ARQ-Predictions-NOx(GT)-FE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from fbprophet import Prophet\n",
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn import linear_model as lm\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "\n",
    "data=pd.read_pickle('AQI-FE1-Binarized.pkl')\n",
    "dx=data[[ 'Month', 'Quarter', 'Dayofweek', 'HourofDay',\n",
    "       'DayofMonth', 'Dayoftheyear', 'Monday', 'Tuesday', 'Wednesday',\n",
    "       'Thursday', 'Friday', 'Saturday', 'Sunday','RH','AH','T','NOx(GT)']].copy(deep=True)\n",
    "dx.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Month</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Dayofweek</th>\n",
       "      <th>HourofDay</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>Dayoftheyear</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>RH</th>\n",
       "      <th>AH</th>\n",
       "      <th>T</th>\n",
       "      <th>NOx(GT)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "      <td>8349.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>6.444844</td>\n",
       "      <td>2.458498</td>\n",
       "      <td>2.996766</td>\n",
       "      <td>11.498383</td>\n",
       "      <td>17.205174</td>\n",
       "      <td>182.712181</td>\n",
       "      <td>0.145526</td>\n",
       "      <td>0.143730</td>\n",
       "      <td>0.140855</td>\n",
       "      <td>0.140855</td>\n",
       "      <td>0.143730</td>\n",
       "      <td>0.137981</td>\n",
       "      <td>0.137981</td>\n",
       "      <td>48.903821</td>\n",
       "      <td>1.028464</td>\n",
       "      <td>18.567703</td>\n",
       "      <td>231.276860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.397338</td>\n",
       "      <td>1.130917</td>\n",
       "      <td>2.011851</td>\n",
       "      <td>6.923302</td>\n",
       "      <td>8.294238</td>\n",
       "      <td>103.850001</td>\n",
       "      <td>0.352652</td>\n",
       "      <td>0.350837</td>\n",
       "      <td>0.347893</td>\n",
       "      <td>0.347893</td>\n",
       "      <td>0.350837</td>\n",
       "      <td>0.344900</td>\n",
       "      <td>0.344900</td>\n",
       "      <td>17.239478</td>\n",
       "      <td>0.393986</td>\n",
       "      <td>8.773010</td>\n",
       "      <td>199.108889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.184700</td>\n",
       "      <td>-1.900000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.700000</td>\n",
       "      <td>0.747500</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>49.300000</td>\n",
       "      <td>0.999674</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.753846</td>\n",
       "      <td>1.303500</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>304.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>88.700000</td>\n",
       "      <td>2.231000</td>\n",
       "      <td>44.600000</td>\n",
       "      <td>1479.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Month      Quarter    Dayofweek    HourofDay   DayofMonth  \\\n",
       "count  8349.000000  8349.000000  8349.000000  8349.000000  8349.000000   \n",
       "mean      6.444844     2.458498     2.996766    11.498383    17.205174   \n",
       "std       3.397338     1.130917     2.011851     6.923302     8.294238   \n",
       "min       1.000000     1.000000     0.000000     0.000000     1.000000   \n",
       "25%       3.000000     1.000000     1.000000     5.000000    10.000000   \n",
       "50%       6.000000     2.000000     3.000000    11.000000    18.000000   \n",
       "75%       9.000000     3.000000     5.000000    18.000000    24.000000   \n",
       "max      12.000000     4.000000     6.000000    23.000000    31.000000   \n",
       "\n",
       "       Dayoftheyear       Monday      Tuesday    Wednesday     Thursday  \\\n",
       "count   8349.000000  8349.000000  8349.000000  8349.000000  8349.000000   \n",
       "mean     182.712181     0.145526     0.143730     0.140855     0.140855   \n",
       "std      103.850001     0.352652     0.350837     0.347893     0.347893   \n",
