -42.919      0.000     -22.218     -20.277\n",
       "month_7                 -21.0889      0.533    -39.539      0.000     -22.134     -20.044\n",
       "month_8                 -29.2819      0.527    -55.610      0.000     -30.314     -28.250\n",
       "month_9                 -24.4659      0.463    -52.807      0.000     -25.374     -23.558\n",
       "month_10                -14.1036      0.381    -37.055      0.000     -14.850     -13.358\n",
       "month_11                -10.1322      0.308    -32.936      0.000     -10.735      -9.529\n",
       "month_12                 -4.5190      0.283    -15.946      0.000      -5.074      -3.964\n",
       "day_2                    -2.4032      0.376     -6.395      0.000      -3.140      -1.667\n",
       "day_3                    -4.1603      0.378    -11.015      0.000      -4.901      -3.420\n",
       "day_4                    -2.7616      0.379     -7.284      0.000      -3.505      -2.018\n",
       "day_5                    -2.5668      0.380     -6.750      0.000      -3.312      -1.822\n",
       "day_6                    -1.7490      0.379     -4.620      0.000      -2.491      -1.007\n",
       "day_7                    -2.3684      0.379     -6.246      0.000      -3.112      -1.625\n",
       "day_8                    -1.9168      0.376     -5.096      0.000      -2.654      -1.180\n",
       "day_9                    -3.0335      0.373     -8.133      0.000      -3.765      -2.302\n",
       "day_10                   -4.8653      0.374    -13.005      0.000      -5.599      -4.132\n",
       "day_11                   -3.9317      0.369    -10.659      0.000      -4.655      -3.209\n",
       "day_12                   -2.6813      0.367     -7.313      0.000      -3.400      -1.963\n",
       "day_13                   -3.0169      0.374     -8.067      0.000      -3.750      -2.284\n",
       "day_14                   -4.7137      0.377    -12.496      0.000      -5.453      -3.974\n",
       "day_15                   -8.0030      0.385    -20.803      0.000      -8.757      -7.249\n",
       "day_16                   -5.1750      0.386    -13.399      0.000      -5.932      -4.418\n",
       "day_17                   -6.4995      0.374    -17.376      0.000      -7.233      -5.766\n",
       "day_18                   -2.3126      0.374     -6.189      0.000      -3.045      -1.580\n",
       "day_19                   -4.5139      0.374    -12.057      0.000      -5.248      -3.780\n",
       "day_20                   -4.8638      0.377    -12.901      0.000      -5.603      -4.125\n",
       "day_21                   -3.3974      0.374     -9.096      0.000      -4.130      -2.665\n",
       "day_22                   -6.5880      0.378    -17.439      0.000      -7.328      -5.848\n",
       "day_23                   -5.1564      0.375    -13.763      0.000      -5.891      -4.422\n",
       "day_24                   -5.6835      0.368    -15.439      0.000      -6.405      -4.962\n",
       "day_25                   -2.5352      0.372     -6.816      0.000      -3.264      -1.806\n",
       "day_26                   -4.8016      0.378    -12.719      0.000      -5.541      -4.062\n",
       "day_27                   -4.8829      0.371    -13.158      0.000      -5.610      -4.156\n",
       "day_28                   -4.5617      0.372    -12.270      0.000      -5.290      -3.833\n",
       "day_29                   -4.1779      0.376    -11.112      0.000      -4.915      -3.441\n",
       "day_30                   -3.2041      0.381     -8.411      0.000      -3.951      -2.457\n",
       "day_31                   -3.3531      0.439     -7.635      0.000      -4.214      -2.492\n",
       "hour_1                    0.0446      0.322      0.138      0.890      -0.587       0.676\n",
       "hour_2                    1.9623      0.322      6.089      0.000       1.331       2.594\n",
       "hour_3                    3.1066      0.322      9.635      0.000       2.475       3.738\n",
       "hour_4                    4.4556      0.323     13.796      0.000       3.823       5.089\n",
       "hour_5                    3.7400      0.323     11.577      0.000       3.107       4.373\n",
       "hour_6                    2.3905      0.322      7.429      0.000       1.760       3.021\n",
       "hour_7                    1.1684      0.322      3.630      0.000       0.537       1.799\n",
       "hour_8                    0.0690      0.325      0.212      0.832      -0.568       0.706\n",
       "hour_9                    1.7709      0.331      5.348      0.000       1.122       2.420\n",
       "hour_10                   4.2083      0.336     12.506      0.000       3.549       4.868\n",
       "hour_11                   6.5985      0.341     19.365      0.000       5.931       7.266\n",
       "hour_12                   7.8970      0.348     22.669      0.000       7.214       8.580\n",
       "hour_13                   9.2828      0.357     26.030      0.000       8.584       9.982\n",
       "hour_14                   9.4909      0.363     26.115      0.000       8.779      10.203\n",
       "hour_15                   8.2707      0.365     22.632      0.000       7.554       8.987\n",
       "hour_16                   6.1002      0.362     16.850      0.000       5.391       6.810\n",
       "hour_17                   3.3960      0.355      9.558      0.000       2.700       4.092\n",
       "hour_18                   0.3248      0.347      0.937      0.349      -0.354       1.004\n",
       "hour_19                  -2.2969      0.338     -6.795      0.000      -2.959      -1.634\n",
       "hour_20                  -3.2837      0.332     -9.877      0.000      -3.935      -2.632\n",
       "hour_21                  -3.1780      0.328     -9.682      0.000      -3.821      -2.535\n",
       "hour_22                  -2.1856      0.326     -6.708      0.000      -2.824      -1.547\n",
       "hour_23                  -1.1980      0.324     -3.695      0.000      -1.833      -0.563\n",
       "wd_ENE                   -0.0153      0.247     -0.062      0.951      -0.499       0.468\n",
       "wd_ESE                    0.4168      0.269      1.548      0.122      -0.111       0.944\n",
       "wd_N                      1.2486      0.252      4.961      0.000       0.755       1.742\n",
       "wd_NE                     0.6091      0.235      2.595      0.009       0.149       1.069\n",
       "wd_NNE                    0.6610      0.257      2.576      0.010       0.158       1.164\n",
       "wd_NNW                    1.8569      0.270      6.890      0.000       1.329       2.385\n",
       "wd_NW                     1.2687      0.259      4.892      0.000       0.760       1.777\n",
       "wd_S                     -0.0135      0.293     -0.046      0.963      -0.587       0.560\n",
       "wd_SE                    -0.2616      0.289     -0.906      0.365      -0.827       0.304\n",
       "wd_SSE                   -0.0684      0.300     -0.228      0.819      -0.656       0.519\n",
       "wd_SSW                    0.0849      0.279      0.305      0.760      -0.461       0.631\n",
       "wd_SW                    -1.0332      0.265     -3.894      0.000      -1.553      -0.513\n",
       "wd_W                     -0.6691      0.295     -2.267      0.023      -1.248      -0.091\n",
       "wd_WNW                    0.6232      0.282      2.211      0.027       0.071       1.176\n",
       "wd_WSW                   -0.2656      0.282     -0.942      0.346      -0.818       0.287\n",
       "station_Changping         2.5364      0.247     10.254      0.000       2.052       3.021\n",
       "station_Dingling          5.8724      0.251     23.415      0.000       5.381       6.364\n",
       "station_Dongsi            1.4240      0.241      5.919      0.000       0.952       1.895\n",
       "station_Guanyuan         -0.0265      0.238     -0.112      0.911      -0.492       0.439\n",
       "station_Gucheng          -4.5053      0.241    -18.706      0.000      -4.977      -4.033\n",
       "station_Huairou           1.7981      0.251      7.169      0.000       1.307       2.290\n",
       "station_Nongzhanguan      1.3004      0.242      5.380      0.000       0.827       1.774\n",
       "station_Shunyi            3.2149      0.241     13.353      0.000       2.743       3.687\n",
       "station_Tiantan           1.2205      0.240      5.094      0.000       0.751       1.690\n",
       "station_Wanliu            0.4253      0.238      1.784      0.074      -0.042       0.893\n",
       "station_Wanshouxigong    -1.1074      0.240     -4.614      0.000      -1.578      -0.637\n",
       "==============================================================================\n",
       "Omnibus:                    15077.358   Durbin-Watson:                   2.003\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            83191.165\n",
       "Skew:                          -0.047   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.115   Cond. No.                     2.25e+15\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 8.29e-20. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basemodel = sm.OLS(ytrain,xtrain).fit()\n",
    "\n",
    "basemodel.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### STRONG MULTICOLLINEARITY persists!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  21.8\n",
      "RMSE test:  21.83\n"
     ]
    }
   ],
   "source": [
    "ypred1 = basemodel.predict(xtrain)\n",
    "ypred2 = basemodel.predict(xtest)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(ytrain,ypred1))\n",
    "\n",
    "rmse2 = np.sqrt(mean_squared_error(ytest,ypred2))\n",
    "\n",
    "print(\"RMSE train: \",round(rmse1,2))\n",
    "print(\"RMSE test: \",round(rmse2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Model Name','Alpha','L1_ratio','R2','RMSE_TRAIN','RMSE_TEST']\n",
    "\n",
    "results = pd.DataFrame(columns= cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814</td>\n",
       "      <td>21.79785</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Model Name Alpha L1_ratio     R2  RMSE_TRAIN  RMSE_TEST\n",
       "0  LinReg_without_VIF   NIL      NIL  0.814    21.79785  21.827985"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = pd.DataFrame({'Model Name':['LinReg_without_VIF'],'Alpha':['NIL'],\n",
    "                      'L1_ratio':['NIL'],'R2':0.814,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  MODEL2 : WITH VIF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m70pqC7wj6bnGSi+5KfK0lc7FU8pDRAv9JNAZWGZmXwOY2TJgJXAucJmZVYbb7wG+Bo4wsyVm9m64fRVB8uXGyyzmnHPpSvhJoNCVwDhgO0kfSfqrpMMJchAvMLMv0o6dBOwV3RBmJduPDGkpJQ2RNEnSpJEjR6bvds655FRWZr8UmYI2B5nZakkHAIcC/YBHgRvIPP+/otsltQH+DVyaocIoWGYx55yroQjv8LNV8NFBYZPPy8DLkqYDFwA7SGobNvek7A+MBZDUnKACeMjMHitwkZ1zrk6lnGO4oM1BknaXtGtkUy/gQ+A+YISkpuFx5wCbAy9KEkGy+vfNbEQhy1suLr9qOGddcBnzFlTQf9Bg/j322cYuknOlpYT7BAr9JNAGuE1Se4JRP7OAIcAqguTzH0mqAj4ATjIzk/Qd4GxguqQpYZwrw2xjLg9u/P2vG7sIzpW2Eh4dVOg+gXeAPrXsvjhc0s95jTzkAHXOucQU4R1+tvyNYeeci8kq/UnAOefKlz8JuHLkGcucC3kl4IpVUhdUz1jm3Eb5HCIq6WjgFqAp8A8zG562vwNwN7Az8BVwnpnNyObcTAo+gZxzzm1y8jRENBwmfwdwDLAncIakPdMOuxKYYmb7AOcQXPSzPbcGrwSccy4mW29ZL/U4CJhlZnPM7BtgFHBi2jF7Ai8AmNkHQHdJ22Z5bg1eCTjnXFwNeBKIznMWLkMikboCCyPrFdScMHMqcDKApIOAHYBuWZ5bg/cJOOdcXA0YIZo2z1m6TO9EpT8+DAduCV+enQ5MJnj5Nptza/BKwCXKs5a5cpDHjuEKYLvIejdgcbXvCibQPBcgnFZnbrhsXt+5mRSsOUiSSbo5sv5LSVdH1odI+iBc3g6ni0jte0jSh5JmSLo7nFDOlQDPWubKQlUDlrpNBHaVtKOkFsDpwJjoAZLah/sAzgfGhxVDvedmUsg+ga+Bk8NsYtVIGkgwm+h3zGwPYCjwsKRO4SEPAXsAPYFWBD+4KwG9e/Vki3ZtG7sYziXKqizrpc44ZuuBi4BnCRJojTazmZKGShoaHtYDmCnpA4KRQJfUdW59ZS9kc9B6gnawy4DfpO27Arg8zDRGmEryPuBC4LfRyeIkvU3wmOOcc0XB1ucxVnC9eypt252RzxOAXdPPq+3c+hR6dNAdwFmStkjbvhfwTtq2TJnFmhPMKPpMemDPLOacazT5aw4quELPIvqFpPuBnwFr6zm8Wmax0F8J2r9ezRDbM4s55xqFFeHFPVuN8Z7AX4AfAa0j294DDkg7bv9wOwCSrgK2Bn6ecPmcc65hSvhJoOCVgJktB0YTVAQpfwL+KGlLAEm9gB8S3Pkj6XzgKOAMs1Kuc8uPZy1z5cCqsl+KTWO9J3AzQS82AGY2RlJX4A1JRpBpbLCZLQkPuROYD0wIhsXymJldU+Ayuxx41jJXDorx4p6tglUCZtYm8vlTghcbovv/BvytlnP9pTbnXNGyytJNfugXV+eci8mfBJxzroxZlT8JlIVmLeqdkC9n679ZlFjsUpNkwhrPWuaS4E8Crux4xjLnNjLzJwHnnCtbVeu9EnDOubJlJTxHgVcCzjkXk3cMO+dcGSvlSsBzDOfgqAF9mTljPB+89xq/uvzCGvt/8fOhTJo4jkkTxzFl8gt8vXYBHTq0B+DvI29mccVUpkx+ocCl3rQMu34Ehx13OoMGD63/YOcSZpb9UmyKohKQ1EnSKEmzJb0n6SlJu0naS9KLkj6S9LGk34bp1BpNkyZNuPWW6xh4/GB67tuP004bRI8e1af2vnnEnfQ+cAC9DxzAsGHDGT/+TVasWAnA/feP5riBZzVCyTctnrHMFROrUtZLsWn0SiC8qD8OvGxmO5vZnsCVwLYEqdGGm9luwL5AH+CnjVZY4KAD92P27HnMnbuAdevWMXr0k5xw/FG1Hn/aaScy6tEnNqy/+tpbLA8rBJc7z1jmiklVpbJeik2jVwJAP2BdWuacKcBuwOtmNi7ctoZg0rlGnZGsS9dOLKzYmLu5YtESunTplPHYVq1actSAvjz2eIMS/TjnSkyVKeul2BRDJbA3NbOKQYZsY2Y2G2gjqV36wYXKLJapNcpqaegbOHAAb0yYtKEpyDm3aTJT1kuxKebRQZkyi6XU2F6ozGKLKpawXbcuG9a7de3MkiWfZjz2tFNPqNYU5JzbNBVjW3+2iuFJYCY1s4qltveObpC0E7DazFYVomCZTJw0hV122ZHu3bejefPmnHrqiYz9z7gax7Vr15bDDj2YMWM8iYpzmzofHRTPi8Bmkn6c2iDpQOBj4DuSvhtuawXcSpCFrNFUVlZyyaXDeOq/DzNj2sv8619jee+9jxjy47MZ8uOzNxw36MRjeO758axZUz2V8oMP3MFr48ew+247M2/OJM794emF/hE2CZ6xzBWTUh4dpNraswtaCKkLQe7hA4CvgHnApUBL4DagM9AUeAC4xuovdCI/lM8imrykJ5DzWURdBrGvzNN3PD7ra07PuWOLqiYoij4BM1sMnFrL7r4FLIpzzjVYEdxL56woKgHnnCtlxTj0M1teCTjnXEzFOPQzW14JuLKSVJ+D9zWUN28OKhPeeZu8JC+mnrXMJaWyqhgGWubGKwHnnIuplPsESrf6cs65ImENWOoj6WhJH0qaJanGXGmStpA0VtJUSTMlnRvZN0/SdElTJE3Kpuz+JOCcczHl60lAUlPgDuBIoAKYKGmMmb0XOexC4D0zO17S1sCHkh4ys2/C/f3MbFm23+lPAs45F1MeJ5A7CJhlZnPCi/oo4MT0rwPahtPwtwGWA+tzLbtXAs5FeMYyl4uqBizRGY/DZUgkVFdgYWS9ItwWdTvQA1gMTAcuMbOqcJ8B4yS9kxa3VkVTCUjqJunJMIPYbEm3SGoh6aCwfWtK2AZ2UmOX1W26PGOZy0WlKevFzEaaWe/IEp37PtOjQnpXwlHAFKAL0Au4PTK9/iFmtj9wDHChpMPqK3tRVALhY81jwBNmtitBQpk2wHXADKC3mfUCjgbukuR9GS4RnrHM5aIKZb3UowLYLrLejeCOP+pc4DELzALmAnvAhil4MLOlBBkbD6rvC4uiEgCOAL4ys3sAzKwSuAw4L1xPtXe1JMFcAc45lwtDWS/1mAjsKmlHSS2A0wnS7EYtAPoDSNoW2B2YI6m1pLbh9tbAAIKb6DoVSyWQKYvYFwQ/7C6SviVpJkH719BIpbBBoTKLOedcuob0CdQlvLZdBDwLvA+MNrOZkoZKSnVU/QHoI2k68AJwRTgaaFvgNUlTgbeB/5rZM/WVvViaVWrLIibAzOwtYC9JPYD7JD1tZl9FDyxUZjHnnEuXxR1+9rHMngKeStsWzcG+mOAuP/28OcC+Df2+YnkSyJRFrB1B29js1DYzex/4kiAvsXPOFYX1DViKTbFUAi8Am0s6Bza8MHEzcC+wbaojWNIOBO1f8xqnmG5T5xnLXC7y2CdQcEXRHGRmFg79/Kuk3xJUTk8BVxIkm/m1pHUETWo/bcjbcM41xI2/r/GWvnP1KsKskVkrikoAwMwWAsdn2PVAuDjnXFHKYuhn0SqaSsA550pVKY9E8UrAOediqm/oZzHzSsA552KqlDcHOVfWSi1t5V3dBicSF+CCigcTi12s/EnAuRKQ1AXV01Y6Hx3knHNlzEcHOedcGfPRQc45V8a8Ocg5V69h149g/Otv07FDe5548M76TyiA7fruQ5/fn42aNuGDR15myh1jq+1v0bYVR9z6E9p03RI1bcq0u57iw9Hjad25I/1uGcrmW2+BVRnvP/wSM/5ZvlNsVDZ2AWLI69xBkraMZAH7RNKiyLpFPk+R9OvwnJclLQgTy6TiPCFpdfi5u6S14TnvSbpTUrHMeeRc1oota5maiEOu/QFPnf0nRvf7FbuceDDtd+1S7Zi9fnAkKz5exL8G/Iax37+Og393Jk2aN8Uqq3jzmocZ3e8Knjjhavb6wXdrnFtOqpT9Umzy+iRgZv8jSHeGpKuB1WZ2U7i+OswOlslK4BCCubDbA53T9s82s17hRHIvAoMIMpE5VzJ69+rJoiWfNnYxNtim1858Me9TVi34DIBZT75J9wEHMOXjjYmszIzmrVsB0Lx1S75e+SVV66tYs3Qla5auBGDdl1+x8uPFtO7UkZUfpyfBKg+lPES0WO6oRxFk0AE4mVou8GHChTeAXQpULuc2WZt37sDqJcs3rH/5yXJad+5Q7ZiZ9z5H+127MPid2/n+8zfwxu8eAKveDdqm21ZsufcOLJ08m3KVr6QyjaGQlUCrtOag0yL7XgAOC6eQPh14NFMASZsTpFWbnmGfZxZzrgGUaVhj2jCXbn178r+Z83nwgIv411G/4ZBrz6F5m1Yb9jfbfDMGjLyECVc/yLrVaxMucfEyZb8Um0J2DK+tozmoEngNOA1oZWbzVP017J0lTSH4FX3SzJ5OD+CZxZxrmC+XLKdN544b1lt36siXn6yodszupx6+obP4i3mfsmrhZ7TfpTOfTZlDk2ZNGTDyEj5+/A3mPj2poGUvNsWYLCZbxdIcBEGT0G3A6Az7ZptZLzPbz8yuLmyxnNs0LZ06hy127ETb7bamSfOm7HLiwcx/7t1qx6xetIyu39kLgFZbtaP9zp1ZNX8pAIffdD4rZy1m+t9r3JOVHWvAUmyKaYjoq8ANwCONXRDnknD5VcOZOHkaK1d+Qf9Bg/npj87mlOOParTyWGUVr/32Po596FeoSRM+fPQVVny0iB6DjwDg/Qdf5N1bnqDviAv43vM3IOCt6x/lqxWr6XTgbuz2vUP53/sLOOXZ6wB4+4+jWfji1Eb7eRpTMY76yVYhK4FWYZNOyjNmtiGNk5kZcFMBy+NcQRVj1rKFL07l0bQL9/sPvrjh85pPV/LUWX+scd4nEz9KdBK6UlOMHb7ZSqwSSG+2MbOmtRzXt5btbcL/z8MTyzvniphXAs45V8aKsa0/W14JOOdcTOu9T8A558qXPwk45xKRVMKa86Zcw929fpdI7HJUVcLVgFcCzsVUqhnLyjENZFK8Y9g558pY6T4HeCXgnHOx+ZOAc86VsfUq3WeBYpo7yDmXg2HXj+Cw405n0OChjV2UspXPuYMkHS3pQ0mzUsm30vZvIWmspKmSZko6N9tzM0msEpBUGU4ZPUPS/wungU7fPjZMIpOeQSy1nBPuO0/SdEnTwvNOTKrczpWaYstYVo7ylU8gnE7/DuAYYE/gDEl7ph12IfCeme0L9AVultQiy3NrSPJJYG048+fewDfA0AzblxP8QCmp2UJTy/2SugG/Ab5jZvsABwPTEiy3cyWld6+ebNGubWMXo6xVYVkv9TgImGVmc8zsG4LZldNveg1oG6bkbUNwHV2f5bk1FKo56FUyZwObAHSt59xtgFXAagAzW21mc/NbPOecy11DmoOiCbDCZUgkVFdgYWS9gprXyNuBHsBiggRbl5hZVZbn1pB4JRDmBT6GtGxg4aNLf2BMZPPOac1BhwJTgU+BuZLukXR8Ld/jmcWcc41iPZb1YmYjzax3ZIlesDJNQJH++HAUMAXoQpDT/XZJ7bI8t4YkRwdFp45+Ffhn2vbuwDvAc5FzZmfKPibpaOBAgkrjz5IOyDBLqWcWc841ijxecCqA7SLr3Qju+KPOBYaH0+/PkjQX2CPLc2soRJ9ALzO7OGyj2rAd2AFoQfU+gYws8LaZ3UCQg/iUxErtnHMNlMdE8xOBXSXtKKkFwfVuTNoxCwhuiJG0LbA7MCfLc2totCGiZvY58DPgl5Ka13acpC6S9o9s6gXMT7h4zpWMy68azlkXXMa8BRX0HzSYf499trGLVHasAf/VGcdsPXAR8CzwPjDazGZKGiopNbjmD0AfSdOBF4ArzGxZbefWV3YFTxT5J2l1KjFMXdsljSXIK/wqQcE/jBx+N/AkcA9B+9dXwGfAUDObXcfXe3OQK3lJzx2U1JxHJSj2RNAXdT8t62vO7fMeLaqJp5PMLFajAsi03cyiHb2tagl3RL7K5Zxz+eaziDrnXBmr9ErAOefKl08g55xzZay+Dt9i5pWAczHd1W1wYrHPm3JNYrGT6nguxw5nfxJwziWiVLOWlRt/EnDOuTLmTwLOOVfGKhN636oQvBJwzrmY/D0B5xzb9d2HPr8/GzVtwgePvMyUO8ZW29+ibSuOuPUntOm6JWralGl3PcWHo8fTunNH+t0ylM233gKrMt5/+CVm/LPxp34Ydv0Ixr/+Nh07tOeJB+9s7OIUtbLvE5BUSTBVdHOC5Ab3AX8xsypJfQmmfojmAPglcBww38z+EsZ4FlhoZueH6zcDi4DH2DidRAtgPPDTcP5s54qCmohDrv0B/z1zOF8uWc7J/72GeePeYeXHGydx3OsHR7Li40U8c+4IWnZsy2njb+Tjx1/HKqt485qHWTZjHs1bt+Tkp/9Axfjp1c5tDIOOPZIzTzmBK/9wU6OWoxSU8sUoXxPIpWYM3Qs4EjgWuCqy/9W0jGHPA28AfQAkNQG2AvaKnNMHeD38nJpieh+CtGmD8lRu5/Jim14788W8T1m14DOq1lUy68k36T7ggGrHmBnNWwczozRv3ZKvV35J1foq1ixdybIZ8wBY9+VXrPx4Ma07dSz0j1CDZyzLXh4zixVc3mcRNbOlwBDgojD9WW1eJ6wECC7+M4BVkjpI2owgc87ktNjrCSqPTFnKnGs0m3fuwOolyzesf/nJclp37lDtmJn3Pkf7Xbsw+J3b+f7zN/DG7x6AtA7FNt22Ysu9d2Dp5LrmR3TFphLLeik2iUwlbWZzwtjbhJsOTcsYtrOZLQbWS9qeoDKYALwFfBvoDUyL5CAAIExW35+0LGXhPs8s5hqNMk1EmfbvvVvfnvxv5nwePOAi/nXUbzjk2nNo3mbjnInNNt+MASMvYcLVD7Ju9dqES+zyycyyXopNkh3D0X8Vr5rZwAzHpJ4G+gAjCPJh9gE+J7jjT9k5zEZmwJNm9nR6IM8s5hrTl0uW06bzxiac1p068uUnK6ods/uph2/oLP5i3qesWvgZ7XfpzGdT5tCkWVMGjLyEjx9/g7lPTypo2V18xdjMk61EngQk7QRUAkvrOTTVL9CToDnoTYIngWh/AIR9Ama2X3paSeeKwdKpc9hix0603W5rmjRvyi4nHsz8596tdszqRcvo+p2g26vVVu1ov3NnVs0P/okcftP5rJy1mOl/r3F/40pAHjOLFVzenwQkbQ3cCdxuZlZ3twCvA78A5phZJbBcUnuCPoIf57tsziXFKqt47bf3cexDv0JNmvDho6+w4qNF9BgcpMJ4/8EXefeWJ+g74gK+9/wNCHjr+kf5asVqOh24G7t971D+9/4CTnn2OgDe/uNoFr44tRF/oiBj2cTJ01i58gv6DxrMT390Nqccf1SjlqlYlfIQ0bxkFsswRPQBYEQdQ0SvNbN/SWoKrABuNbNhYax7gW+b2e7henfgP2a2dwOKVLp/I67kJDmB3AUVDyYSN8m5g0pwArnYmb6O3f7YrK85Ty14atPLLGZmTevY9zKwRS37KoF2adt+mLY+D2hIBeCccwXl00Y451wZK+XmIK8EnHMuplIeHeSVgHPOxVSM4/+z5ZWAc87F5E8CzpWxpEbwJKkER/AUtcoSns/SKwHnnIupdJ8DvBJwzrnYvDnIOefKmFcCzjlXxkp5dFAiE8hFSaoMp4+eIWlsODdQdP9USY+kbbtX0vfCzy9LmhTZ11vSy0mX2znnsuVJZeqWyjq2N7AcuDC1Q1KPsAyHSWpdR4xtJB2TcDmdcy4nVVaV9VIfSUdL+lDSLEm/zrD/8khulhnhjXbHcN88SdPDfVnNSV6ISiBqAkHOgJQzCSabGwecUMd5NwLDEiyXc87lLF9PAuGkmncAxxCk0j1D0p7RY8zsxlSqXuD/gFfMbHnkkH7h/t7ZlL1glUD4w/UHxkQ2nwY8CjwCnFHH6ROAryX1qyO+ZxZzzjWKPGYWOwiYZWZzwsyKo4AT6zj+DILrZ84K0THcKswK1h14B3gOQNKBwGdmNl9SBXC3pA5mtqKWONcSPA1ckWmnZxZzzjWWhrT1SxpCkIc9ZWR4/YKgpWRhZF8F8K1a4mwOHA1cFNlswDhJBtwViVurgvUJADsALdjYJ3AGsIekecBsgimlT6ktiJm9CLQEDk6ysM4511DWkP/MRppZ78gSvVBnyjVQWw1zPPB6WlPQIWa2P0Fz0oWSDquv7AVrDjKzz4GfAb+UtBnwfWAfM+tuZt0JHnnqahICuA74VaIFdc65Bqoyy3qpRwWwXWS9G7C4lmNPJ60pyMwWh/9fCjxO0LxUp4J2DJvZZGAqcCqwyMwWRXaPB/aU1LmO858CPku2lM451zCVVpX1Uo+JwK6SdpTUguBCPyb9IElbAIcTZG1MbWstqW3qMzCAIHd7nRLvEzCzNmnrx4cfH0jbXgmkKoAfRrb3TTvugLwX0jnnYshXUhkzWy/pIuBZoClwt5nNlDQ03H9neOhJwDgz+zJy+rbA42Fe92bAw2b2TH3fmZccw0Vok/yhnHOJiJ3zd7ete2d9zfnos0mbXo5h55wrZ55e0jnnylgWHb5FyysB51zRW7dsTmKx85Fgp8oq81CSxuGVgHPOxVSME8NlyysB55yLqZQH2Hgl4JxzMfmTgHPOlTF/EnDOuTLmo4Occ64IDLt+BONff5uOHdrzxIN31n9CnmSTLKZYxZ47SNKfJV0aWX9W0j8i6zdL+rmkW8MsONMlTZS0Y7g/lQlnqqRxkjpFtr+a9l1TJNU7F4ZzrjwNOvZI7hxxbcG/t9zTS74B9AGQ1ATYCtgrsr8P0BboQjBraE+CeS9WRo7pZ2b7ApOAKyPb20raLozdIw9ldc5twnr36skW7doW/HvzmFSm4PJRCbxOWAkQXPxnAKskdQinjO4BrAWWmAXPTGZWUUvymPHALpH10QTZxyAPGXSccy4JeZxKuuBiVwLh/NXrJW1PUBlMAN4Cvg30BqYBDwPHh805N0var5ZwA4HpkfV/ASeHn48HxtZWDk8v6ZxrLKX8JJCvjuHU00AfYARBirQ+wOfAG2ZWIWl34IhweUHS983shfD8lyRVElQY0YTyy4EVkk4H3gfW1FYATy/pnGssxdjWn618VQKpfoGeBM1BC4FfAF8AdwOY2dfA08DTkj4FBgGpSqCfmS2rJfajwB1Ecgw451wxqawq49FBodcJmnKWm1llmPOyPUGT0ARJ+0vqAhs6j/cB5mcZ+3HgTwRJFpxzrlaXXzWcsy64jHkLKug/aDD/HluYy0ZDcgwXm3w9CUwnGBX0cNq2Nma2TFJv4O9hRzHA28Dt2QQ2s1XAHwHCjDnOOZfRjb//daN8bzF2+GbLM4s554pewlNJx767bNly+6yvOV99taCo7mb9jWHnnIupGJt5suWVgHPOxVRVwh3DXgk451xMpfscQMNectgUF2BIqcUutbilWGb/s/A/i3JZ8jVEtJQNKcHYpRY3ydilFjfJ2KUWN8nYSZZ5k+KVgHPOlTGvBJxzrox5JbBxvqFSil1qcZOMXWpxk4xdanGTjO2zSGZpU31ZzDnnXBb8ScA558qYVwLOOVfGvBJwzrky5m8Mu5IkqQ1gZvZlY5elLpIGEaRMnW5mPh26Kzpl2TEsqQVwFkFOZAPeAx62IPFNrjH3MbNp4efmwBXAQQRJdq41s1qzomURew/gRIKMbQYsBsaY2fu5xozE7Qq8ZWarI9uPNrNnYsRtCpwPdAOeMbPXI/uGmdm1MWL/FPg10BoQsAr4o5n9NdeYYdwmAGZWFf5+7A3MsyA3Rq4x/0rwO/YG0B8Ya2Z/iFPOSOzjgWlmNj9c/x1wCkGejkvMbG6M2INIoOKS9BK1z7BgZtY/h5jTatsVxtynoTHLTdlVApL2BMYQJMJ5h+CXZX/gEOAEM3svx7jvmtn+4eebgS2BewgyqG1pZufkGPcK4AxgFFARbu4GnA6MMrPhOcb9GXAhQdrOXgQXjifTf5YcY/8D2Jwgb8TZwCtm9vO4sSUNI8hgd5GZzQm37QTcQlCR5VS5hBe9u4AqYChwJfAlsBvwEzOrNbd1PXFnAPuaWaWkzYFXzeyAXGJliD0NONjM1kgaSJDW9QxgP+D7ZnZUjnGTrLgy/ewHA78ClprZgTnEnEJQsTxMkIN8bXR/qpJ0dWjseSsKvRCktDwyw/bvAi/FiDs58nkK0Dz8LII7tlzjfpSKlba9BfBxjLippD8A3YFJBBVBtZ8lx9jTIp+bEYzZfgzYLE5s4EOgZYbtrYCP4vzdAZ2AHQlSou4ebt8BmBQj7rt1rcf8M54a+Xw3cEU+vofgybVp+Hlz4J18lTntew4HngdeBY6JGWsP4PfAu8CDwLFAsyTKvSku5dgx3NXMnkvfaGbPE1wIcrWFpJMknQJsZmbrwrhGvEkGq4AuGbZ3DvflqqmFTUBmNg/oCxwjaQRBxRVHi9QHM1tvZkMIKsYXgTZxApvZVxm2rSXenwVm9okFTSgLzOzDcNt84g2e2EPStHCZHlmfXkczRrYkqU3YjNWfjfm6AVrGiPuNmVUCWNCEmdcEKJKOkvQa8FvgOjM71MyejhPTzD4ws6sseMIcC9wPXJaH4paFcuwYbiJpM0tr/5fUknh/Hq8AJ4Sf35S0rZl9KqkTsCxG3EuBFyR9DCwMt21P0GZ7UYy4n0jqZWZTAMxsddiscDfQM0ZcgEnp/Qpmdo2kxcDfYsStkNTfzKIXPCT1B5bEiIukJmZWBZwX2daUSIWWgx5xylSPvxBUrF8A75vZJABJ+xHvz2KPSAUlYOdwPXYbu6SJwNbAjcCEcNuGpkEzezfHuF0JmkdPAlYQVACP51rOclOOfQLDCNohLwrvgJHUHbiV4NH/msYrXWbh3d5BBJ24IugbmJi6Y8sxZjdgvZl9kmHfIRbpzC0WkvYCngReI+jPMeBAgv6cE81sZo5xDyRoHoOgcjVgNrAtcKiZPRiz6IkIL37bEDQNVYXbOhM0Hy7IMeYOde23GG3skl6m7o7hI3KI+QrQFhgN/Auo1pFvMTr2y0XZVQIAki4i6IzanOCiuhq4ycxuS+j7OmW62OYhbhuLjOrJMUY/IqOkzOylPJVtG4KO5+gIrL+a2acxYu5C0GS3WxhXwEzgY2CRmc3OMW5z4DqCp4BUE1A34F7gylTTXg5xV1H9oqdwPXVX3S6XuGHswanKKb3SlnSRmd2eY9xLCQZNTDaz9bmWr1AkzaP6n3Hqc+rPeKeCF6rElGUlkCKpLYCZrUr4e/5rZsclEHeBmW2f47ldCTprv6L6KKlWwElmtihGuQ4hGK1xb1rsHwBn5fqUIek/BBflaWnbewNXmdnxOcb9M8Hd5GWp3wVJ7YCbgDVmdmmOcZ8gqLQeIxjJldPdeS2xo6PRqo24ijkC6yaCEVh7ANMIRgm9DkyIe1ct6eS69pvZY3Hiu9yUXSUgqc6hmmZ2f6HKkg1JP69tF/AbM+uYY9zHgSfN7N607ecAp5jZibnEDWO8STC0cnLa9l7AXWb2rRzjzjCzvWvZN93McurLCPtbdrO0fwxhn8AHZrZrLnHDGFsAJxO0WbcEHiWoEOJeUCeb2X7pnzOt5xi/BdCboEL4drisNLM9Y8S8p47dZmbn1bG/tpjbEAzp3YWg0hpuZl/kWMSyVI4dw5nGIgs4nqDNvagqAeB6go60TI/mcUau7GlmJ6VvNLP7Jf0mRlyAdukVQBh7SurpK0d1jXppFSOupVcA4cZKSbHukszsc+AeSfcBpwG3EfwcI+LEJXMTSG3ruWgFtAO2CJfFbOw3yYmZnVvbPknb5hj2foKnzduAgQR9ez/MMVZZKrtKwMwuTn2WJII3h68A3iRoF86JpJ7A3wkqkqcJxm2vCPe9bWYH5Rj6XeAJM3snw3een2t5gaaZNoad0Bn3NYAkdUj9/JGNHYlXcU2U9GMz+3ta3B8RXAhy9Z6kc9KfAiUNBj6IERdJfQhe4jqUoEP7JDN7NU7MUGoUT3QED+F6zu3gkkYS9LesAt4iaA4akf53mQ/hU9IpwJkEI6m65hCmk5mlblqelZTTCKNyVnaVAICkZgR3C78g+EX/XmpseAx/A64mqEzOB16TdELYWdk8RtxzSRvxENE7Rtz/SPo7cKmF8+9Iag38GXgqRlzCGOMk/ZKgEgM4APhjuC9XlwKPSzqLjRf93gTDOGs81TTAhcBjks6j+qijVnHihp2WKwne9h5C+DSXGhaZ65DIUFLDT7cneKnvY2ARwUi0lfkKLqkVwVDqMwn6idoSvFU/PveQ6sDG9xmaRtd9dFD9yrFP4ELgEoKXa4bHGfKWFneKmfWKrPcjeFP2bIJRMTlPw5CEcETM9QSVzHyCC98OwH0Ena/fxIw/kGAE1l7hppnAjZbjFAxpsfsRzO0DMNPMXowbM4x7BJFRR+nvI+QQ72XyPCQyi+9sCpxuZg/FiCGCP4c+4bI3wY3IBDO7Kkbch4DDgHEEFeOLwCwz2zFGzHkELwpmeqnNRwdloRwrgSpgKfAZGYbv5foyjKSpwGFhG3Bq2z7Av4GOZrZljnHH1LXfzE6oa38dcQ9k413eLkA/gjbVD4Cr/Q6quIWjly4kaEIZAzxH8PLgL4EpcTr2I9/RjeAdjD4Evxtbmln7GPGmEvw7ux941MwWSprjF+rGVY6VQCIvw0g6E5hjZm+mbd8e+K2Z/TjHuJ8RvCn8CEHTVbU7HjN7Jce47wLfNbPlkg4juDO7mGAyuR5m9r1c4oaxf1fHbrM8TUhW7CT9ysz+FH7+vpn9v8i+683syhixnyR4O3YCwbQRHQiaxS6x8C3wHONeQjAS6BBgHeHw0PD/01MvpcWIvwdBU9BpBDdjewA9c32PJvz3vDJ18xU+JQ4C5gF3xH2iLQdlVwlkS9IEM/t2EZSjKXAkQefiPsB/gUcsx7djI3Gnmtm+4ec7gM/M7OpwvVrTVg6xf5Fhc2vgRwR3k7HmDyoVSY3lD8/fMCQ2/B1ZBmwf950XBXNHvQG8bmaxpuLI4rt6E1QI3wMqzKxPDjHeIuhsXxwOQX4euIHg38o6M4szeKIslGXHcJYaNAlXUs02FkwN8QzwjKTNCCqDlyVdY/HecG4qqZkFb4X2J+i4TIn1e2FmN6c+h0NCLyHoexgF3FzbeZsg1fI503pDbXiLORzKOjduBRD6T6qPRdKOFslLIOnkfL7QZcF8R5Mk/Ro4Nccwrcxscfh5MHC3md0cjnKbkodibvK8EqhdQx+Rvk0dzTZxhBf/4wgqgO4EY6Hj/mN8BHhF0jKCOdhfDb9rF+Dzuk7MRjgc9OcEQ3DvA/ZPYphhkUtyLP++kr5g4+9Zq8i6We5TUtxEMGoHgv6s6NPKMGL83tXSj3EhQT/GVIJpoBscNvL5COD/YENyoFyLWla8EsifTmxstjmT/DXb3EcwOuNp4PdmNiNuQQHM7DpJLxBMST0u8rJUE4K+gZxJupHgLdmRBO29seY3KmG9Ihfm1EWacD3OdM+YWdx3OWqT5NPLA2zsxzgfuJygH2NQjH6MFyWNBj4h6BdJPcV0Brw/IAveJ1CLOK/eR5ptbgRiNduEo5mieXTTJ8jKeRKypIRl/ppgXHymEVhFV+Yk5GP6hjpityTIgpaaLuFuy8OEb6XWjyHpMoIcFWsJUsQuDrfvB2xjnte5XmX3JCDp4PQRPLU4O4fYeW+2MbOSS/xTimVOSJJ3WPcR9Au8SpBJay+Cvpe4dgr7txT5TLie83j+UBL9GF0JhrD2AAZKik54V2PqEldT2T0JpN3p5G0EUFqzzah8NdskdcfnkiepgjrmCDKznOcPSrurbga8nY8XEiUdXtf+XIckh7Er2fhUK4I3slPZy2I9ISqBCe/KRdk9CVC9XTNWu2yas9mYnPwSbZx4LO4veFJ3fC55TQmaKpLooYzeVa/PVydo9CIvaetw22d5ip1UPwYkMOFduSjHJ4GpBPl0mxB0IvUl8o+02N6UTeqOzyUvbht6PbETuasOp4z4HcHgABH8O1kP3GbFmXUvfcK7N4E3y3AkWs7K8UlgCzYmOoGNE5xB0Iab0yvsCTbbJHLH5woisb+sBO+qLwW+AxyYekdA0k7A3yRdZmZxJgBMQqIT3pWDsnsSSIqkR9nYbHMMMN/MYjfbJNmO6pIlqWOxPVnWR9Jk4EgzW5a2fWuCocSJjHaKI6kJ78pF2VUCSi43qzfbuJKnurO31bqvGOR7wrtyUY5D+aLpGtPH7zc4vV1EtWabGHGca0x1vWBVdC9fSfqZpFGSFhLkJBgIfEjwsmJOqVfLTTn2CST1RmTqNf5UnHy9xu9cIUV/j6Niv+WckO7Av4DLkp7wblNVjpVAIvO5JDz8zbmCKLXfYzP7ef1HubqUY5/AGmAWwZ3NzuFnwvWdzKx1Y5XNOecKrRyfBJLKzeqccyWn7CoBqyVzWDih1ekE+Xadc64slN3oIEntJP2fpNslDVDgYmAOuSe2cM65klSOfQKJ5GZ1zrlSVI6VQCK5WZ1zrhSVXXMQaXOaA/nKzeqccyWnHJ8EfC4e55wLlV0l4JxzbqNybA5yzjkX8krAOefKmFcCzjlXxrwScM65Mvb/AUewVtbQ/XD2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(df_train.corr()[df_train.corr()>0.7],annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(df_train.corr()[df_train.corr() < - 0.7],annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SUGGEST TO REMOVE TEMP,DEWPT,CO,PM10 on basis of high correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VIF:\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>WSPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>1026.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PM2.5  PM10   SO2   NO2     CO    O3  TEMP    PRES  DEWP  RAIN  WSPM\n",
       "3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4  1024.5 -19.4   0.0   3.1\n",
       "4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0  1025.2 -19.5   0.0   2.0\n",
       "5    5.0   5.0  18.0  18.0  400.0  66.0  -2.2  1025.6 -19.6   0.0   3.7\n",
       "6    3.0   3.0  18.0  32.0  500.0  50.0  -2.6  1026.5 -19.1   0.0   2.5\n",
       "7    3.0   6.0  19.0  41.0  500.0  43.0  -1.6  1027.4 -19.1   0.0   3.8"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df_train.select_dtypes(include = np.number)\n",
    "df2.drop('REF_NO',axis=1,inplace = True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.873030</td>\n",
       "      <td>TEMP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.719981</td>\n",
       "      <td>PRES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.919684</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.834697</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.048429</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.085993</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.934034</td>\n",
       "      <td>DEWP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.977035</td>\n",
       "      <td>WSPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.549211</td>\n",
       "      <td>O3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.320031</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RAIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          VIF Features\n",
       "6   18.873030     TEMP\n",
       "7   17.719981     PRES\n",
       "1   11.919684     PM10\n",
       "0   11.834697    PM2.5\n",
       "4   10.048429       CO\n",
       "3    9.085993      NO2\n",
       "8    6.934034     DEWP\n",
       "10   5.977035     WSPM\n",
       "5    5.549211       O3\n",
       "2    3.320031      SO2\n",
       "9         NaN     RAIN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "\n",
    "vif['VIF'] = [variance_inflation_factor(df2.values,i) for i in range(df2.shape[1])]\n",
    "\n",
    "vif['Features'] = df2.columns\n",
    "\n",
    "vif.sort_values(by = 'VIF',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.067618</td>\n",
       "      <td>PRES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.661750</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.377118</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.601387</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.917688</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.338926</td>\n",
       "      <td>WSPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.360372</td>\n",
       "      <td>O3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.314309</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.845062</td>\n",
       "      <td>DEWP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RAIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF Features\n",
       "6  15.067618     PRES\n",
       "1  11.661750     PM10\n",
       "0  11.377118    PM2.5\n",
       "4   9.601387       CO\n",
       "3   8.917688      NO2\n",
       "9   5.338926     WSPM\n",
       "5   4.360372       O3\n",
       "2   3.314309      SO2\n",
       "7   1.845062     DEWP\n",
       "8        NaN     RAIN"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop('TEMP',axis=1,inplace=True)\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "vif['VIF'] = [variance_inflation_factor(df2.values,i) for i in range(df2.shape[1])]\n",
    "\n",
    "vif['Features'] = df2.columns\n",
    "\n",
    "vif.sort_values(by = 'VIF',ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.630694</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.303688</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.543730</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.304169</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.755790</td>\n",
       "      <td>WSPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.397946</td>\n",
       "      <td>O3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.286193</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.844830</td>\n",
       "      <td>DEWP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RAIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         VIF Features\n",
       "1  11.630694     PM10\n",
       "0  11.303688    PM2.5\n",
       "4   8.543730       CO\n",
       "3   6.304169      NO2\n",
       "8   3.755790     WSPM\n",
       "5   3.397946       O3\n",
       "2   3.286193      SO2\n",
       "6   1.844830     DEWP\n",
       "7        NaN     RAIN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop('PRES',axis=1,inplace=True)\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "vif['VIF'] = [variance_inflation_factor(df2.values,i) for i in range(df2.shape[1])]\n",
    "\n",
    "vif['Features'] = df2.columns\n",
    "\n",
    "vif.sort_values(by = 'VIF',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.482692</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.718615</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.366883</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.754004</td>\n",
       "      <td>WSPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.305081</td>\n",
       "      <td>O3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.271765</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.843895</td>\n",
       "      <td>DEWP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RAIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF Features\n",
       "3  8.482692       CO\n",
       "0  5.718615    PM2.5\n",
       "2  5.366883      NO2\n",
       "7  3.754004     WSPM\n",
       "4  3.305081       O3\n",
       "1  3.271765      SO2\n",
       "5  1.843895     DEWP\n",
       "6       NaN     RAIN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop('PM10',axis=1,inplace=True)\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "vif['VIF'] = [variance_inflation_factor(df2.values,i) for i in range(df2.shape[1])]\n",
    "\n",
    "vif['Features'] = df2.columns\n",
    "\n",
    "vif.sort_values(by = 'VIF',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.278972</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.890282</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.695725</td>\n",
       "      <td>WSPM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.298083</td>\n",
       "      <td>O3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.094233</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.843393</td>\n",
       "      <td>DEWP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RAIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF Features\n",
       "2  4.278972      NO2\n",
       "0  3.890282    PM2.5\n",
       "6  3.695725     WSPM\n",
       "3  3.298083       O3\n",
       "1  3.094233      SO2\n",
       "4  1.843393     DEWP\n",
       "5       NaN     RAIN"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop('CO',axis=1,inplace=True)\n",
    "vif = pd.DataFrame()\n",
    "\n",
    "vif['VIF'] = [variance_inflation_factor(df2.values,i) for i in range(df2.shape[1])]\n",
    "\n",
    "vif['Features'] = df2.columns\n",
    "\n",
    "vif.sort_values(by = 'VIF',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    PM2.5\n",
       "1      SO2\n",
       "2      NO2\n",
       "3       O3\n",
       "4     DEWP\n",
       "5     RAIN\n",
       "6     WSPM\n",
       "Name: Features, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = vif.Features\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WE SHALL TRY TO BUILD A MODEL WITH THE ABOVE FEATURES WITH AND WITHOUT CATEGORICAL COLUMNS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SO2   NO2    O3  WSPM  DEWP  RAIN\n",
       "3  11.0  11.0  72.0   3.1 -19.4   0.0\n",
       "4  12.0  12.0  72.0   2.0 -19.5   0.0\n",
       "5  18.0  18.0  66.0   3.7 -19.6   0.0\n",
       "6  18.0  32.0  50.0   2.5 -19.1   0.0\n",
       "7  19.0  41.0  43.0   3.8 -19.1   0.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_train[['SO2','NO2','O3','WSPM','DEWP','RAIN']]\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const   SO2   NO2    O3  WSPM  DEWP  RAIN\n",
       "3    1.0  11.0  11.0  72.0   3.1 -19.4   0.0\n",
       "4    1.0  12.0  12.0  72.0   2.0 -19.5   0.0\n",
       "5    1.0  18.0  18.0  66.0   3.7 -19.6   0.0\n",
       "6    1.0  18.0  32.0  50.0   2.5 -19.1   0.0\n",
       "7    1.0  19.0  41.0  43.0   3.8 -19.1   0.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y = X['PM2.5']\n",
    "\n",
    "X.drop('RAIN',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205604, 6), (88116, 6), (205604,), (88116,))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3,random_state = 111)\n",
    "\n",
    "xtrain.shape,xtest.shape,ytrain.shape,ytest.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MODEL 2: WITH VIF AND WITHOUT CATEG COLUMNS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>PM2.5</td>      <th>  R-squared:         </th>  <td>   0.387</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.387</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>2.594e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Nov 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:58:58</td>     <th>  Log-Likelihood:    </th> <td>-1.0479e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>205604</td>      <th>  AIC:               </th>  <td>2.096e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>205598</td>      <th>  BIC:               </th>  <td>2.096e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    6.0065</td> <td>    0.320</td> <td>   18.789</td> <td> 0.000</td> <td>    5.380</td> <td>    6.633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO2</th>   <td>    1.5113</td> <td>    0.010</td> <td>  144.039</td> <td> 0.000</td> <td>    1.491</td> <td>    1.532</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NO2</th>   <td>    0.7034</td> <td>    0.004</td> <td>  164.120</td> <td> 0.000</td> <td>    0.695</td> <td>    0.712</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>O3</th>    <td>    0.0710</td> <td>    0.003</td> <td>   28.089</td> <td> 0.000</td> <td>    0.066</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WSPM</th>  <td>   -1.2922</td> <td>    0.112</td> <td>  -11.554</td> <td> 0.000</td> <td>   -1.511</td> <td>   -1.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DEWP</th>  <td>    1.2704</td> <td>    0.008</td> <td>  161.614</td> <td> 0.000</td> <td>    1.255</td> <td>    1.286</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>43183.024</td> <th>  Durbin-Watson:     </th> <td>   2.003</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>91555.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.233</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.147</td>   <th>  Cond. No.          </th> <td>    290.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  PM2.5   R-squared:                       0.387\n",
       "Model:                            OLS   Adj. R-squared:                  0.387\n",
       "Method:                 Least Squares   F-statistic:                 2.594e+04\n",
       "Date:                Mon, 08 Nov 2021   Prob (F-statistic):               0.00\n",
       "Time:                        21:58:58   Log-Likelihood:            -1.0479e+06\n",
       "No. Observations:              205604   AIC:                         2.096e+06\n",
       "Df Residuals:                  205598   BIC:                         2.096e+06\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          6.0065      0.320     18.789      0.000       5.380       6.633\n",
       "SO2            1.5113      0.010    144.039      0.000       1.491       1.532\n",
       "NO2            0.7034      0.004    164.120      0.000       0.695       0.712\n",
       "O3             0.0710      0.003     28.089      0.000       0.066       0.076\n",
       "WSPM          -1.2922      0.112    -11.554      0.000      -1.511      -1.073\n",
       "DEWP           1.2704      0.008    161.614      0.000       1.255       1.286\n",
       "==============================================================================\n",
       "Omnibus:                    43183.024   Durbin-Watson:                   2.003\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            91555.631\n",
       "Skew:                           1.233   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.147   Cond. No.                         290.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = sm.OLS(ytrain,xtrain).fit()\n",