       "min        1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       87.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%      177.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%      273.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max      366.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "            Friday     Saturday       Sunday           RH           AH  \\\n",
       "count  8349.000000  8349.000000  8349.000000  8349.000000  8349.000000   \n",
       "mean      0.143730     0.137981     0.137981    48.903821     1.028464   \n",
       "std       0.350837     0.344900     0.344900    17.239478     0.393986   \n",
       "min       0.000000     0.000000     0.000000     9.200000     0.184700   \n",
       "25%       0.000000     0.000000     0.000000    35.700000     0.747500   \n",
       "50%       0.000000     0.000000     0.000000    49.300000     0.999674   \n",
       "75%       0.000000     0.000000     0.000000    61.753846     1.303500   \n",
       "max       1.000000     1.000000     1.000000    88.700000     2.231000   \n",
       "\n",
       "                 T      NOx(GT)  \n",
       "count  8349.000000  8349.000000  \n",
       "mean     18.567703   231.276860  \n",
       "std       8.773010   199.108889  \n",
       "min      -1.900000     4.000000  \n",
       "25%      12.100000    93.000000  \n",
       "50%      18.000000   173.000000  \n",
       "75%      24.600000   304.000000  \n",
       "max      44.600000  1479.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dx.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['Friday']=data['Friday'].apply(lambda x: 1 if x==4 else 0)\n",
    "minmaxlist=[]\n",
    "for x1 in dx.columns:\n",
    "    minmaxlist.append([x1,dx[x1].min(),dx[x1].max()])\n",
    "    dx[x1]=dx[x1].apply(lambda x: (x-dx[x1].min())/(dx[x1].max()-dx[x1].min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "plt.figure(figsize=[20,8])\n",
    "plt.subplot(211)\n",
    "plot_acf(dx['NOx(GT)'].values, ax=plt.gca(),lags=range(1,80))\n",
    "plt.subplot(212)\n",
    "plot_pacf(dx['NOx(GT)'].values, ax=plt.gca(),lags=range(1,168))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20,8])\n",
    "plt.subplot(211)\n",
    "plot_acf(dx['NOx(GT)'].values, ax=plt.gca(),lags=range(1,25))\n",
    "plt.subplot(212)\n",
    "plot_pacf(dx['NOx(GT)'].values, ax=plt.gca(),lags=range(44\n",
    "                                                        ,100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12afab390>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from pandas.plotting import autocorrelation_plot,lag_plot\n",
    "autocorrelation_plot(dx['NOx(GT)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dx['shift-1']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=1)\n",
    "dx['shift-2']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=2)\n",
    "dx['shift-5']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=5)\n",
    "#dx['shift-3']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=3)\n",
    "dx['shift-6']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=6)\n",
    "dx['shift-7']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=7)\n",
    "#dx['shift-9']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=9)\n",
    "dx['shift-10']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=10)\n",
    "dx['shift-12']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=12)\n",
    "#dx['shift-13']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=13)\n",
    "#dx['shift-14']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=14)\n",
    "dx['shift-15']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=15)\n",
    "#dx['shift-16']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=16)\n",
    "dx['shift-17']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=17)\n",
    "dx['shift-18']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=18)\n",
    "dx['shift-19']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=19)\n",
    "dx['shift-20']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=20)\n",
    "dx['shift-21']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=21)\n",
    "dx['shift-22']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=22)\n",
    "dx['shift-23']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=23)\n",
    "dx['shift-24']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=24)\n",
    "dx['shift-25']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=25)\n",
    "dx['shift-26']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=26)\n",
    "dx['shift-27']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=27)\n",
    "dx['shift-44']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=44)\n",
    "dx['shift-45']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=45)\n",
    "dx['shift-46']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=46)\n",
    "dx['shift-47']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=47)\n",
    "#dx['shift-48']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=48)\n",
    "dx['shift-49']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=49)\n",
    "dx['shift-50']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=50)\n",
    "dx['shift-59']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=59)\n",
    "dx['shift-68']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=68)\n",
    "dx['shift-69']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=69)\n",
    "dx['shift-70']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=70)\n",
    "dx['shift-71']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=71)\n",
    "dx['shift-73']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=73)\n",
    "dx['shift-74']=dx['NOx(GT)'].loc[:'2005-3-1'].shift(periods=73)\n",
    "#dx['shift-145']=dx['T'].loc[:'2005-3-1'].shift(periods=145)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=dx.loc[:'2005-3-1'].copy(deep=True)\n",
    "train.dropna(inplace=True)\n",
    "test=dx.loc['2005-3-2':].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Quarter', 'Dayofweek', 'HourofDay', 'DayofMonth',\n",
       "       'Dayoftheyear', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',\n",
       "       'Saturday', 'Sunday', 'RH', 'AH', 'T', 'NOx(GT)', 'shift-1', 'shift-2',\n",
       "       'shift-5', 'shift-6', 'shift-7', 'shift-10', 'shift-12', 'shift-15',\n",
       "       'shift-17', 'shift-18', 'shift-19', 'shift-20', 'shift-21', 'shift-22',\n",
       "       'shift-23', 'shift-24', 'shift-25', 'shift-26', 'shift-27', 'shift-44',\n",
       "       'shift-45', 'shift-46', 'shift-47', 'shift-49', 'shift-50', 'shift-59',\n",
       "       'shift-68', 'shift-69', 'shift-70', 'shift-71', 'shift-73', 'shift-74'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#attb=['rolling-72','rolling-48','rolling-24','rolling-12']\n",
    "attb=[ 'RH','AH','T',\n",
    "    'HourofDay',\n",
    "        'Monday', 'Tuesday', 'Wednesday',\n",
    "       'Thursday', 'Friday', 'Saturday', 'Sunday',\n",
    "    'shift-1'\n",
    "      ,'shift-2','shift-5','shift-6','shift-7','shift-10','shift-12','shift-15','shift-18',\n",
    "      'shift-19','shift-20','shift-21','shift-22','shift-23','shift-24','shift-25','shift-26',\n",
    "      'shift-27','shift-44','shift-45','shift-46','shift-47','shift-49','shift-50','shift-59',\n",
    "      'shift-68','shift-69','shift-70','shift-71','shift-73','shift-74'\n",
    "     ]\n",
    "RM = lm.LinearRegression()\n",
    "#RM.fit(train[attb],train['NOx(GT)'])\n",
    "RM.fit(train[attb].loc['2005-2-24':],train['NOx(GT)'].loc['2005-2-24':])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "615"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "window=train['NOx(GT)'].loc[\"2005-2-20\":].values.tolist()\n",
    "window[-12:]\n",
    "dta_lst=[]\n",
    "ctr=0\n",
    "for x in test.index:\n",
    "    lst=[test['RH'].loc[x],test['AH'].loc[x],test['T'].loc[x],\n",
    "        #test['Month'].loc[x],\n",
    "         #test['DayofMonth'].loc[x],\n",
    "         test['HourofDay'].loc[x],test['Monday'].loc[x],\n",
    "         test['Tuesday'].loc[x],test['Wednesday'].loc[x],test['Thursday'].loc[x],\n",
    "         test['Friday'].loc[x],test['Saturday'].loc[x],test['Sunday'].loc[x],\n",
    "        window[-1],window[-2],window[-5],window[-6],window[-7],window[-10],window[-12],window[-15],window[-18],\n",
    "         window[-19],window[-20],window[-21],window[-22],window[-23],window[-24],window[-25],\n",
    "         window[-26],window[-27],window[-44],window[-45],window[-46],window[-47],window[-49],\n",
    "         window[-50],window[-59],window[-68],window[-69],window[-70],window[-71],window[-73],\n",