    "\n",
    "m2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  40\n",
      "RMSE test:  39\n"
     ]
    }
   ],
   "source": [
    "ypred1 = m2.predict(xtrain)\n",
    "ypred2 = m2.predict(xtest)\n",
    "\n",
    "rmse1 = round(np.sqrt(mean_squared_error(ytrain,ypred1)),2)\n",
    "\n",
    "rmse2 = round(np.sqrt(mean_squared_error(ytest,ypred2)),2)\n",
    "\n",
    "print(\"RMSE train: \",round(rmse1))\n",
    "print(\"RMSE test: \",round(rmse2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.pvalues[m2.pvalues >0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814</td>\n",
       "      <td>21.79785</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387</td>\n",
       "      <td>39.56000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name Alpha L1_ratio     R2  RMSE_TRAIN  RMSE_TEST\n",
       "0             LinReg_without_VIF   NIL      NIL  0.814    21.79785  21.827985\n",
       "0  LinReg_with_VIF_without categ   NIL      NIL  0.387    39.56000  39.450000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1 = pd.DataFrame({'Model Name':['LinReg_with_VIF_without categ'],'Alpha':['NIL'],\n",
    "                      'L1_ratio':['NIL'],'R2':0.387,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results.append(score).append(score1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MODEL 2: WITH VIF AND WITH CATEG COLUMNS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train[['SO2','NO2','O3','WSPM','DEWP','PM2.5']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X['PM2.5']\n",
    "\n",
    "X.drop('PM2.5',inplace=True,axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>year_2014</th>\n",
       "      <th>year_2015</th>\n",
       "      <th>year_2016</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</th>\n",
       "      <th>month_4</th>\n",
       "      <th>month_5</th>\n",
       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "      <th>month_12</th>\n",
       "      <th>day_2</th>\n",
       "      <th>day_3</th>\n",
       "      <th>day_4</th>\n",
       "      <th>day_5</th>\n",
       "      <th>day_6</th>\n",
       "      <th>day_7</th>\n",
       "      <th>day_8</th>\n",
       "      <th>day_9</th>\n",
       "      <th>day_10</th>\n",
       "      <th>day_11</th>\n",
       "      <th>day_12</th>\n",
       "      <th>day_13</th>\n",
       "      <th>day_14</th>\n",
       "      <th>day_15</th>\n",
       "      <th>day_16</th>\n",
       "      <th>day_17</th>\n",
       "      <th>day_18</th>\n",
       "      <th>day_19</th>\n",
       "      <th>day_20</th>\n",
       "      <th>day_21</th>\n",
       "      <th>day_22</th>\n",
       "      <th>day_23</th>\n",
       "      <th>day_24</th>\n",
       "      <th>day_25</th>\n",
       "      <th>day_26</th>\n",
       "      <th>day_27</th>\n",
       "      <th>day_28</th>\n",
       "      <th>day_29</th>\n",
       "      <th>day_30</th>\n",
       "      <th>day_31</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
       "      <th>hour_7</th>\n",
       "      <th>hour_8</th>\n",
       "      <th>hour_9</th>\n",
       "      <th>hour_10</th>\n",
       "      <th>hour_11</th>\n",
       "      <th>hour_12</th>\n",
       "      <th>hour_13</th>\n",
       "      <th>hour_14</th>\n",
       "      <th>hour_15</th>\n",
       "      <th>hour_16</th>\n",
       "      <th>hour_17</th>\n",
       "      <th>hour_18</th>\n",
       "      <th>hour_19</th>\n",
       "      <th>hour_20</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>wd_ENE</th>\n",
       "      <th>wd_ESE</th>\n",
       "      <th>wd_N</th>\n",
       "      <th>wd_NE</th>\n",
       "      <th>wd_NNE</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "      <th>station_Changping</th>\n",
       "      <th>station_Dingling</th>\n",
       "      <th>station_Dongsi</th>\n",
       "      <th>station_Guanyuan</th>\n",
       "      <th>station_Gucheng</th>\n",
       "      <th>station_Huairou</th>\n",
       "      <th>station_Nongzhanguan</th>\n",
       "      <th>station_Shunyi</th>\n",
       "      <th>station_Tiantan</th>\n",
       "      <th>station_Wanliu</th>\n",
       "      <th>station_Wanshouxigong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const   SO2   NO2    O3  WSPM  DEWP  year_2014  year_2015  year_2016  \\\n",
       "3    1.0  11.0  11.0  72.0   3.1 -19.4          0          0          0   \n",
       "4    1.0  12.0  12.0  72.0   2.0 -19.5          0          0          0   \n",
       "5    1.0  18.0  18.0  66.0   3.7 -19.6          0          0          0   \n",
       "6    1.0  18.0  32.0  50.0   2.5 -19.1          0          0          0   \n",
       "7    1.0  19.0  41.0  43.0   3.8 -19.1          0          0          0   \n",
       "\n",
       "   month_2  month_3  month_4  month_5  month_6  month_7  month_8  month_9  \\\n",
       "3        0        1        0        0        0        0        0        0   \n",
       "4        0        1        0        0        0        0        0        0   \n",
       "5        0        1        0        0        0        0        0        0   \n",
       "6        0        1        0        0        0        0        0        0   \n",
       "7        0        1        0        0        0        0        0        0   \n",
       "\n",
       "   month_10  month_11  month_12  day_2  day_3  day_4  day_5  day_6  day_7  \\\n",
       "3         0         0         0      0      0      0      0      0      0   \n",
       "4         0         0         0      0      0      0      0      0      0   \n",
       "5         0         0         0      0      0      0      0      0      0   \n",
       "6         0         0         0      0      0      0      0      0      0   \n",
       "7         0         0         0      0      0      0      0      0      0   \n",
       "\n",
       "   day_8  day_9  day_10  day_11  day_12  day_13  day_14  day_15  day_16  \\\n",
       "3      0      0       0       0       0       0       0       0       0   \n",
       "4      0      0       0       0       0       0       0       0       0   \n",
       "5      0      0       0       0       0       0       0       0       0   \n",
       "6      0      0       0       0       0       0       0       0       0   \n",
       "7      0      0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_17  day_18  day_19  day_20  day_21  day_22  day_23  day_24  day_25  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_26  day_27  day_28  day_29  day_30  day_31  hour_1  hour_2  hour_3  \\\n",
       "3       0       0       0       0       0       0       0       0       1   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   hour_4  hour_5  hour_6  hour_7  hour_8  hour_9  hour_10  hour_11  hour_12  \\\n",
       "3       0       0       0       0       0       0        0        0        0   \n",
       "4       1       0       0       0       0       0        0        0        0   \n",
       "5       0       1       0       0       0       0        0        0        0   \n",
       "6       0       0       1       0       0       0        0        0        0   \n",
       "7       0       0       0       1       0       0        0        0        0   \n",
       "\n",
       "   hour_13  hour_14  hour_15  hour_16  hour_17  hour_18  hour_19  hour_20  \\\n",
       "3        0        0        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "6        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   hour_21  hour_22  hour_23  wd_ENE  wd_ESE  wd_N  wd_NE  wd_NNE  wd_NNW  \\\n",
       "3        0        0        0       0       0     0      0       0       0   \n",
       "4        0        0        0       0       0     1      0       0       0   \n",
       "5        0        0        0       0       0     1      0       0       0   \n",
       "6        0        0        0       0       0     0      0       1       0   \n",
       "7        0        0        0       0       0     0      0       0       1   \n",
       "\n",
       "   wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW  wd_SW  wd_W  wd_WNW  wd_WSW  \\\n",
       "3      1     0      0       0       0      0     0       0       0   \n",
       "4      0     0      0       0       0      0     0       0       0   \n",
       "5      0     0      0       0       0      0     0       0       0   \n",
       "6      0     0      0       0       0      0     0       0       0   \n",
       "7      0     0      0       0       0      0     0       0       0   \n",
       "\n",
       "   station_Changping  station_Dingling  station_Dongsi  station_Guanyuan  \\\n",
       "3                  0                 0               0                 0   \n",
       "4                  0                 0               0                 0   \n",
       "5                  0                 0               0                 0   \n",
       "6                  0                 0               0                 0   \n",
       "7                  0                 0               0                 0   \n",
       "\n",
       "   station_Gucheng  station_Huairou  station_Nongzhanguan  station_Shunyi  \\\n",
       "3                0                0                     0               0   \n",
       "4                0                0                     0               0   \n",
       "5                0                0                     0               0   \n",
       "6                0                0                     0               0   \n",
       "7                0                0                     0               0   \n",
       "\n",
       "   station_Tiantan  station_Wanliu  station_Wanshouxigong  \n",
       "3                0               0                      0  \n",
       "4                0               0                      0  \n",
       "5                0               0                      0  \n",
       "6                0               0                      0  \n",
       "7                0               0                      0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X)\n",
    "X = pd.concat([X,df1_cat_encode],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205604, 99), (88116, 99), (205604,), (88116,))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3,random_state = 111)\n",
    "\n",
    "xtrain.shape,xtest.shape,ytrain.shape,ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>PM2.5</td>      <th>  R-squared:         </th>  <td>   0.520</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.519</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   2268.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Nov 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:59:42</td>     <th>  Log-Likelihood:    </th> <td>-1.0228e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>205604</td>      <th>  AIC:               </th>  <td>2.046e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>205505</td>      <th>  BIC:               </th>  <td>2.047e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    98</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                 <td>   45.2179</td> <td>    0.899</td> <td>   50.288</td> <td> 0.000</td> <td>   43.456</td> <td>   46.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO2</th>                   <td>    1.0063</td> <td>    0.010</td> <td>   95.919</td> <td> 0.000</td> <td>    0.986</td> <td>    1.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NO2</th>                   <td>    0.7421</td> <td>    0.004</td> <td>  168.545</td> <td> 0.000</td> <td>    0.733</td> <td>    0.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>O3</th>                    <td>    0.1871</td> <td>    0.003</td> <td>   66.664</td> <td> 0.000</td> <td>    0.182</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WSPM</th>                  <td>    1.3392</td> <td>    0.107</td> <td>   12.524</td> <td> 0.000</td> <td>    1.130</td> <td>    1.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DEWP</th>                  <td>    3.6837</td> <td>    0.015</td> <td>  247.086</td> <td> 0.000</td> <td>    3.654</td> <td>    3.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year_2014</th>             <td>   -0.3362</td> <td>    0.231</td> <td>   -1.454</td> <td> 0.146</td> <td>   -0.789</td> <td>    0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year_2015</th>             <td>    2.5189</td> <td>    0.235</td> <td>   10.707</td> <td> 0.000</td> <td>    2.058</td> <td>    2.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>year_2016</th>             <td>    3.4637</td> <td>    0.235</td> <td>   14.744</td> <td> 0.000</td> <td>    3.003</td> <td>    3.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_2</th>               <td>   -1.8963</td> <td>    0.488</td> <td>   -3.883</td> <td> 0.000</td> <td>   -2.853</td> <td>   -0.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_3</th>               <td>  -15.8617</td> <td>    0.459</td> <td>  -34.586</td> <td> 0.000</td> <td>  -16.761</td> <td>  -14.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_4</th>               <td>  -41.2687</td> <td>    0.512</td> <td>  -80.650</td> <td> 0.000</td> <td>  -42.272</td> <td>  -40.266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_5</th>               <td>  -76.0044</td> <td>    0.571</td> <td> -133.157</td> <td> 0.000</td> <td>  -77.123</td> <td>  -74.886</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_6</th>               <td>  -95.2328</td> <td>    0.668</td> <td> -142.515</td> <td> 0.000</td> <td>  -96.543</td> <td>  -93.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_7</th>               <td> -101.4158</td> <td>    0.716</td> <td> -141.621</td> <td> 0.000</td> <td> -102.819</td> <td> -100.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_8</th>               <td> -113.1462</td> <td>    0.704</td> <td> -160.654</td> <td> 0.000</td> <td> -114.527</td> <td> -111.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_9</th>               <td>  -90.1668</td> <td>    0.638</td> <td> -141.391</td> <td> 0.000</td> <td>  -91.417</td> <td>  -88.917</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_10</th>              <td>  -51.4664</td> <td>    0.549</td> <td>  -93.766</td> <td> 0.000</td> <td>  -52.542</td> <td>  -50.391</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_11</th>              <td>  -19.9649</td> <td>    0.478</td> <td>  -41.763</td> <td> 0.000</td> <td>  -20.902</td> <td>  -19.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>month_12</th>              <td>   -8.3699</td> <td>    0.453</td> <td>  -18.462</td> <td> 0.000</td> <td>   -9.258</td> <td>   -7.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_2</th>                 <td>   -8.0388</td> <td>    0.603</td> <td>  -13.329</td> <td> 0.000</td> <td>   -9.221</td> <td>   -6.857</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_3</th>                 <td>   -6.2956</td> <td>    0.606</td> <td>  -10.384</td> <td> 0.000</td> <td>   -7.484</td> <td>   -5.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_4</th>                 <td>   -3.9020</td> <td>    0.609</td> <td>   -6.408</td> <td> 0.000</td> <td>   -5.096</td> <td>   -2.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_5</th>                 <td>   -2.8970</td> <td>    0.611</td> <td>   -4.744</td> <td> 0.000</td> <td>   -4.094</td> <td>   -1.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_6</th>                 <td>    2.1082</td> <td>    0.608</td> <td>    3.469</td> <td> 0.001</td> <td>    0.917</td> <td>    3.299</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_7</th>                 <td>    0.9715</td> <td>    0.609</td> <td>    1.596</td> <td> 0.110</td> <td>   -0.221</td> <td>    2.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_8</th>                 <td>   -2.0068</td> <td>    0.604</td> <td>   -3.323</td> <td> 0.001</td> <td>   -3.190</td> <td>   -0.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_9</th>                 <td>   -4.7319</td> <td>    0.599</td> <td>   -7.904</td> <td> 0.000</td> <td>   -5.905</td> <td>   -3.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_10</th>                <td>   -5.6735</td> <td>    0.601</td> <td>   -9.446</td> <td> 0.000</td> <td>   -6.851</td> <td>   -4.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_11</th>                <td>   -6.9046</td> <td>    0.592</td> <td>  -11.657</td> <td> 0.000</td> <td>   -8.066</td> <td>   -5.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_12</th>                <td>   -9.1694</td> <td>    0.588</td> <td>  -15.585</td> <td> 0.000</td> <td>  -10.323</td> <td>   -8.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_13</th>                <td>   -7.6606</td> <td>    0.600</td> <td>  -12.759</td> <td> 0.000</td> <td>   -8.837</td> <td>   -6.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_14</th>                <td>   -8.0105</td> <td>    0.606</td> <td>  -13.224</td> <td> 0.000</td> <td>   -9.198</td> <td>   -6.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_15</th>                <td>   -8.1424</td> <td>    0.618</td> <td>  -13.180</td> <td> 0.000</td> <td>   -9.353</td> <td>   -6.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_16</th>                <td>   -4.4256</td> <td>    0.620</td> <td>   -7.137</td> <td> 0.000</td> <td>   -5.641</td> <td>   -3.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_17</th>                <td>   -6.8716</td> <td>    0.601</td> <td>  -11.438</td> <td> 0.000</td> <td>   -8.049</td> <td>   -5.694</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_18</th>                <td>   -2.7111</td> <td>    0.600</td> <td>   -4.519</td> <td> 0.000</td> <td>   -3.887</td> <td>   -1.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_19</th>                <td>   -4.7421</td> <td>    0.601</td> <td>   -7.891</td> <td> 0.000</td> <td>   -5.920</td> <td>   -3.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_20</th>                <td>   -4.0303</td> <td>    0.605</td> <td>   -6.665</td> <td> 0.000</td> <td>   -5.216</td> <td>   -2.845</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_21</th>                <td>   -4.6943</td> <td>    0.600</td> <td>   -7.829</td> <td> 0.000</td> <td>   -5.870</td> <td>   -3.519</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_22</th>                <td>   -6.7508</td> <td>    0.606</td> <td>  -11.137</td> <td> 0.000</td> <td>   -7.939</td> <td>   -5.563</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_23</th>                <td>   -7.0663</td> <td>    0.601</td> <td>  -11.754</td> <td> 0.000</td> <td>   -8.245</td> <td>   -5.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_24</th>                <td>   -6.4720</td> <td>    0.591</td> <td>  -10.952</td> <td> 0.000</td> <td>   -7.630</td> <td>   -5.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_25</th>                <td>   -2.1720</td> <td>    0.597</td> <td>   -3.637</td> <td> 0.000</td> <td>   -3.343</td> <td>   -1.