    "         window[-74]\n",
    "        ]\n",
    "    \n",
    "    pred=RM.predict(np.asarray([lst]).reshape(1,-42))\n",
    "    window.append(pred[0])\n",
    "    lst.append(pred[0])\n",
    "    dta_lst.append(lst)\n",
    "\n",
    "#train['T'].loc[\"2005-2-27\":]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "collist=[ 'RH','AH','T',\n",
    "    'HourofDay',\n",
    "        'Monday', 'Tuesday', 'Wednesday',\n",
    "       'Thursday', 'Friday', 'Saturday', 'Sunday',\n",
    "    'shift-1'\n",
    "         ,'shift-2','shift-5','shift-6','shift-7','shift-10','shift-12','shift-15','shift-18',\n",
    "      'shift-19','shift-20','shift-21','shift-22','shift-23','shift-24','shift-25','shift-26',\n",
    "      'shift-27','shift-44','shift-45','shift-46','shift-47','shift-49','shift-50','shift-59',\n",
    "      'shift-68','shift-69','shift-70','shift-71','shift-73','shift-74',\n",
    "         'Prediction']\n",
    "prediction=pd.DataFrame(dta_lst,columns=collist,dtype='float')\n",
    "prediction.index=test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['shift-1']=prediction['shift-1'].copy(deep=True)\n",
    "test['shift-2']=prediction['shift-2'].copy(deep=True)\n",
    "#test['shift-3']=prediction['shift-3'].copy(deep=True)\n",
    "test['Prediction']=prediction['Prediction'].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1304bdd10>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "#finding trend using rolling window operation\n",
    "test.plot(y='NOx(GT)',ax=ax,style='.-')\n",
    "test.plot(y='Prediction',ax=ax,style='.-')\n",
    "\n"
   ]
  },
  {
   "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>NOx(GT)</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2005-03-02 00:00:00</td>\n",
       "      <td>0.161356</td>\n",
       "      <td>0.159827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 01:00:00</td>\n",
       "      <td>0.161356</td>\n",
       "      <td>0.164019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 02:00:00</td>\n",
       "      <td>0.127458</td>\n",
       "      <td>0.137327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 03:00:00</td>\n",
       "      <td>0.107797</td>\n",
       "      <td>0.149417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 04:00:00</td>\n",
       "      <td>0.088136</td>\n",
       "      <td>0.117571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 05:00:00</td>\n",
       "      <td>0.093559</td>\n",
       "      <td>0.068707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 06:00:00</td>\n",
       "      <td>0.128814</td>\n",
       "      <td>0.076009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 07:00:00</td>\n",
       "      <td>0.143729</td>\n",
       "      <td>0.157698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 08:00:00</td>\n",
       "      <td>0.490847</td>\n",
       "      <td>0.245482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 09:00:00</td>\n",
       "      <td>0.658305</td>\n",
       "      <td>0.259660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 10:00:00</td>\n",
       "      <td>0.639322</td>\n",
       "      <td>0.208366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 11:00:00</td>\n",
       "      <td>0.532881</td>\n",
       "      <td>0.145160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 12:00:00</td>\n",
       "      <td>0.370847</td>\n",
       "      <td>0.096861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 13:00:00</td>\n",
       "      <td>0.380339</td>\n",
       "      <td>0.041038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 14:00:00</td>\n",
       "      <td>0.357966</td>\n",
       "      <td>0.012355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 15:00:00</td>\n",
       "      <td>0.308475</td>\n",
       "      <td>0.001860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 16:00:00</td>\n",
       "      <td>0.273220</td>\n",
       "      <td>0.022259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 17:00:00</td>\n",
       "      <td>0.291525</td>\n",
       "      <td>0.104336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 18:00:00</td>\n",
       "      <td>0.661695</td>\n",
       "      <td>0.238887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 19:00:00</td>\n",
       "      <td>0.652203</td>\n",
       "      <td>0.333637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 20:00:00</td>\n",
       "      <td>0.435254</td>\n",
       "      <td>0.368069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 21:00:00</td>\n",
       "      <td>0.316610</td>\n",
       "      <td>0.324353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 22:00:00</td>\n",
       "      <td>0.355254</td>\n",
       "      <td>0.225962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 23:00:00</td>\n",
       "      <td>0.253559</td>\n",
       "      <td>0.189300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 00:00:00</td>\n",
       "      <td>0.137627</td>\n",
       "      <td>0.212201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 01:00:00</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.209359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 02:00:00</td>\n",
       "      <td>0.102373</td>\n",
       "      <td>0.190563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 03:00:00</td>\n",
       "      <td>0.110508</td>\n",
       "      <td>0.181114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 04:00:00</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.131703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 05:00:00</td>\n",
       "      <td>0.094237</td>\n",
       "      <td>0.089060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 06:00:00</td>\n",
       "      <td>0.141017</td>\n",
       "      <td>0.100126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 07:00:00</td>\n",
       "      <td>0.175593</td>\n",
       "      <td>0.201163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 08:00:00</td>\n",
       "      <td>0.436610</td>\n",
       "      <td>0.297262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 09:00:00</td>\n",
       "      <td>0.483390</td>\n",
       "      <td>0.293841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 10:00:00</td>\n",
       "      <td>0.261017</td>\n",
       "      <td>0.233179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 11:00:00</td>\n",
       "      <td>0.294237</td>\n",
       "      <td>0.205786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 12:00:00</td>\n",
       "      <td>0.188475</td>\n",
       "      <td>0.181427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 13:00:00</td>\n",
       "      <td>0.178983</td>\n",
       "      <td>0.141212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 14:00:00</td>\n",
       "      <td>0.165424</td>\n",
       "      <td>0.097631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 15:00:00</td>\n",
       "      <td>0.181695</td>\n",
       "      <td>0.066532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 16:00:00</td>\n",
       "      <td>0.194576</td>\n",
       "      <td>0.070046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 17:00:00</td>\n",
       "      <td>0.243390</td>\n",
       "      <td>0.115448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 18:00:00</td>\n",
       "      <td>0.444746</td>\n",
       "      <td>0.215865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 19:00:00</td>\n",
       "      <td>0.612203</td>\n",
       "      <td>0.297230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 20:00:00</td>\n",
       "      <td>0.430508</td>\n",
       "      <td>0.328643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 21:00:00</td>\n",
       "      <td>0.386441</td>\n",
       "      <td>0.327659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 22:00:00</td>\n",
       "      <td>0.250169</td>\n",
       "      <td>0.316827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 23:00:00</td>\n",
       "      <td>0.221017</td>\n",
       "      <td>0.296734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      NOx(GT)  Prediction\n",
       "2005-03-02 00:00:00  0.161356    0.159827\n",
       "2005-03-02 01:00:00  0.161356    0.164019\n",
       "2005-03-02 02:00:00  0.127458    0.137327\n",
       "2005-03-02 03:00:00  0.107797    0.149417\n",
       "2005-03-02 04:00:00  0.088136    0.117571\n",