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_26</th>                <td>   -4.9068</td> <td>    0.606</td> <td>   -8.095</td> <td> 0.000</td> <td>   -6.095</td> <td>   -3.719</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_27</th>                <td>  -10.8385</td> <td>    0.596</td> <td>  -18.197</td> <td> 0.000</td> <td>  -12.006</td> <td>   -9.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_28</th>                <td>   -6.6295</td> <td>    0.597</td> <td>  -11.107</td> <td> 0.000</td> <td>   -7.799</td> <td>   -5.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_29</th>                <td>   -4.2761</td> <td>    0.604</td> <td>   -7.085</td> <td> 0.000</td> <td>   -5.459</td> <td>   -3.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_30</th>                <td>   -2.6116</td> <td>    0.612</td> <td>   -4.270</td> <td> 0.000</td> <td>   -3.810</td> <td>   -1.413</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>day_31</th>                <td>    2.0954</td> <td>    0.704</td> <td>    2.974</td> <td> 0.003</td> <td>    0.715</td> <td>    3.476</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_1</th>                <td>    1.1557</td> <td>    0.518</td> <td>    2.233</td> <td> 0.026</td> <td>    0.141</td> <td>    2.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_2</th>                <td>    2.8090</td> <td>    0.517</td> <td>    5.431</td> <td> 0.000</td> <td>    1.795</td> <td>    3.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_3</th>                <td>    4.0540</td> <td>    0.517</td> <td>    7.840</td> <td> 0.000</td> <td>    3.041</td> <td>    5.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_4</th>                <td>    4.9418</td> <td>    0.518</td> <td>    9.549</td> <td> 0.000</td> <td>    3.927</td> <td>    5.956</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_5</th>                <td>    4.5840</td> <td>    0.518</td> <td>    8.857</td> <td> 0.000</td> <td>    3.570</td> <td>    5.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_6</th>                <td>    2.9217</td> <td>    0.516</td> <td>    5.661</td> <td> 0.000</td> <td>    1.910</td> <td>    3.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_7</th>                <td>    1.7424</td> <td>    0.517</td> <td>    3.370</td> <td> 0.001</td> <td>    0.729</td> <td>    2.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_8</th>                <td>    0.1880</td> <td>    0.521</td> <td>    0.361</td> <td> 0.718</td> <td>   -0.832</td> <td>    1.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_9</th>                <td>    0.1368</td> <td>    0.527</td> <td>    0.260</td> <td> 0.795</td> <td>   -0.896</td> <td>    1.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_10</th>               <td>    0.5706</td> <td>    0.532</td> <td>    1.072</td> <td> 0.284</td> <td>   -0.472</td> <td>    1.614</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_11</th>               <td>    0.9477</td> <td>    0.537</td> <td>    1.766</td> <td> 0.077</td> <td>   -0.104</td> <td>    2.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_12</th>               <td>    0.4881</td> <td>    0.547</td> <td>    0.892</td> <td> 0.372</td> <td>   -0.584</td> <td>    1.561</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_13</th>               <td>    0.8238</td> <td>    0.559</td> <td>    1.473</td> <td> 0.141</td> <td>   -0.272</td> <td>    1.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_14</th>               <td>    0.0183</td> <td>    0.569</td> <td>    0.032</td> <td> 0.974</td> <td>   -1.097</td> <td>    1.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_15</th>               <td>   -0.4920</td> <td>    0.573</td> <td>   -0.858</td> <td> 0.391</td> <td>   -1.616</td> <td>    0.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_16</th>               <td>   -1.9674</td> <td>    0.570</td> <td>   -3.453</td> <td> 0.001</td> <td>   -3.084</td> <td>   -0.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_17</th>               <td>   -3.4951</td> <td>    0.562</td> <td>   -6.215</td> <td> 0.000</td> <td>   -4.597</td> <td>   -2.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_18</th>               <td>   -4.1711</td> <td>    0.552</td> <td>   -7.563</td> <td> 0.000</td> <td>   -5.252</td> <td>   -3.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_19</th>               <td>   -5.5325</td> <td>    0.540</td> <td>  -10.241</td> <td> 0.000</td> <td>   -6.591</td> <td>   -4.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_20</th>               <td>   -4.9364</td> <td>    0.532</td> <td>   -9.272</td> <td> 0.000</td> <td>   -5.980</td> <td>   -3.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_21</th>               <td>   -3.2493</td> <td>    0.526</td> <td>   -6.174</td> <td> 0.000</td> <td>   -4.281</td> <td>   -2.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_22</th>               <td>   -2.6162</td> <td>    0.523</td> <td>   -5.003</td> <td> 0.000</td> <td>   -3.641</td> <td>   -1.591</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hour_23</th>               <td>   -1.5848</td> <td>    0.521</td> <td>   -3.045</td> <td> 0.002</td> <td>   -2.605</td> <td>   -0.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_ENE</th>                <td>   -2.7523</td> <td>    0.396</td> <td>   -6.947</td> <td> 0.000</td> <td>   -3.529</td> <td>   -1.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_ESE</th>                <td>    1.1199</td> <td>    0.433</td> <td>    2.589</td> <td> 0.010</td> <td>    0.272</td> <td>    1.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_N</th>                  <td>   -1.3429</td> <td>    0.404</td> <td>   -3.323</td> <td> 0.001</td> <td>   -2.135</td> <td>   -0.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NE</th>                 <td>   -2.8290</td> <td>    0.377</td> <td>   -7.507</td> <td> 0.000</td> <td>   -3.568</td> <td>   -2.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NNE</th>                <td>   -3.6859</td> <td>    0.412</td> <td>   -8.947</td> <td> 0.000</td> <td>   -4.493</td> <td>   -2.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NNW</th>                <td>    0.3629</td> <td>    0.433</td> <td>    0.837</td> <td> 0.402</td> <td>   -0.487</td> <td>    1.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_NW</th>                 <td>   -0.8537</td> <td>    0.417</td> <td>   -2.047</td> <td> 0.041</td> <td>   -1.671</td> <td>   -0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_S</th>                  <td>   -1.9094</td> <td>    0.470</td> <td>   -4.064</td> <td> 0.000</td> <td>   -2.830</td> <td>   -0.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SE</th>                 <td>    0.7535</td> <td>    0.464</td> <td>    1.625</td> <td> 0.104</td> <td>   -0.155</td> <td>    1.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SSE</th>                <td>   -0.1411</td> <td>    0.481</td> <td>   -0.293</td> <td> 0.769</td> <td>   -1.084</td> <td>    0.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SSW</th>                <td>   -3.5335</td> <td>    0.447</td> <td>   -7.902</td> <td> 0.000</td> <td>   -4.410</td> <td>   -2.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_SW</th>                 <td>   -4.5935</td> <td>    0.426</td> <td>  -10.777</td> <td> 0.000</td> <td>   -5.429</td> <td>   -3.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_W</th>                  <td>   -4.9817</td> <td>    0.473</td> <td>  -10.526</td> <td> 0.000</td> <td>   -5.909</td> <td>   -4.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_WNW</th>                <td>   -2.4215</td> <td>    0.453</td> <td>   -5.344</td> <td> 0.000</td> <td>   -3.310</td> <td>   -1.533</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wd_WSW</th>                <td>   -5.3339</td> <td>    0.453</td> <td>  -11.787</td> <td> 0.000</td> <td>   -6.221</td> <td>   -4.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Changping</th>     <td>    9.2371</td> <td>    0.389</td> <td>   23.767</td> <td> 0.000</td> <td>    8.475</td> <td>    9.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Dingling</th>      <td>   18.2200</td> <td>    0.394</td> <td>   46.284</td> <td> 0.000</td> <td>   17.448</td> <td>   18.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Dongsi</th>        <td>    5.9363</td> <td>    0.386</td> <td>   15.399</td> <td> 0.000</td> <td>    5.181</td> <td>    6.692</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Guanyuan</th>      <td>    2.3160</td> <td>    0.381</td> <td>    6.072</td> <td> 0.000</td> <td>    1.568</td> <td>    3.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Gucheng</th>       <td>    7.4956</td> <td>    0.384</td> <td>   19.510</td> <td> 0.000</td> <td>    6.743</td> <td>    8.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Huairou</th>       <td>   18.1776</td> <td>    0.390</td> <td>   46.560</td> <td> 0.000</td> <td>   17.412</td> <td>   18.943</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Nongzhanguan</th>  <td>    1.7387</td> <td>    0.388</td> <td>    4.483</td> <td> 0.000</td> <td>    0.979</td> <td>    2.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Shunyi</th>        <td>   14.2361</td> <td>    0.385</td> <td>   36.961</td> <td> 0.000</td> <td>   13.481</td> <td>   14.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Tiantan</th>       <td>    8.5590</td> <td>    0.383</td> <td>   22.328</td> <td> 0.000</td> <td>    7.808</td> <td>    9.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Wanliu</th>        <td>   -2.3435</td> <td>    0.383</td> <td>   -6.122</td> <td> 0.000</td> <td>   -3.094</td> <td>   -1.593</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>station_Wanshouxigong</th> <td>    5.8857</td> <td>    0.384</td> <td>   15.328</td> <td> 0.000</td> <td>    5.133</td> <td>    6.638</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>37697.120</td> <th>  Durbin-Watson:     </th> <td>   2.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>75286.714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.111</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 4.962</td>   <th>  Cond. No.          </th> <td>2.43e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.43e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  PM2.5   R-squared:                       0.520\n",
       "Model:                            OLS   Adj. R-squared:                  0.519\n",
       "Method:                 Least Squares   F-statistic:                     2268.\n",
       "Date:                Mon, 08 Nov 2021   Prob (F-statistic):               0.00\n",
       "Time:                        21:59:42   Log-Likelihood:            -1.0228e+06\n",
       "No. Observations:              205604   AIC:                         2.046e+06\n",
       "Df Residuals:                  205505   BIC:                         2.047e+06\n",
       "Df Model:                          98                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "const                    45.2179      0.899     50.288      0.000      43.456      46.980\n",
       "SO2                       1.0063      0.010     95.919      0.000       0.986       1.027\n",
       "NO2                       0.7421      0.004    168.545      0.000       0.733       0.751\n",
       "O3                        0.1871      0.003     66.664      0.000       0.182       0.193\n",
       "WSPM                      1.3392      0.107     12.524      0.000       1.130       1.549\n",
       "DEWP                      3.6837      0.015    247.086      0.000       3.654       3.713\n",
       "year_2014                -0.3362      0.231     -1.454      0.146      -0.789       0.117\n",
       "year_2015                 2.5189      0.235     10.707      0.000       2.058       2.980\n",
       "year_2016                 3.4637      0.235     14.744      0.000       3.003       3.924\n",
       "month_2                  -1.8963      0.488     -3.883      0.000      -2.853      -0.939\n",
       "month_3                 -15.8617      0.459    -34.586      0.000     -16.761     -14.963\n",
       "month_4                 -41.2687      0.512    -80.650      0.000     -42.272     -40.266\n",
       "month_5                 -76.0044      0.571   -133.157      0.000     -77.123     -74.886\n",
       "month_6                 -95.2328      0.668   -142.515      0.000     -96.543     -93.923\n",
       "month_7                -101.4158      0.716   -141.621      0.000    -102.819    -100.012\n",
       "month_8                -113.1462      0.704   -160.654      0.000    -114.527    -111.766\n",
       "month_9                 -90.1668      0.638   -141.391      0.000     -91.417     -88.917\n",
       "month_10                -51.4664      0.549    -93.766      0.000     -52.542     -50.391\n",
       "month_11                -19.9649      0.478    -41.763      0.000     -20.902     -19.028\n",
       "month_12                 -8.3699      0.453    -18.462      0.000      -9.258      -7.481\n",
       "day_2                    -8.0388      0.603    -13.329      0.000      -9.221      -6.857\n",
       "day_3                    -6.2956      0.606    -10.384      0.000      -7.484      -5.107\n",
       "day_4                    -3.9020      0.609     -6.408      0.000      -5.096      -2.708\n",
       "day_5                    -2.8970      0.611     -4.744      0.000      -4.094      -1.700\n",
       "day_6                     2.1082      0.608      3.469      0.001       0.917       3.299\n",
       "day_7                     0.9715      0.609      1.596      0.110      -0.221       2.164\n",
       "day_8                    -2.0068      0.604     -3.323      0.001      -3.190      -0.823\n",
       "day_9                    -4.7319      0.599     -7.904      0.000      -5.905      -3.559\n",
       "day_10                   -5.6735      0.601     -9.446      0.000      -6.851      -4.496\n",
       "day_11                   -6.9046      0.592    -11.657      0.000      -8.066      -5.744\n",
       "day_12                   -9.1694      0.588    -15.585      0.000     -10.323      -8.016\n",
       "day_13                   -7.6606      0.600    -12.759      0.000      -8.837      -6.484\n",
       "day_14                   -8.0105      0.606    -13.224      0.000      -9.198      -6.823\n",
       "day_15                   -8.1424      0.618    -13.180      0.000      -9.353      -6.932\n",
       "day_16                   -4.4256      0.620     -7.137      0.000      -5.641      -3.210\n",
       "day_17                   -6.8716      0.601    -11.438      0.000      -8.049      -5.694\n",
       "day_18                   -2.7111      0.600     -4.519      0.000      -3.887      -1.535\n",
       "day_19                   -4.7421      0.601     -7.891      0.000      -5.920      -3.564\n",
       "day_20                   -4.0303      0.605     -6.665      0.000      -5.216      -2.845\n",
       "day_21                   -4.6943      0.600     -7.829      0.000      -5.870      -3.519\n",
       "day_22                   -6.7508      0.606    -11.137      0.000      -7.939      -5.563\n",
       "day_23                   -7.0663      0.601    -11.754      0.000      -8.245      -5.888\n",
       "day_24                   -6.4720      0.591    -10.952      0.000      -7.630      -5.314\n",
       "day_25                   -2.1720      0.597     -3.637      0.000      -3.343      -1.001\n",
       "day_26                   -4.9068      0.606     -8.095      0.000      -6.095      -3.719\n",
       "day_27                  -10.8385      0.596    -18.197      0.000     -12.006      -9.671\n",
       "day_28                   -6.6295      0.597    -11.107      0.000      -7.799      -5.460\n",
       "day_29                   -4.2761      0.604     -7.085      0.000      -5.459      -3.093\n",
       "day_30                   -2.6116      0.612     -4.270      0.000      -3.810      -1.413\n",
       "day_31                    2.0954      0.704      2.974      0.003       0.715       3.476\n",
       "hour_1                    1.1557      0.518      2.233      0.026       0.141       2.170\n",
       "hour_2                    2.8090      0.517      5.431      0.000       1.795       3.823\n",
       "hour_3                    4.0540      0.517      7.840      0.000       3.041       5.068\n",
       "hour_4                    4.9418      0.518      9.549      0.000       3.927       5.956\n",
       "hour_5                    4.5840      0.518      8.857      0.000       3.570       5.598\n",
       "hour_6                    2.9217      0.516      5.661      0.000       1.910       3.933\n",
       "hour_7                    1.7424      0.517      3.370      0.001       0.729       2.756\n",
       "hour_8                    0.1880      0.521      0.361      0.718      -0.832       1.208\n",
       "hour_9                    0.1368      0.527      0.260      0.795      -0.896       1.170\n",
       "hour_10                   0.5706      0.532      1.072      0.284      -0.472       1.614\n",
       "hour_11                   0.9477      0.537      1.766      0.077      -0.104       2.000\n",
       "hour_12                   0.4881      0.547      0.892      0.372      -0.584       1.561\n",
       "hour_13                   0.8238      0.559      1.473      0.141      -0.272       1.920\n",
       "hour_14                   0.0183      0.569      0.032      0.974      -1.097       1.134\n",
       "hour_15                  -0.4920      0.573     -0.858      0.391      -1.616       0.632\n",
       "hour_16                  -1.9674      0.570     -3.453      0.001      -3.084      -0.851\n",
       "hour_17                  -3.4951      0.562     -6.215      0.000      -4.597      -2.393\n",
       "hour_18                  -4.1711      0.552     -7.563      0.000      -5.252      -3.090\n",
       "hour_19                  -5.5325      0.540    -10.241      0.000      -6.591      -4.474\n",
       "hour_20                  -4.9364      0.532     -9.272      0.000      -5.980      -3.893\n",
       "hour_21                  -3.2493      0.526     -6.174      0.000      -4.281      -2.218\n",
       "hour_22                  -2.6162      0.523     -5.003      0.000      -3.641      -1.591\n",
       "hour_23                  -1.5848      0.521     -3.045      0.002      -2.605      -0.565\n",
       "wd_ENE                   -2.7523      0.396     -6.947      0.000      -3.529      -1.976\n",
       "wd_ESE                    1.1199      0.433      2.589      0.010       0.272       1.968\n",
       "wd_N                     -1.3429      0.404     -3.323      0.001      -2.135      -0.551\n",
       "wd_NE                    -2.8290      0.377     -7.507      0.000      -3.568      -2.090\n",
       "wd_NNE                   -3.6859      0.412     -8.947      0.000      -4.493      -2.878\n",
       "wd_NNW                    0.3629      0.433      0.837      0.402      -0.487       1.212\n",