       "2005-03-02 05:00:00  0.093559    0.068707\n",
       "2005-03-02 06:00:00  0.128814    0.076009\n",
       "2005-03-02 07:00:00  0.143729    0.157698\n",
       "2005-03-02 08:00:00  0.490847    0.245482\n",
       "2005-03-02 09:00:00  0.658305    0.259660\n",
       "2005-03-02 10:00:00  0.639322    0.208366\n",
       "2005-03-02 11:00:00  0.532881    0.145160\n",
       "2005-03-02 12:00:00  0.370847    0.096861\n",
       "2005-03-02 13:00:00  0.380339    0.041038\n",
       "2005-03-02 14:00:00  0.357966    0.012355\n",
       "2005-03-02 15:00:00  0.308475    0.001860\n",
       "2005-03-02 16:00:00  0.273220    0.022259\n",
       "2005-03-02 17:00:00  0.291525    0.104336\n",
       "2005-03-02 18:00:00  0.661695    0.238887\n",
       "2005-03-02 19:00:00  0.652203    0.333637\n",
       "2005-03-02 20:00:00  0.435254    0.368069\n",
       "2005-03-02 21:00:00  0.316610    0.324353\n",
       "2005-03-02 22:00:00  0.355254    0.225962\n",
       "2005-03-02 23:00:00  0.253559    0.189300\n",
       "2005-03-03 00:00:00  0.137627    0.212201\n",
       "2005-03-03 01:00:00  0.135593    0.209359\n",
       "2005-03-03 02:00:00  0.102373    0.190563\n",
       "2005-03-03 03:00:00  0.110508    0.181114\n",
       "2005-03-03 04:00:00  0.118644    0.131703\n",
       "2005-03-03 05:00:00  0.094237    0.089060\n",
       "2005-03-03 06:00:00  0.141017    0.100126\n",
       "2005-03-03 07:00:00  0.175593    0.201163\n",
       "2005-03-03 08:00:00  0.436610    0.297262\n",
       "2005-03-03 09:00:00  0.483390    0.293841\n",
       "2005-03-03 10:00:00  0.261017    0.233179\n",
       "2005-03-03 11:00:00  0.294237    0.205786\n",
       "2005-03-03 12:00:00  0.188475    0.181427\n",
       "2005-03-03 13:00:00  0.178983    0.141212\n",
       "2005-03-03 14:00:00  0.165424    0.097631\n",
       "2005-03-03 15:00:00  0.181695    0.066532\n",
       "2005-03-03 16:00:00  0.194576    0.070046\n",
       "2005-03-03 17:00:00  0.243390    0.115448\n",
       "2005-03-03 18:00:00  0.444746    0.215865\n",
       "2005-03-03 19:00:00  0.612203    0.297230\n",
       "2005-03-03 20:00:00  0.430508    0.328643\n",
       "2005-03-03 21:00:00  0.386441    0.327659\n",
       "2005-03-03 22:00:00  0.250169    0.316827\n",
       "2005-03-03 23:00:00  0.221017    0.296734"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[['NOx(GT)','Prediction']].loc['2005-3-2':'2005-3-3']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028640585026781067"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(test['NOx(GT)'].loc['2005-3-2':'2005-3-8'], test['Prediction'].loc['2005-3-2':'2005-3-8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10804798210873519"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(test['NOx(GT)'], test['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.04262327013987899"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test['NOx(GT)'].loc['2005-3-2':'2005-3-8'], test['Prediction'].loc['2005-3-2':'2005-3-8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.293947912979451"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test['NOx(GT)'], test['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fbprophet import Prophet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x130d23810>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attb=[ 'RH','AH','T',\n",
    "    'HourofDay',\n",
    "        #'Monday', 'Tuesday', 'Wednesday',\n",
    "       #'Thursday', 'Friday', 'Saturday', 'Sunday',\n",
    "    'shift-1'\n",
    "      ,'shift-2','shift-5','shift-6','shift-7','shift-10','shift-12','shift-15','shift-18',\n",
    "      'shift-19','shift-20','shift-21','shift-22','shift-23','shift-24','shift-25','shift-26',\n",
    "      'shift-27','shift-44','shift-45','shift-46','shift-47','shift-49','shift-50','shift-59',\n",
    "      'shift-68','shift-69','shift-70','shift-71','shift-73','shift-74'\n",
    "     ]\n",
    "reg2 = RandomForestRegressor(random_state=1, n_estimators=10,max_depth=10,\n",
    "                             min_samples_leaf=3,min_samples_split=2)\n",
    "reg2.fit(train[attb].loc['2005-2-22':],train['NOx(GT)'].loc['2005-2-22':])\n",
    "window=train['NOx(GT)'].values.tolist()\n",
    "window[-12:]\n",
    "dta_lst=[]\n",