       "wd_NW                    -0.8537      0.417     -2.047      0.041      -1.671      -0.036\n",
       "wd_S                     -1.9094      0.470     -4.064      0.000      -2.830      -0.989\n",
       "wd_SE                     0.7535      0.464      1.625      0.104      -0.155       1.662\n",
       "wd_SSE                   -0.1411      0.481     -0.293      0.769      -1.084       0.802\n",
       "wd_SSW                   -3.5335      0.447     -7.902      0.000      -4.410      -2.657\n",
       "wd_SW                    -4.5935      0.426    -10.777      0.000      -5.429      -3.758\n",
       "wd_W                     -4.9817      0.473    -10.526      0.000      -5.909      -4.054\n",
       "wd_WNW                   -2.4215      0.453     -5.344      0.000      -3.310      -1.533\n",
       "wd_WSW                   -5.3339      0.453    -11.787      0.000      -6.221      -4.447\n",
       "station_Changping         9.2371      0.389     23.767      0.000       8.475       9.999\n",
       "station_Dingling         18.2200      0.394     46.284      0.000      17.448      18.992\n",
       "station_Dongsi            5.9363      0.386     15.399      0.000       5.181       6.692\n",
       "station_Guanyuan          2.3160      0.381      6.072      0.000       1.568       3.064\n",
       "station_Gucheng           7.4956      0.384     19.510      0.000       6.743       8.249\n",
       "station_Huairou          18.1776      0.390     46.560      0.000      17.412      18.943\n",
       "station_Nongzhanguan      1.7387      0.388      4.483      0.000       0.979       2.499\n",
       "station_Shunyi           14.2361      0.385     36.961      0.000      13.481      14.991\n",
       "station_Tiantan           8.5590      0.383     22.328      0.000       7.808       9.310\n",
       "station_Wanliu           -2.3435      0.383     -6.122      0.000      -3.094      -1.593\n",
       "station_Wanshouxigong     5.8857      0.384     15.328      0.000       5.133       6.638\n",
       "==============================================================================\n",
       "Omnibus:                    37697.120   Durbin-Watson:                   2.000\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            75286.714\n",
       "Skew:                           1.111   Prob(JB):                         0.00\n",
       "Kurtosis:                       4.962   Cond. No.                     2.43e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.43e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3 = sm.OLS(ytrain,xtrain).fit()\n",
    "\n",
    "m3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  35.01\n",
      "RMSE test:  34.88\n"
     ]
    }
   ],
   "source": [
    "ypred1 = m3.predict(xtrain)\n",
    "ypred2 = m3.predict(xtest)\n",
    "\n",
    "rmse1 = round(np.sqrt(mean_squared_error(ytrain,ypred1)),2)\n",
    "\n",
    "rmse2 = round(np.sqrt(mean_squared_error(ytest,ypred2)),2)\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814</td>\n",
       "      <td>21.79785</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387</td>\n",
       "      <td>39.56000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name Alpha L1_ratio     R2  RMSE_TRAIN  RMSE_TEST\n",
       "0             LinReg_without_VIF   NIL      NIL  0.814    21.79785  21.827985\n",
       "0  LinReg_with_VIF_without categ   NIL      NIL  0.387    39.56000  39.450000\n",
       "0     LinReg_with_VIF_with_categ   NIL      NIL  0.520    35.01000  34.880000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score2 = pd.DataFrame({'Model Name':['LinReg_with_VIF_with_categ'],'Alpha':['NIL'],\n",
    "                      'L1_ratio':['NIL'],'R2':0.52,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results.append(score).append(score1).append(score2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RIDGE REGRESSION:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2:  0.5196292511054439\n",
      "RMSE train:  35.01\n",
      "RMSE test:  34.88\n"
     ]
    }
   ],
   "source": [
    "### RIDGE REGRESSION\n",
    "ridge = Ridge()\n",
    "\n",
    "m4 = ridge.fit(xtrain,ytrain)\n",
    "\n",
    "r2 = m4.score(xtrain,ytrain)\n",
    "print(\"R2: \",r2)\n",
    "\n",
    "ypred1 = m4.predict(xtrain)\n",
    "ypred2 = m4.predict(xtest)\n",
    "\n",
    "rmse1 = round(np.sqrt(mean_squared_error(ytrain,ypred1)),2)\n",
    "\n",
    "rmse2 = round(np.sqrt(mean_squared_error(ytest,ypred2)),2)\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.79785</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.56000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0             LinReg_without_VIF   NIL      NIL  0.814000    21.79785   \n",
       "0  LinReg_with_VIF_without categ   NIL      NIL  0.387000    39.56000   \n",
       "0     LinReg_with_VIF_with_categ   NIL      NIL  0.520000    35.01000   \n",
       "0                          Ridge     1      NIL  0.519629    35.01000   \n",
       "\n",
       "   RMSE_TEST  \n",
       "0  21.827985  \n",
       "0  39.450000  \n",
       "0  34.880000  \n",
       "0  34.880000  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score3 = pd.DataFrame({'Model Name':['Ridge'],'Alpha':[1],\n",
    "                      'L1_ratio':['NIL'],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results.append(score).append(score1).append(score2).append(score3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2:  0.5196292511054439\n",
      "RMSE train:  35.01\n",
      "RMSE test:  34.88\n"
     ]
    }
   ],
   "source": [
    "### LASSO REGRESSION\n",
    "lasso = Lasso()\n",
    "\n",
    "m5 = ridge.fit(xtrain,ytrain)\n",
    "\n",
    "r2 = m5.score(xtrain,ytrain)\n",
    "print(\"R2: \",r2)\n",
    "\n",
    "ypred1 = m5.predict(xtrain)\n",
    "ypred2 = m5.predict(xtest)\n",
    "\n",
    "rmse1 = round(np.sqrt(mean_squared_error(ytrain,ypred1)),2)\n",
    "\n",
    "rmse2 = round(np.sqrt(mean_squared_error(ytest,ypred2)),2)\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score4 = pd.DataFrame({'Model Name':['Lasso'],'Alpha':[1],\n",
    "                      'L1_ratio':['NIL'],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "\n",
    "results = results.append(score).append(score1).append(score2).append(score3).append(score4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.79785</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.56000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0             LinReg_without_VIF   NIL      NIL  0.814000    21.79785   \n",
       "0  LinReg_with_VIF_without categ   NIL      NIL  0.387000    39.56000   \n",
       "0     LinReg_with_VIF_with_categ   NIL      NIL  0.520000    35.01000   \n",
       "0                          Ridge     1      NIL  0.519629    35.01000   \n",
       "0                          Lasso     1      NIL  0.519629    35.01000   \n",
       "\n",
       "   RMSE_TEST  \n",
       "0  21.827985  \n",
       "0  39.450000  \n",
       "0  34.880000  \n",
       "0  34.880000  \n",
       "0  34.880000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2:  0.5196292511054439\n",
      "RMSE train:  35.01\n",
      "RMSE test:  34.88\n"
     ]
    }
   ],
   "source": [
    "### ELASTIC NET:\n",
    "\n",
    "enet = ElasticNet()\n",
    "m6 = ridge.fit(xtrain,ytrain)\n",
    "\n",
    "r2 = m6.score(xtrain,ytrain)\n",
    "print(\"R2: \",r2)\n",
    "\n",
    "ypred1 = m6.predict(xtrain)\n",
    "ypred2 = m6.predict(xtest)\n",
    "\n",
    "rmse1 = round(np.sqrt(mean_squared_error(ytrain,ypred1)),2)\n",
    "\n",
    "rmse2 = round(np.sqrt(mean_squared_error(ytest,ypred2)),2)\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score5 = pd.DataFrame({'Model Name':['Elastic_Net'],'Alpha':[1],\n",
    "                      'L1_ratio':[0.5],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>8.140000e-01</td>\n",
       "      <td>2.179785e+01</td>\n",
       "      <td>2.182798e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>3.870000e-01</td>\n",
       "      <td>3.956000e+01</td>\n",
       "      <td>3.945000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>5.200000e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>5.196293e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>5.196293e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.196293e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SGD Regressor</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-1.565148e+15</td>\n",
       "      <td>1.998379e+09</td>\n",
       "      <td>1.992694e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SGD Regressor</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-8.998737e+11</td>\n",
       "      <td>4.791715e+07</td>\n",
       "      <td>4.782514e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name   Alpha L1_ratio            R2    RMSE_TRAIN  \\\n",
       "0             LinReg_without_VIF     NIL      NIL  8.140000e-01  2.179785e+01   \n",
       "0  LinReg_with_VIF_without categ     NIL      NIL  3.870000e-01  3.956000e+01   \n",
       "0     LinReg_with_VIF_with_categ     NIL      NIL  5.200000e-01  3.501000e+01   \n",
       "0                          Ridge       1      NIL  5.196293e-01  3.501000e+01   \n",
       "0                          Lasso       1      NIL  5.196293e-01  3.501000e+01   \n",
       "0                    Elastic_Net       1      0.5  5.196293e-01  3.501000e+01   \n",
       "0                  SGD Regressor  0.0001     0.15 -1.565148e+15  1.998379e+09   \n",
       "0                  SGD Regressor  0.0001     0.15 -8.998737e+11  4.791715e+07   \n",
       "\n",
       "      RMSE_TEST  \n",
       "0  2.182798e+01  \n",
       "0  3.945000e+01  \n",
       "0  3.488000e+01  \n",
       "0  3.488000e+01  \n",
       "0  3.488000e+01  \n",
       "0  3.488000e+01  \n",
       "0  1.992694e+09  \n",
       "0  4.782514e+07  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.index = [0,1,2,3,4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>8.140000e-01</td>\n",
       "      <td>2.179785e+01</td>\n",
       "      <td>2.182798e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>3.870000e-01</td>\n",
       "      <td>3.956000e+01</td>\n",
       "      <td>3.945000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>5.200000e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>5.196293e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>5.196293e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.196293e-01</td>\n",
       "      <td>3.501000e+01</td>\n",
       "      <td>3.488000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SGD Regressor</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-1.565148e+15</td>\n",
       "      <td>1.998379e+09</td>\n",
       "      <td>1.992694e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SGD Regressor</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-8.998737e+11</td>\n",
       "      <td>4.791715e+07</td>\n",
       "      <td>4.782514e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name   Alpha L1_ratio            R2    RMSE_TRAIN  \\\n",
       "0             LinReg_without_VIF     NIL      NIL  8.140000e-01  2.179785e+01   \n",
       "1  LinReg_with_VIF_without categ     NIL      NIL  3.870000e-01  3.956000e+01   \n",
       "2     LinReg_with_VIF_with_categ     NIL      NIL  5.200000e-01  3.501000e+01   \n",
       "3                          Ridge       1      NIL  5.196293e-01  3.501000e+01   \n",
       "4                          Lasso       1      NIL  5.196293e-01  3.501000e+01   \n",
       "5                    Elastic_Net       1      0.5  5.196293e-01  3.501000e+01   \n",
       "6                  SGD Regressor  0.0001     0.15 -1.565148e+15  1.998379e+09   \n",
       "7                  SGD Regressor  0.0001     0.15 -8.998737e+11  4.791715e+07   \n",
       "\n",
       "      RMSE_TEST  \n",
       "0  2.182798e+01  \n",
       "1  3.945000e+01  \n",
       "2  3.488000e+01  \n",
       "3  3.488000e+01  \n",
       "4  3.488000e+01  \n",
       "5  3.488000e+01  \n",
       "6  1.992694e+09  \n",
       "7  4.782514e+07  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.drop(index = [6,7],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "plt.plot(np.array([1,2,3,4,5,6]),results['RMSE_TRAIN'],'r-o',label = 'RMSE Train Values')\n",
    "plt.plot(np.array([1,2,3,4,5,6]),results['RMSE_TEST'],'b-o',label = 'RMSE Test values')\n",
    "\n",
    "plt.xlabel(\"Base Model Number\")\n",
    "plt.ylabel(\"RMSE\")\n",
    "plt.legend()\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So far, it can be noted that the model is in the underfit zone as RMSE is low (high bias) and difference between trian and test RMSE is very less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### HYPERTUNING ELASTIC NET MODEL:\n",
    "\n",
    "params = {'alpha': [0.01,0.5,1],'l1_ratio':[0,0.1,0.5]}\n",
    "\n",
    "grid1 = GridSearchCV(estimator= enet,param_grid= params)\n",
    "\n",
    "m8 = grid1.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.01, 'l1_ratio': 0.5}"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m8.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  36.17\n",
      "RMSE test:  36.03\n"
     ]
    }
   ],
   "source": [
    "m8 = ElasticNet(alpha= 0.01,l1_ratio= 0)\n",
    "\n",
    "m8 = m8.fit(xtrain,ytrain)\n",
    "r2 = m8.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = m8.predict(xtrain)\n",
    "ypred2 = m8.predict(xtest)\n",
    "\n",
    "rmse1 = round(np.sqrt(mean_squared_error(ytrain,ypred1)),2)\n",
    "\n",
    "rmse2 = round(np.sqrt(mean_squared_error(ytest,ypred2)),2)\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score7 = pd.DataFrame({'Model Name':['ENet_GridSearch'],'Alpha':[.01],\n",
    "                      'L1_ratio':[0],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score7)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.index = [0,1,2,3,4,5,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.79785</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.56000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.01000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.17000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0             LinReg_without_VIF   NIL      NIL  0.814000    21.79785   \n",
       "1  LinReg_with_VIF_without categ   NIL      NIL  0.387000    39.56000   \n",
       "2     LinReg_with_VIF_with_categ   NIL      NIL  0.520000    35.01000   \n",
       "3                          Ridge     1      NIL  0.519629    35.01000   \n",
       "4                          Lasso     1      NIL  0.519629    35.01000   \n",
       "5                    Elastic_Net     1      0.5  0.519629    35.01000   \n",
       "6                ENet_GridSearch  0.01        0  0.487257    36.17000   \n",
       "\n",
       "   RMSE_TEST  \n",
       "0  21.827985  \n",
       "1  39.450000  \n",
       "2  34.880000  \n",
       "3  34.880000  \n",
       "4  34.880000  \n",
       "5  34.880000  \n",
       "6  36.030000  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TRANSFORMING TARGET VARIABLE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train[['SO2','NO2','O3','WSPM','DEWP','PM2.5']]\n",
    "\n",
    "y = X['PM2.5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SO2      1.512136\n",
       "NO2      0.688403\n",
       "O3       0.878186\n",
       "WSPM     0.730435\n",
       "DEWP    -0.356371\n",
       "PM2.5    1.187056\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1870557382806683"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.49318465318592675"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = y.copy()\n",
    "\n",
    "y1 = np.log(y1)\n",
    "\n",
    "y1.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.49318465318592675"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.log(X['PM2.5'])\n",
    "y.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop('PM2.5',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.concat([X,df1_cat_encode],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>DEWP</th>\n",
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       "      <th>day_26</th>\n",
       "      <th>day_27</th>\n",
       "      <th>day_28</th>\n",
       "      <th>day_29</th>\n",
       "      <th>day_30</th>\n",
       "      <th>day_31</th>\n",
       "      <th>hour_1</th>\n",
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       "      <th>hour_3</th>\n",
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       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
       "      <th>hour_7</th>\n",
       "      <th>hour_8</th>\n",
       "      <th>hour_9</th>\n",
       "      <th>hour_10</th>\n",
       "      <th>hour_11</th>\n",
       "      <th>hour_12</th>\n",
       "      <th>hour_13</th>\n",
       "      <th>hour_14</th>\n",
       "      <th>hour_15</th>\n",
       "      <th>hour_16</th>\n",
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       "      <th>hour_18</th>\n",
       "      <th>hour_19</th>\n",
       "      <th>hour_20</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>wd_ENE</th>\n",
       "      <th>wd_ESE</th>\n",
       "      <th>wd_N</th>\n",
       "      <th>wd_NE</th>\n",
       "      <th>wd_NNE</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "      <th>station_Changping</th>\n",
       "      <th>station_Dingling</th>\n",
       "      <th>station_Dongsi</th>\n",
       "      <th>station_Guanyuan</th>\n",
       "      <th>station_Gucheng</th>\n",
       "      <th>station_Huairou</th>\n",
       "      <th>station_Nongzhanguan</th>\n",
       "      <th>station_Shunyi</th>\n",
       "      <th>station_Tiantan</th>\n",
       "      <th>station_Wanliu</th>\n",
       "      <th>station_Wanshouxigong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.5</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const   SO2   NO2    O3  WSPM  DEWP  year_2014  year_2015  year_2016  \\\n",
       "3    1.0  11.0  11.0  72.0   3.1 -19.4          0          0          0   \n",
       "4    1.0  12.0  12.0  72.0   2.0 -19.5          0          0          0   \n",
       "5    1.0  18.0  18.0  66.0   3.7 -19.6          0          0          0   \n",
       "6    1.0  18.0  32.0  50.0   2.5 -19.1          0          0          0   \n",
       "7    1.0  19.0  41.0  43.0   3.8 -19.1          0          0          0   \n",
       "\n",
       "   month_2  month_3  month_4  month_5  month_6  month_7  month_8  month_9  \\\n",
       "3        0        1        0        0        0        0        0        0   \n",
       "4        0        1        0        0        0        0        0        0   \n",
       "5        0        1        0        0        0        0        0        0   \n",
       "6        0        1        0        0        0        0        0        0   \n",
       "7        0        1        0        0        0        0        0        0   \n",
       "\n",
       "   month_10  month_11  month_12  day_2  day_3  day_4  day_5  day_6  day_7  \\\n",
       "3         0         0         0      0      0      0      0      0      0   \n",
       "4         0         0         0      0      0      0      0      0      0   \n",
       "5         0         0         0      0      0      0      0      0      0   \n",
       "6         0         0         0      0      0      0      0      0      0   \n",
       "7         0         0         0      0      0      0      0      0      0   \n",
       "\n",
       "   day_8  day_9  day_10  day_11  day_12  day_13  day_14  day_15  day_16  \\\n",
       "3      0      0       0       0       0       0       0       0       0   \n",
       "4      0      0       0       0       0       0       0       0       0   \n",
       "5      0      0       0       0       0       0       0       0       0   \n",
       "6      0      0       0       0       0       0       0       0       0   \n",
       "7      0      0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_17  day_18  day_19  day_20  day_21  day_22  day_23  day_24  day_25  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_26  day_27  day_28  day_29  day_30  day_31  hour_1  hour_2  hour_3  \\\n",
       "3       0       0       0       0       0       0       0       0       1   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   hour_4  hour_5  hour_6  hour_7  hour_8  hour_9  hour_10  hour_11  hour_12  \\\n",
       "3       0       0       0       0       0       0        0        0        0   \n",
       "4       1       0       0       0       0       0        0        0        0   \n",
       "5       0       1       0       0       0       0        0        0        0   \n",
       "6       0       0       1       0       0       0        0        0        0   \n",
       "7       0       0       0       1       0       0        0        0        0   \n",
       "\n",
       "   hour_13  hour_14  hour_15  hour_16  hour_17  hour_18  hour_19  hour_20  \\\n",
       "3        0        0        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "6        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   hour_21  hour_22  hour_23  wd_ENE  wd_ESE  wd_N  wd_NE  wd_NNE  wd_NNW  \\\n",
       "3        0        0        0       0       0     0      0       0       0   \n",
       "4        0        0        0       0       0     1      0       0       0   \n",
       "5        0        0        0       0       0     1      0       0       0   \n",
       "6        0        0        0       0       0     0      0       1       0   \n",
       "7        0        0        0       0       0     0      0       0       1   \n",
       "\n",
       "   wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW  wd_SW  wd_W  wd_WNW  wd_WSW  \\\n",
       "3      1     0      0       0       0      0     0       0       0   \n",
       "4      0     0      0       0       0      0     0       0       0   \n",
       "5      0     0      0       0       0      0     0       0       0   \n",
       "6      0     0      0       0       0      0     0       0       0   \n",
       "7      0     0      0       0       0      0     0       0       0   \n",
       "\n",
       "   station_Changping  station_Dingling  station_Dongsi  station_Guanyuan  \\\n",
       "3                  0                 0               0                 0   \n",
       "4                  0                 0               0                 0   \n",
       "5                  0                 0               0                 0   \n",
       "6                  0                 0               0                 0   \n",
       "7                  0                 0               0                 0   \n",
       "\n",
       "   station_Gucheng  station_Huairou  station_Nongzhanguan  station_Shunyi  \\\n",
       "3                0                0                     0               0   \n",
       "4                0                0                     0               0   \n",
       "5                0                0                     0               0   \n",
       "6                0                0                     0               0   \n",
       "7                0                0                     0               0   \n",
       "\n",
       "   station_Tiantan  station_Wanliu  station_Wanshouxigong  \n",
       "3                0               0                      0  \n",
       "4                0               0                      0  \n",
       "5                0               0                      0  \n",
       "6                0               0                      0  \n",
       "7                0               0                      0  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sm.add_constant(X)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205604, 99), (88116, 99), (205604,), (88116,))"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3,random_state = 111)\n",
    "\n",
    "xtrain.shape,xtest.shape,ytrain.shape,ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  0.6577567107690843\n",
      "RMSE test:  0.6564855021479957\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0             LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1  LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2     LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                          Ridge     1      NIL  0.519629   35.010000   \n",
       "4                          Lasso     1      NIL  0.519629   35.010000   \n",
       "5                    Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7       ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "\n",
       "   RMSE_TEST  \n",
       "0  21.827985  \n",
       "1  39.450000  \n",
       "2  34.880000  \n",
       "3  34.880000  \n",
       "4  34.880000  \n",
       "5  34.880000  \n",
       "6  36.030000  \n",
       "7   0.656486  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m9 = ElasticNet(alpha= 0.01,l1_ratio= 0)\n",
    "\n",
    "m9 = m9.fit(xtrain,ytrain)\n",
    "r2 = m9.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = m9.predict(xtrain)\n",
    "ypred2 = m9.predict(xtest)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(ytrain,ypred1))\n",
    "\n",
    "rmse2 = np.sqrt(mean_squared_error(ytest,ypred2))\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score8 = pd.DataFrame({'Model Name':['ENet_GridSearch_transf-y'],'Alpha':[.01],\n",
    "                      'L1_ratio':[0],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score8)\n",
    "results.index = [0,1,2,3,4,5,6,7]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TRANSFORMING SKEW OF PREDICTORS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SO2      1.512136\n",
       "NO2      0.688403\n",
       "O3       0.878186\n",
       "WSPM     0.730435\n",
       "DEWP    -0.356371\n",
       "PM2.5    1.187056\n",
       "dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_train[['SO2','NO2','O3','WSPM','DEWP','PM2.5']]\n",
    "\n",
    "y = X['PM2.5']\n",
    "\n",
    "X.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import boxcox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0030195633088023043"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TRANSFORMING SO2 VARIABLE:\n",
    "\n",
    "X['SO2'] = boxcox(X['SO2'],lmbda = -0.2)\n",
    "\n",
    "X['SO2'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.904780</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.958178</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.195112</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.195112</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.225279</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SO2   NO2    O3  WSPM  DEWP  PM2.5\n",
       "3  1.904780  11.0  72.0   3.1 -19.4    6.0\n",
       "4  1.958178  12.0  72.0   2.0 -19.5    3.0\n",
       "5  2.195112  18.0  66.0   3.7 -19.6    5.0\n",
       "6  2.195112  32.0  50.0   2.5 -19.1    3.0\n",
       "7  2.225279  41.0  43.0   3.8 -19.1    3.0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X['PM2.5']\n",
    "\n",
    "X.drop('PM2.5',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>year_2014</th>\n",
       "      <th>year_2015</th>\n",
       "      <th>year_2016</th>\n",
       "      <th>month_2</th>\n",
       "      <th>month_3</th>\n",
       "      <th>month_4</th>\n",
       "      <th>month_5</th>\n",
       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "      <th>month_12</th>\n",
       "      <th>day_2</th>\n",
       "      <th>day_3</th>\n",
       "      <th>day_4</th>\n",
       "      <th>day_5</th>\n",
       "      <th>day_6</th>\n",
       "      <th>day_7</th>\n",
       "      <th>day_8</th>\n",
       "      <th>day_9</th>\n",
       "      <th>day_10</th>\n",
       "      <th>day_11</th>\n",
       "      <th>day_12</th>\n",
       "      <th>day_13</th>\n",
       "      <th>day_14</th>\n",
       "      <th>day_15</th>\n",
       "      <th>day_16</th>\n",
       "      <th>day_17</th>\n",
       "      <th>day_18</th>\n",
       "      <th>day_19</th>\n",
       "      <th>day_20</th>\n",
       "      <th>day_21</th>\n",
       "      <th>day_22</th>\n",
       "      <th>day_23</th>\n",
       "      <th>day_24</th>\n",
       "      <th>day_25</th>\n",
       "      <th>day_26</th>\n",
       "      <th>day_27</th>\n",
       "      <th>day_28</th>\n",
       "      <th>day_29</th>\n",
       "      <th>day_30</th>\n",
       "      <th>day_31</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
       "      <th>hour_7</th>\n",
       "      <th>hour_8</th>\n",
       "      <th>hour_9</th>\n",
       "      <th>hour_10</th>\n",
       "      <th>hour_11</th>\n",
       "      <th>hour_12</th>\n",
       "      <th>hour_13</th>\n",
       "      <th>hour_14</th>\n",
       "      <th>hour_15</th>\n",
       "      <th>hour_16</th>\n",
       "      <th>hour_17</th>\n",
       "      <th>hour_18</th>\n",
       "      <th>hour_19</th>\n",
       "      <th>hour_20</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>wd_ENE</th>\n",
       "      <th>wd_ESE</th>\n",
       "      <th>wd_N</th>\n",
       "      <th>wd_NE</th>\n",
       "      <th>wd_NNE</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "      <th>station_Changping</th>\n",
       "      <th>station_Dingling</th>\n",
       "      <th>station_Dongsi</th>\n",
       "      <th>station_Guanyuan</th>\n",
       "      <th>station_Gucheng</th>\n",
       "      <th>station_Huairou</th>\n",
       "      <th>station_Nongzhanguan</th>\n",
       "      <th>station_Shunyi</th>\n",
       "      <th>station_Tiantan</th>\n",
       "      <th>station_Wanliu</th>\n",
       "      <th>station_Wanshouxigong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.904780</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.958178</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.195112</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.195112</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.225279</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SO2   NO2    O3  WSPM  DEWP  year_2014  year_2015  year_2016  month_2  \\\n",
       "3  1.904780  11.0  72.0   3.1 -19.4          0          0          0        0   \n",
       "4  1.958178  12.0  72.0   2.0 -19.5          0          0          0        0   \n",
       "5  2.195112  18.0  66.0   3.7 -19.6          0          0          0        0   \n",
       "6  2.195112  32.0  50.0   2.5 -19.1          0          0          0        0   \n",
       "7  2.225279  41.0  43.0   3.8 -19.1          0          0          0        0   \n",
       "\n",
       "   month_3  month_4  month_5  month_6  month_7  month_8  month_9  month_10  \\\n",
       "3        1        0        0        0        0        0        0         0   \n",
       "4        1        0        0        0        0        0        0         0   \n",
       "5        1        0        0        0        0        0        0         0   \n",
       "6        1        0        0        0        0        0        0         0   \n",
       "7        1        0        0        0        0        0        0         0   \n",
       "\n",
       "   month_11  month_12  day_2  day_3  day_4  day_5  day_6  day_7  day_8  day_9  \\\n",
       "3         0         0      0      0      0      0      0      0      0      0   \n",
       "4         0         0      0      0      0      0      0      0      0      0   \n",
       "5         0         0      0      0      0      0      0      0      0      0   \n",
       "6         0         0      0      0      0      0      0      0      0      0   \n",
       "7         0         0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   day_10  day_11  day_12  day_13  day_14  day_15  day_16  day_17  day_18  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_19  day_20  day_21  day_22  day_23  day_24  day_25  day_26  day_27  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_28  day_29  day_30  day_31  hour_1  hour_2  hour_3  hour_4  hour_5  \\\n",
       "3       0       0       0       0       0       0       1       0       0   \n",
       "4       0       0       0       0       0       0       0       1       0   \n",
       "5       0       0       0       0       0       0       0       0       1   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   hour_6  hour_7  hour_8  hour_9  hour_10  hour_11  hour_12  hour_13  \\\n",
       "3       0       0       0       0        0        0        0        0   \n",
       "4       0       0       0       0        0        0        0        0   \n",
       "5       0       0       0       0        0        0        0        0   \n",
       "6       1       0       0       0        0        0        0        0   \n",
       "7       0       1       0       0        0        0        0        0   \n",
       "\n",
       "   hour_14  hour_15  hour_16  hour_17  hour_18  hour_19  hour_20  hour_21  \\\n",
       "3        0        0        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "6        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   hour_22  hour_23  wd_ENE  wd_ESE  wd_N  wd_NE  wd_NNE  wd_NNW  wd_NW  wd_S  \\\n",
       "3        0        0       0       0     0      0       0       0      1     0   \n",
       "4        0        0       0       0     1      0       0       0      0     0   \n",
       "5        0        0       0       0     1      0       0       0      0     0   \n",
       "6        0        0       0       0     0      0       1       0      0     0   \n",
       "7        0        0       0       0     0      0       0       1      0     0   \n",
       "\n",
       "   wd_SE  wd_SSE  wd_SSW  wd_SW  wd_W  wd_WNW  wd_WSW  station_Changping  \\\n",
       "3      0       0       0      0     0       0       0                  0   \n",
       "4      0       0       0      0     0       0       0                  0   \n",
       "5      0       0       0      0     0       0       0                  0   \n",
       "6      0       0       0      0     0       0       0                  0   \n",
       "7      0       0       0      0     0       0       0                  0   \n",
       "\n",
       "   station_Dingling  station_Dongsi  station_Guanyuan  station_Gucheng  \\\n",
       "3                 0               0                 0                0   \n",
       "4                 0               0                 0                0   \n",
       "5                 0               0                 0                0   \n",
       "6                 0               0                 0                0   \n",
       "7                 0               0                 0                0   \n",
       "\n",
       "   station_Huairou  station_Nongzhanguan  station_Shunyi  station_Tiantan  \\\n",
       "3                0                     0               0                0   \n",
       "4                0                     0               0                0   \n",
       "5                0                     0               0                0   \n",
       "6                0                     0               0                0   \n",
       "7                0                     0               0                0   \n",
       "\n",
       "   station_Wanliu  station_Wanshouxigong  \n",
       "3               0                      0  \n",
       "4               0                      0  \n",
       "5               0                      0  \n",
       "6               0                      0  \n",
       "7               0                      0  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.concat([X,df1_cat_encode],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  36.390854433916495\n",
      "RMSE test:  36.25751419668579\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENet_GridSearch_transformedall</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480980</td>\n",
       "      <td>36.390854</td>\n",
       "      <td>36.257514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0              LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1   LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2      LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                           Ridge     1      NIL  0.519629   35.010000   \n",
       "4                           Lasso     1      NIL  0.519629   35.010000   \n",
       "5                     Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                 ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7        ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "8  ENet_GridSearch_transformedall  0.01        0  0.480980   36.390854   \n",
       "\n",
       "   RMSE_TEST  \n",
       "0  21.827985  \n",
       "1  39.450000  \n",
       "2  34.880000  \n",
       "3  34.880000  \n",
       "4  34.880000  \n",
       "5  34.880000  \n",
       "6  36.030000  \n",
       "7   0.656486  \n",
       "8  36.257514  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3,random_state = 111)\n",
    "\n",
    "#xtrain.shape,xtest.shape,ytrain.shape,ytest.shape\n",
    "\n",
    "m10 = ElasticNet(alpha= 0.01,l1_ratio= 0)\n",
    "\n",
    "m10 = m10.fit(xtrain,ytrain)\n",
    "r2 = m10.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = m10.predict(xtrain)\n",
    "ypred2 = m10.predict(xtest)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(ytrain,ypred1))\n",
    "\n",
    "rmse2 = np.sqrt(mean_squared_error(ytest,ypred2))\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score9 = pd.DataFrame({'Model Name':['ENet_GridSearch_transformedall'],'Alpha':[.01],\n",
    "                      'L1_ratio':[0],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score9)\n",
    "results.index = [0,1,2,3,4,5,6,7,8]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### MODEL WITHOUT YEAR:\n",
    "X = df_train[['SO2','NO2','O3','WSPM','DEWP','PM2.5']]\n",
    "X['PM2.5'] = np.log(X['PM2.5'])\n",
    "\n",
    "df1_cat_encode1 = df1_cat_encode.drop(['year_2014','year_2015','year_2016'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>DEWP</th>\n",