    "ctr=0\n",
    "for x in test.index:\n",
    "    lst=[test['RH'].loc[x],test['AH'].loc[x],test['T'].loc[x],\n",
    "        #test['Month'].loc[x],\n",
    "         #test['DayofMonth'].loc[x],\n",
    "         test['HourofDay'].loc[x],\n",
    "         #test['Monday'].loc[x],\n",
    "         #test['Tuesday'].loc[x],test['Wednesday'].loc[x],test['Thursday'].loc[x],\n",
    "         #test['Friday'].loc[x],test['Saturday'].loc[x],test['Sunday'].loc[x],\n",
    "         window[-1],window[-2],window[-5],window[-6],window[-7],window[-10],window[-12],window[-15],window[-18],\n",
    "         window[-19],window[-20],window[-21],window[-22],window[-23],window[-24],window[-25],\n",
    "         window[-26],window[-27],window[-44],window[-45],window[-46],window[-47],window[-49],\n",
    "         window[-50],window[-59],window[-68],window[-69],window[-70],window[-71],window[-73],\n",
    "         window[-74]\n",
    "        ]\n",
    "    \n",
    "    pred=reg2.predict(np.asarray([lst]).reshape(1,-42))\n",
    "    window.append(pred[0])\n",
    "    lst.append(pred[0])\n",
    "    dta_lst.append(lst)\n",
    "\n",
    "collist=[ 'RH','AH','T',\n",
    "    'HourofDay',\n",
    "        #'Monday', 'Tuesday', 'Wednesday',\n",
    "       #'Thursday', 'Friday', 'Saturday', 'Sunday',\n",
    "    'shift-1'\n",
    "         ,'shift-2','shift-5','shift-6','shift-7','shift-10','shift-12','shift-15','shift-18',\n",
    "      'shift-19','shift-20','shift-21','shift-22','shift-23','shift-24','shift-25','shift-26',\n",
    "      'shift-27','shift-44','shift-45','shift-46','shift-47','shift-49','shift-50','shift-59',\n",
    "      'shift-68','shift-69','shift-70','shift-71','shift-73','shift-74',\n",
    "         'Prediction']\n",
    "prediction=pd.DataFrame(dta_lst,columns=collist,dtype='float')\n",
    "prediction.index=test.index\n",
    "test['Prediction-RF']=prediction['Prediction'].copy(deep=True)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#finding trend using rolling window operation\n",
    "test.plot(y='NOx(GT)',ax=ax)\n",
    "test.plot(y='Prediction-RF',ax=ax,style='.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04422754588526598"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(test['NOx(GT)'].loc['2005-3-2':'2005-3-2'], test['Prediction-RF'].loc['2005-3-2':'2005-3-2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.25043670379985605"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test['NOx(GT)'].loc['2005-3-2':'2005-3-2'], test['Prediction-RF'].loc['2005-3-2':'2005-3-2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>NOx(GT)</th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Prediction-RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2005-03-02 00:00:00</td>\n",
       "      <td>0.161356</td>\n",
       "      <td>0.159827</td>\n",
       "      <td>0.192246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 01:00:00</td>\n",
       "      <td>0.161356</td>\n",
       "      <td>0.164019</td>\n",
       "      <td>0.208730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 02:00:00</td>\n",
       "      <td>0.127458</td>\n",
       "      <td>0.137327</td>\n",
       "      <td>0.225054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 03:00:00</td>\n",
       "      <td>0.107797</td>\n",
       "      <td>0.149417</td>\n",
       "      <td>0.197708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 04:00:00</td>\n",
       "      <td>0.088136</td>\n",
       "      <td>0.117571</td>\n",
       "      <td>0.198210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 05:00:00</td>\n",
       "      <td>0.093559</td>\n",
       "      <td>0.068707</td>\n",
       "      <td>0.199122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 06:00:00</td>\n",
       "      <td>0.128814</td>\n",
       "      <td>0.076009</td>\n",
       "      <td>0.248867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 07:00:00</td>\n",
       "      <td>0.143729</td>\n",
       "      <td>0.157698</td>\n",
       "      <td>0.265267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 08:00:00</td>\n",
       "      <td>0.490847</td>\n",
       "      <td>0.245482</td>\n",