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       "      <th>month_3</th>\n",
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       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
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       "      <th>day_2</th>\n",
       "      <th>day_3</th>\n",
       "      <th>day_4</th>\n",
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       "      <th>day_6</th>\n",
       "      <th>day_7</th>\n",
       "      <th>day_8</th>\n",
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       "      <th>day_15</th>\n",
       "      <th>day_16</th>\n",
       "      <th>day_17</th>\n",
       "      <th>day_18</th>\n",
       "      <th>day_19</th>\n",
       "      <th>day_20</th>\n",
       "      <th>day_21</th>\n",
       "      <th>day_22</th>\n",
       "      <th>day_23</th>\n",
       "      <th>day_24</th>\n",
       "      <th>day_25</th>\n",
       "      <th>day_26</th>\n",
       "      <th>day_27</th>\n",
       "      <th>day_28</th>\n",
       "      <th>day_29</th>\n",
       "      <th>day_30</th>\n",
       "      <th>day_31</th>\n",
       "      <th>hour_1</th>\n",
       "      <th>hour_2</th>\n",
       "      <th>hour_3</th>\n",
       "      <th>hour_4</th>\n",
       "      <th>hour_5</th>\n",
       "      <th>hour_6</th>\n",
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       "      <th>hour_8</th>\n",
       "      <th>hour_9</th>\n",
       "      <th>hour_10</th>\n",
       "      <th>hour_11</th>\n",
       "      <th>hour_12</th>\n",
       "      <th>hour_13</th>\n",
       "      <th>hour_14</th>\n",
       "      <th>hour_15</th>\n",
       "      <th>hour_16</th>\n",
       "      <th>hour_17</th>\n",
       "      <th>hour_18</th>\n",
       "      <th>hour_19</th>\n",
       "      <th>hour_20</th>\n",
       "      <th>hour_21</th>\n",
       "      <th>hour_22</th>\n",
       "      <th>hour_23</th>\n",
       "      <th>wd_ENE</th>\n",
       "      <th>wd_ESE</th>\n",
       "      <th>wd_N</th>\n",
       "      <th>wd_NE</th>\n",
       "      <th>wd_NNE</th>\n",
       "      <th>wd_NNW</th>\n",
       "      <th>wd_NW</th>\n",
       "      <th>wd_S</th>\n",
       "      <th>wd_SE</th>\n",
       "      <th>wd_SSE</th>\n",
       "      <th>wd_SSW</th>\n",
       "      <th>wd_SW</th>\n",
       "      <th>wd_W</th>\n",
       "      <th>wd_WNW</th>\n",
       "      <th>wd_WSW</th>\n",
       "      <th>station_Changping</th>\n",
       "      <th>station_Dingling</th>\n",
       "      <th>station_Dongsi</th>\n",
       "      <th>station_Guanyuan</th>\n",
       "      <th>station_Gucheng</th>\n",
       "      <th>station_Huairou</th>\n",
       "      <th>station_Nongzhanguan</th>\n",
       "      <th>station_Shunyi</th>\n",
       "      <th>station_Tiantan</th>\n",
       "      <th>station_Wanliu</th>\n",
       "      <th>station_Wanshouxigong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-19.4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-19.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const   SO2   NO2    O3  WSPM  DEWP  month_2  month_3  month_4  month_5  \\\n",
       "3    1.0  11.0  11.0  72.0   3.1 -19.4        0        1        0        0   \n",
       "4    1.0  12.0  12.0  72.0   2.0 -19.5        0        1        0        0   \n",
       "5    1.0  18.0  18.0  66.0   3.7 -19.6        0        1        0        0   \n",
       "6    1.0  18.0  32.0  50.0   2.5 -19.1        0        1        0        0   \n",
       "7    1.0  19.0  41.0  43.0   3.8 -19.1        0        1        0        0   \n",
       "\n",
       "   month_6  month_7  month_8  month_9  month_10  month_11  month_12  day_2  \\\n",
       "3        0        0        0        0         0         0         0      0   \n",
       "4        0        0        0        0         0         0         0      0   \n",
       "5        0        0        0        0         0         0         0      0   \n",
       "6        0        0        0        0         0         0         0      0   \n",
       "7        0        0        0        0         0         0         0      0   \n",
       "\n",
       "   day_3  day_4  day_5  day_6  day_7  day_8  day_9  day_10  day_11  day_12  \\\n",
       "3      0      0      0      0      0      0      0       0       0       0   \n",
       "4      0      0      0      0      0      0      0       0       0       0   \n",
       "5      0      0      0      0      0      0      0       0       0       0   \n",
       "6      0      0      0      0      0      0      0       0       0       0   \n",
       "7      0      0      0      0      0      0      0       0       0       0   \n",
       "\n",
       "   day_13  day_14  day_15  day_16  day_17  day_18  day_19  day_20  day_21  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_22  day_23  day_24  day_25  day_26  day_27  day_28  day_29  day_30  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_31  hour_1  hour_2  hour_3  hour_4  hour_5  hour_6  hour_7  hour_8  \\\n",
       "3       0       0       0       1       0       0       0       0       0   \n",
       "4       0       0       0       0       1       0       0       0       0   \n",
       "5       0       0       0       0       0       1       0       0       0   \n",
       "6       0       0       0       0       0       0       1       0       0   \n",
       "7       0       0       0       0       0       0       0       1       0   \n",
       "\n",
       "   hour_9  hour_10  hour_11  hour_12  hour_13  hour_14  hour_15  hour_16  \\\n",
       "3       0        0        0        0        0        0        0        0   \n",
       "4       0        0        0        0        0        0        0        0   \n",
       "5       0        0        0        0        0        0        0        0   \n",
       "6       0        0        0        0        0        0        0        0   \n",
       "7       0        0        0        0        0        0        0        0   \n",
       "\n",
       "   hour_17  hour_18  hour_19  hour_20  hour_21  hour_22  hour_23  wd_ENE  \\\n",
       "3        0        0        0        0        0        0        0       0   \n",
       "4        0        0        0        0        0        0        0       0   \n",
       "5        0        0        0        0        0        0        0       0   \n",
       "6        0        0        0        0        0        0        0       0   \n",
       "7        0        0        0        0        0        0        0       0   \n",
       "\n",
       "   wd_ESE  wd_N  wd_NE  wd_NNE  wd_NNW  wd_NW  wd_S  wd_SE  wd_SSE  wd_SSW  \\\n",
       "3       0     0      0       0       0      1     0      0       0       0   \n",
       "4       0     1      0       0       0      0     0      0       0       0   \n",
       "5       0     1      0       0       0      0     0      0       0       0   \n",
       "6       0     0      0       1       0      0     0      0       0       0   \n",
       "7       0     0      0       0       1      0     0      0       0       0   \n",
       "\n",
       "   wd_SW  wd_W  wd_WNW  wd_WSW  station_Changping  station_Dingling  \\\n",
       "3      0     0       0       0                  0                 0   \n",
       "4      0     0       0       0                  0                 0   \n",
       "5      0     0       0       0                  0                 0   \n",
       "6      0     0       0       0                  0                 0   \n",
       "7      0     0       0       0                  0                 0   \n",
       "\n",
       "   station_Dongsi  station_Guanyuan  station_Gucheng  station_Huairou  \\\n",
       "3               0                 0                0                0   \n",
       "4               0                 0                0                0   \n",
       "5               0                 0                0                0   \n",
       "6               0                 0                0                0   \n",
       "7               0                 0                0                0   \n",
       "\n",
       "   station_Nongzhanguan  station_Shunyi  station_Tiantan  station_Wanliu  \\\n",
       "3                     0               0                0               0   \n",
       "4                     0               0                0               0   \n",
       "5                     0               0                0               0   \n",
       "6                     0               0                0               0   \n",
       "7                     0               0                0               0   \n",
       "\n",
       "   station_Wanshouxigong  \n",
       "3                      0  \n",
       "4                      0  \n",
       "5                      0  \n",
       "6                      0  \n",
       "7                      0  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = X['PM2.5']\n",
    "X.drop('PM2.5',axis=1,inplace=True)\n",
    "X = sm.add_constant(X)\n",
    "X = pd.concat([X,df1_cat_encode1],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1.791759\n",
       "4    1.098612\n",
       "5    1.609438\n",
       "6    1.098612\n",
       "7    1.098612\n",
       "Name: PM2.5, dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  0.66\n",
      "RMSE test:  0.66\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENet_GridSearch_transformedall</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480980</td>\n",
       "      <td>36.390854</td>\n",
       "      <td>36.257514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENet_GS_withoutyear</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.578986</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0              LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1   LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2      LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                           Ridge     1      NIL  0.519629   35.010000   \n",
       "4                           Lasso     1      NIL  0.519629   35.010000   \n",
       "5                     Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                 ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7        ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "8  ENet_GridSearch_transformedall  0.01        0  0.480980   36.390854   \n",
       "9             ENet_GS_withoutyear  0.01        0  0.578986    0.660000   \n",
       "\n",
       "   RMSE_TEST  \n",
       "0  21.827985  \n",
       "1  39.450000  \n",
       "2  34.880000  \n",
       "3  34.880000  \n",
       "4  34.880000  \n",
       "5  34.880000  \n",
       "6  36.030000  \n",
       "7   0.656486  \n",
       "8  36.257514  \n",
       "9   0.660000  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3,random_state = 111)\n",
    "\n",
    "#xtrain.shape,xtest.shape,ytrain.shape,ytest.shape\n",
    "\n",
    "m11 = ElasticNet(alpha= 0.01,l1_ratio= 0)\n",
    "\n",
    "m11 = m11.fit(xtrain,ytrain)\n",
    "r2 = m11.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = m11.predict(xtrain)\n",
    "ypred2 = m11.predict(xtest)\n",
    "\n",
    "rmse1 = round(np.sqrt(mean_squared_error(ytrain,ypred1)),2)\n",
    "\n",
    "rmse2 = round(np.sqrt(mean_squared_error(ytest,ypred2)),2)\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score10 = pd.DataFrame({'Model Name':['ENet_GS_withoutyear'],'Alpha':[.01],\n",
    "                      'L1_ratio':[0],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score10)\n",
    "results.index = [0,1,2,3,4,5,6,7,8,9]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REF_NO</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>1025.6</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>1026.5</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>1027.4</td>\n",
       "      <td>-19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   REF_NO  year month day hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "3       4  2013     3   1    3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4       5  2013     3   1    4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "5       6  2013     3   1    5    5.0   5.0  18.0  18.0  400.0  66.0  -2.2   \n",
       "6       7  2013     3   1    6    3.0   3.0  18.0  32.0  500.0  50.0  -2.6   \n",
       "7       8  2013     3   1    7    3.0   6.0  19.0  41.0  500.0  43.0  -1.6   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "3  1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4  1025.2 -19.5   0.0    N   2.0  Aotizhongxin  \n",
       "5  1025.6 -19.6   0.0    N   3.7  Aotizhongxin  \n",
       "6  1026.5 -19.1   0.0  NNE   2.5  Aotizhongxin  \n",
       "7  1027.4 -19.1   0.0  NNW   3.8  Aotizhongxin  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        SO2       NO2        CO      PM10        O3      WSPM      DEWP  \\\n",
       "3  0.116133 -1.252594 -1.064387 -1.270813  0.414006  1.694519 -1.708398   \n",
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       "\n",
       "      PM2.5  RAIN      TEMP      PRES  \n",
       "3 -1.057997   0.0 -1.503618  1.407497  \n",
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      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_train[['SO2','NO2','CO','PM10','O3','WSPM','DEWP','PM2.5','RAIN','TEMP','PRES']]\n",
    "\n",
    "col = X.columns\n",
    "X[col] = StandardScaler().fit_transform(X[col])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.217671</td>\n",
       "      <td>-1.215977</td>\n",
       "      <td>-1.064387</td>\n",
       "      <td>-1.321090</td>\n",
       "      <td>0.414006</td>\n",
       "      <td>0.496727</td>\n",
       "      <td>-1.715722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.558892</td>\n",
       "      <td>1.475199</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.826897</td>\n",
       "      <td>-0.996276</td>\n",
       "      <td>-0.892120</td>\n",
       "      <td>-1.287572</td>\n",
       "      <td>0.279480</td>\n",
       "      <td>2.347859</td>\n",
       "      <td>-1.723046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.577316</td>\n",
       "      <td>1.513885</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.826897</td>\n",
       "      <td>-0.483639</td>\n",
       "      <td>-0.719852</td>\n",
       "      <td>-1.321090</td>\n",
       "      <td>-0.079255</td>\n",
       "      <td>1.041178</td>\n",
       "      <td>-1.686427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.614165</td>\n",
       "      <td>1.600930</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.928435</td>\n",
       "      <td>-0.154087</td>\n",
       "      <td>-0.719852</td>\n",
       "      <td>-1.270813</td>\n",
       "      <td>-0.236202</td>\n",
       "      <td>2.456750</td>\n",
       "      <td>-1.686427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.522043</td>\n",
       "      <td>1.687974</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SO2       NO2        CO      PM10        O3      WSPM      DEWP  RAIN  \\\n",
       "3  0.116133 -1.252594 -1.064387 -1.270813  0.414006  1.694519 -1.708398   0.0   \n",
       "4  0.217671 -1.215977 -1.064387 -1.321090  0.414006  0.496727 -1.715722   0.0   \n",
       "5  0.826897 -0.996276 -0.892120 -1.287572  0.279480  2.347859 -1.723046   0.0   \n",
       "6  0.826897 -0.483639 -0.719852 -1.321090 -0.079255  1.041178 -1.686427   0.0   \n",
       "7  0.928435 -0.154087 -0.719852 -1.270813 -0.236202  2.456750 -1.686427   0.0   \n",
       "\n",
       "       TEMP      PRES  year_2014  year_2015  year_2016  month_2  month_3  \\\n",
       "3 -1.503618  1.407497          0          0          0        0        1   \n",
       "4 -1.558892  1.475199          0          0          0        0        1   \n",
       "5 -1.577316  1.513885          0          0          0        0        1   \n",
       "6 -1.614165  1.600930          0          0          0        0        1   \n",
       "7 -1.522043  1.687974          0          0          0        0        1   \n",
       "\n",
       "   month_4  month_5  month_6  month_7  month_8  month_9  month_10  month_11  \\\n",
       "3        0        0        0        0        0        0         0         0   \n",
       "4        0        0        0        0        0        0         0         0   \n",
       "5        0        0        0        0        0        0         0         0   \n",
       "6        0        0        0        0        0        0         0         0   \n",
       "7        0        0        0        0        0        0         0         0   \n",
       "\n",
       "   month_12  day_2  day_3  day_4  day_5  day_6  day_7  day_8  day_9  day_10  \\\n",
       "3         0      0      0      0      0      0      0      0      0       0   \n",
       "4         0      0      0      0      0      0      0      0      0       0   \n",
       "5         0      0      0      0      0      0      0      0      0       0   \n",
       "6         0      0      0      0      0      0      0      0      0       0   \n",
       "7         0      0      0      0      0      0      0      0      0       0   \n",
       "\n",
       "   day_11  day_12  day_13  day_14  day_15  day_16  day_17  day_18  day_19  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_20  day_21  day_22  day_23  day_24  day_25  day_26  day_27  day_28  \\\n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "5       0       0       0       0       0       0       0       0       0   \n",
       "6       0       0       0       0       0       0       0       0       0   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   day_29  day_30  day_31  hour_1  hour_2  hour_3  hour_4  hour_5  hour_6  \\\n",
       "3       0       0       0       0       0       1       0       0       0   \n",
       "4       0       0       0       0       0       0       1       0       0   \n",
       "5       0       0       0       0       0       0       0       1       0   \n",
       "6       0       0       0       0       0       0       0       0       1   \n",
       "7       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   hour_7  hour_8  hour_9  hour_10  hour_11  hour_12  hour_13  hour_14  \\\n",
       "3       0       0       0        0        0        0        0        0   \n",
       "4       0       0       0        0        0        0        0        0   \n",
       "5       0       0       0        0        0        0        0        0   \n",
       "6       0       0       0        0        0        0        0        0   \n",
       "7       1       0       0        0        0        0        0        0   \n",
       "\n",
       "   hour_15  hour_16  hour_17  hour_18  hour_19  hour_20  hour_21  hour_22  \\\n",
       "3        0        0        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0        0        0   \n",
       "5        0        0        0        0        0        0        0        0   \n",
       "6        0        0        0        0        0        0        0        0   \n",
       "7        0        0        0        0        0        0        0        0   \n",
       "\n",
       "   hour_23  wd_ENE  wd_ESE  wd_N  wd_NE  wd_NNE  wd_NNW  wd_NW  wd_S  wd_SE  \\\n",
       "3        0       0       0     0      0       0       0      1     0      0   \n",
       "4        0       0       0     1      0       0       0      0     0      0   \n",
       "5        0       0       0     1      0       0       0      0     0      0   \n",
       "6        0       0       0     0      0       1       0      0     0      0   \n",
       "7        0       0       0     0      0       0       1      0     0      0   \n",
       "\n",
       "   wd_SSE  wd_SSW  wd_SW  wd_W  wd_WNW  wd_WSW  station_Changping  \\\n",
       "3       0       0      0     0       0       0                  0   \n",
       "4       0       0      0     0       0       0                  0   \n",
       "5       0       0      0     0       0       0                  0   \n",
       "6       0       0      0     0       0       0                  0   \n",
       "7       0       0      0     0       0       0                  0   \n",
       "\n",
       "   station_Dingling  station_Dongsi  station_Guanyuan  station_Gucheng  \\\n",
       "3                 0               0                 0                0   \n",
       "4                 0               0                 0                0   \n",
       "5                 0               0                 0                0   \n",
       "6                 0               0                 0                0   \n",
       "7                 0               0                 0                0   \n",
       "\n",
       "   station_Huairou  station_Nongzhanguan  station_Shunyi  station_Tiantan  \\\n",
       "3                0                     0               0                0   \n",
       "4                0                     0               0                0   \n",
       "5                0                     0               0                0   \n",
       "6                0                     0               0                0   \n",
       "7                0                     0               0                0   \n",
       "\n",
       "   station_Wanliu  station_Wanshouxigong  \n",
       "3               0                      0  \n",
       "4               0                      0  \n",
       "5               0                      0  \n",
       "6               0                      0  \n",
       "7               0                      0  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = X['PM2.5']\n",
    "X.drop('PM2.5',inplace=True,axis=1)\n",
    "X = pd.concat([X,df1_cat_encode],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205604, 103), (88116, 103), (205604,), (88116,))"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3)\n",
    "xtrain.shape,xtest.shape,ytrain.shape,ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "\n",
    "knn_model = knn.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2 = knn_model.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = knn_model.predict(xtrain)\n",
    "ypred2 = knn_model.predict(xtest)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(ytrain,ypred1))\n",
    "\n",
    "rmse2 = np.sqrt(mean_squared_error(ytest,ypred2))\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score11 = pd.DataFrame({'Model Name':['KNN_regressor'],'Alpha':[.01],\n",
    "                      'L1_ratio':[0],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENet_GridSearch_transformedall</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480980</td>\n",
       "      <td>36.390854</td>\n",
       "      <td>36.257514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENet_GS_withoutyear</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.578986</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KNN_regressor</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.659878</td>\n",
       "      <td>29.458772</td>\n",
       "      <td>36.390447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0               LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1    LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2       LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                            Ridge     1      NIL  0.519629   35.010000   \n",
       "4                            Lasso     1      NIL  0.519629   35.010000   \n",
       "5                      Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                  ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7         ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "8   ENet_GridSearch_transformedall  0.01        0  0.480980   36.390854   \n",
       "9              ENet_GS_withoutyear  0.01        0  0.578986    0.660000   \n",
       "10                   KNN_regressor  0.01        0  0.659878   29.458772   \n",
       "\n",
       "    RMSE_TEST  \n",
       "0   21.827985  \n",
       "1   39.450000  \n",
       "2   34.880000  \n",
       "3   34.880000  \n",
       "4   34.880000  \n",
       "5   34.880000  \n",
       "6   36.030000  \n",
       "7    0.656486  \n",
       "8   36.257514  \n",
       "9    0.660000  \n",
       "10  36.390447  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train[['SO2','NO2','CO','PM10','O3','WSPM','DEWP','RAIN','TEMP','PRES']]\n",
    "X = pd.concat([X,df1_cat_encode],axis=1)\n",
    "y = df_train['PM2.5']\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size = 0.3)\n",
    "xtrain.shape,xtest.shape,ytrain.shape,ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  0.0\n",
      "RMSE test:  29.13886034826916\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENet_GridSearch_transformedall</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480980</td>\n",
       "      <td>36.390854</td>\n",
       "      <td>36.257514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENet_GS_withoutyear</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.578986</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KNN_regressor</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.659878</td>\n",
       "      <td>29.458772</td>\n",
       "      <td>36.390447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.138860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0               LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1    LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2       LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                            Ridge     1      NIL  0.519629   35.010000   \n",
       "4                            Lasso     1      NIL  0.519629   35.010000   \n",
       "5                      Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                  ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7         ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "8   ENet_GridSearch_transformedall  0.01        0  0.480980   36.390854   \n",
       "9              ENet_GS_withoutyear  0.01        0  0.578986    0.660000   \n",
       "10                   KNN_regressor  0.01        0  0.659878   29.458772   \n",
       "11                              DT  0.01        0  1.000000    0.000000   \n",
       "\n",
       "    RMSE_TEST  \n",
       "0   21.827985  \n",
       "1   39.450000  \n",
       "2   34.880000  \n",
       "3   34.880000  \n",
       "4   34.880000  \n",
       "5   34.880000  \n",
       "6   36.030000  \n",
       "7    0.656486  \n",
       "8   36.257514  \n",
       "9    0.660000  \n",
       "10  36.390447  \n",
       "11  29.138860  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeRegressor()\n",
    "dt_model = dt.fit(xtrain,ytrain)\n",
    "r2 = dt_model.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = dt_model.predict(xtrain)\n",
    "ypred2 = dt_model.predict(xtest)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(ytrain,ypred1))\n",
    "\n",
    "rmse2 = np.sqrt(mean_squared_error(ytest,ypred2))\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score12 = pd.DataFrame({'Model Name':['DT'],'Alpha':[.01],\n",
    "                      'L1_ratio':[0],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score12)\n",
    "results.index = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### rf = RandomForestRegressor()\n",
    "rf_model = rf.fit(xtrain,ytrain)\n",
    "r2 = rf_model.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = rf_model.predict(xtrain)\n",
    "ypred2 = rf_model.predict(xtest)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(ytrain,ypred1))\n",
    "\n",
    "rmse2 = np.sqrt(mean_squared_error(ytest,ypred2))\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score13 = pd.DataFrame({'Model Name':['RF'],'Alpha':['-'],\n",
    "                      'L1_ratio':['-'],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score13)\n",
    "results.index = [0,1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENet_GridSearch_transformedall</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480980</td>\n",
       "      <td>36.390854</td>\n",
       "      <td>36.257514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENet_GS_withoutyear</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.578986</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KNN_regressor</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.659878</td>\n",
       "      <td>29.458772</td>\n",
       "      <td>36.390447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DT</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.138860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RF</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.976949</td>\n",
       "      <td>7.669037</td>\n",
       "      <td>20.462954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0               LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1    LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2       LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                            Ridge     1      NIL  0.519629   35.010000   \n",
       "4                            Lasso     1      NIL  0.519629   35.010000   \n",
       "5                      Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                  ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7         ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "8   ENet_GridSearch_transformedall  0.01        0  0.480980   36.390854   \n",
       "9              ENet_GS_withoutyear  0.01        0  0.578986    0.660000   \n",
       "10                   KNN_regressor     -        -  0.659878   29.458772   \n",
       "11                              DT     -        -  1.000000    0.000000   \n",
       "12                              RF     -        -  0.976949    7.669037   \n",
       "\n",
       "    RMSE_TEST  \n",
       "0   21.827985  \n",
       "1   39.450000  \n",
       "2   34.880000  \n",
       "3   34.880000  \n",
       "4   34.880000  \n",
       "5   34.880000  \n",
       "6   36.030000  \n",
       "7    0.656486  \n",
       "8   36.257514  \n",
       "9    0.660000  \n",
       "10  36.390447  \n",
       "11  29.138860  \n",
       "12  20.462954  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train:  32.85510773986312\n",
      "RMSE test:  32.83415843306275\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENet_GridSearch_transformedall</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480980</td>\n",
       "      <td>36.390854</td>\n",
       "      <td>36.257514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENet_GS_withoutyear</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.578986</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KNN_regressor</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.659878</td>\n",
       "      <td>29.458772</td>\n",
       "      <td>36.390447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DT</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.138860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RF</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.976949</td>\n",
       "      <td>7.669037</td>\n",
       "      <td>20.462954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.976949</td>\n",
       "      <td>7.669037</td>\n",
       "      <td>20.462954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0               LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1    LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2       LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                            Ridge     1      NIL  0.519629   35.010000   \n",
       "4                            Lasso     1      NIL  0.519629   35.010000   \n",
       "5                      Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                  ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7         ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "8   ENet_GridSearch_transformedall  0.01        0  0.480980   36.390854   \n",
       "9              ENet_GS_withoutyear  0.01        0  0.578986    0.660000   \n",
       "10                   KNN_regressor     -        -  0.659878   29.458772   \n",
       "11                              DT     -        -  1.000000    0.000000   \n",
       "12                              RF     -        -  0.976949    7.669037   \n",
       "13                              RF     -        -  0.976949    7.669037   \n",
       "\n",
       "    RMSE_TEST  \n",
       "0   21.827985  \n",
       "1   39.450000  \n",
       "2   34.880000  \n",
       "3   34.880000  \n",
       "4   34.880000  \n",
       "5   34.880000  \n",
       "6   36.030000  \n",
       "7    0.656486  \n",
       "8   36.257514  \n",
       "9    0.660000  \n",
       "10  36.390447  \n",
       "11  29.138860  \n",
       "12  20.462954  \n",
       "13  20.462954  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = GradientBoostingRegressor()\n",
    "\n",
    "gb_model = gb.fit(xtrain,ytrain)\n",
    "r2 = gb_model.score(xtrain,ytrain)\n",
    "\n",
    "ypred1 = gb_model.predict(xtrain)\n",
    "ypred2 = gb_model.predict(xtest)\n",
    "\n",
    "rmse1 = np.sqrt(mean_squared_error(ytrain,ypred1))\n",
    "\n",
    "rmse2 = np.sqrt(mean_squared_error(ytest,ypred2))\n",
    "\n",
    "print(\"RMSE train: \",rmse1)\n",
    "print(\"RMSE test: \",rmse2)\n",
    "\n",
    "score14 = pd.DataFrame({'Model Name':['GB'],'Alpha':['-'],\n",
    "                      'L1_ratio':['-'],'R2':r2,\n",
    "                      'RMSE_TRAIN':rmse1,'RMSE_TEST':rmse2})\n",
    "#score\n",
    "results = results.append(score13)\n",
    "results.index = [0,1,2,3,4,5,6,7,8,9,10,11,12,13]\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.iloc[13,0] = 'GB'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Alpha</th>\n",
       "      <th>L1_ratio</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSE_TRAIN</th>\n",
       "      <th>RMSE_TEST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinReg_without_VIF</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>21.797850</td>\n",
       "      <td>21.827985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinReg_with_VIF_without categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.387000</td>\n",
       "      <td>39.560000</td>\n",
       "      <td>39.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinReg_with_VIF_with_categ</td>\n",
       "      <td>NIL</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1</td>\n",
       "      <td>NIL</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elastic_Net</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.519629</td>\n",
       "      <td>35.010000</td>\n",
       "      <td>34.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ENet_GridSearch</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.487257</td>\n",
       "      <td>36.170000</td>\n",
       "      <td>36.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ENet_GridSearch_transf-y</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.585290</td>\n",
       "      <td>0.657757</td>\n",
       "      <td>0.656486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ENet_GridSearch_transformedall</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.480980</td>\n",
       "      <td>36.390854</td>\n",
       "      <td>36.257514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ENet_GS_withoutyear</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.578986</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>KNN_regressor</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.659878</td>\n",
       "      <td>29.458772</td>\n",
       "      <td>36.390447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DT</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.138860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RF</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.976949</td>\n",
       "      <td>7.669037</td>\n",
       "      <td>20.462954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.976949</td>\n",
       "      <td>7.669037</td>\n",
       "      <td>20.462954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model Name Alpha L1_ratio        R2  RMSE_TRAIN  \\\n",
       "0               LinReg_without_VIF   NIL      NIL  0.814000   21.797850   \n",
       "1    LinReg_with_VIF_without categ   NIL      NIL  0.387000   39.560000   \n",
       "2       LinReg_with_VIF_with_categ   NIL      NIL  0.520000   35.010000   \n",
       "3                            Ridge     1      NIL  0.519629   35.010000   \n",
       "4                            Lasso     1      NIL  0.519629   35.010000   \n",
       "5                      Elastic_Net     1      0.5  0.519629   35.010000   \n",
       "6                  ENet_GridSearch  0.01        0  0.487257   36.170000   \n",
       "7         ENet_GridSearch_transf-y  0.01        0  0.585290    0.657757   \n",
       "8   ENet_GridSearch_transformedall  0.01        0  0.480980   36.390854   \n",
       "9              ENet_GS_withoutyear  0.01        0  0.578986    0.660000   \n",
       "10                   KNN_regressor     -        -  0.659878   29.458772   \n",
       "11                              DT     -        -  1.000000    0.000000   \n",
       "12                              RF     -        -  0.976949    7.669037   \n",
       "13                              GB     -        -  0.976949    7.669037   \n",
       "\n",
       "    RMSE_TEST  \n",
       "0   21.827985  \n",
       "1   39.450000  \n",
       "2   34.880000  \n",
       "3   34.880000  \n",
       "4   34.880000  \n",
       "5   34.880000  \n",
       "6   36.030000  \n",
       "7    0.656486  \n",
       "8   36.257514  \n",
       "9    0.660000  \n",
       "10  36.390447  \n",
       "11  29.138860  \n",
       "12  20.462954  \n",
       "13  20.462954  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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