       "      <td>0.266553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 09:00:00</td>\n",
       "      <td>0.658305</td>\n",
       "      <td>0.259660</td>\n",
       "      <td>0.232260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 10:00:00</td>\n",
       "      <td>0.639322</td>\n",
       "      <td>0.208366</td>\n",
       "      <td>0.214576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 11:00:00</td>\n",
       "      <td>0.532881</td>\n",
       "      <td>0.145160</td>\n",
       "      <td>0.233052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 12:00:00</td>\n",
       "      <td>0.370847</td>\n",
       "      <td>0.096861</td>\n",
       "      <td>0.214399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 13:00:00</td>\n",
       "      <td>0.380339</td>\n",
       "      <td>0.041038</td>\n",
       "      <td>0.221013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 14:00:00</td>\n",
       "      <td>0.357966</td>\n",
       "      <td>0.012355</td>\n",
       "      <td>0.209835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 15:00:00</td>\n",
       "      <td>0.308475</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.230783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 16:00:00</td>\n",
       "      <td>0.273220</td>\n",
       "      <td>0.022259</td>\n",
       "      <td>0.238490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 17:00:00</td>\n",
       "      <td>0.291525</td>\n",
       "      <td>0.104336</td>\n",
       "      <td>0.243805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 18:00:00</td>\n",
       "      <td>0.661695</td>\n",
       "      <td>0.238887</td>\n",
       "      <td>0.247517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 19:00:00</td>\n",
       "      <td>0.652203</td>\n",
       "      <td>0.333637</td>\n",
       "      <td>0.243415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 20:00:00</td>\n",
       "      <td>0.435254</td>\n",
       "      <td>0.368069</td>\n",
       "      <td>0.224307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 21:00:00</td>\n",
       "      <td>0.316610</td>\n",
       "      <td>0.324353</td>\n",
       "      <td>0.222851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 22:00:00</td>\n",
       "      <td>0.355254</td>\n",
       "      <td>0.225962</td>\n",
       "      <td>0.243218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-02 23:00:00</td>\n",
       "      <td>0.253559</td>\n",
       "      <td>0.189300</td>\n",
       "      <td>0.237307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 00:00:00</td>\n",
       "      <td>0.137627</td>\n",
       "      <td>0.212201</td>\n",
       "      <td>0.223464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 01:00:00</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.209359</td>\n",
       "      <td>0.239841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 02:00:00</td>\n",
       "      <td>0.102373</td>\n",
       "      <td>0.190563</td>\n",
       "      <td>0.230584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 03:00:00</td>\n",
       "      <td>0.110508</td>\n",
       "      <td>0.181114</td>\n",
       "      <td>0.233256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 04:00:00</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.131703</td>\n",
       "      <td>0.209911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 05:00:00</td>\n",
       "      <td>0.094237</td>\n",
       "      <td>0.089060</td>\n",
       "      <td>0.229048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 06:00:00</td>\n",
       "      <td>0.141017</td>\n",
       "      <td>0.100126</td>\n",
       "      <td>0.238558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 07:00:00</td>\n",
       "      <td>0.175593</td>\n",
       "      <td>0.201163</td>\n",
       "      <td>0.246626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 08:00:00</td>\n",
       "      <td>0.436610</td>\n",
       "      <td>0.297262</td>\n",
       "      <td>0.229805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 09:00:00</td>\n",
       "      <td>0.483390</td>\n",
       "      <td>0.293841</td>\n",
       "      <td>0.256105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 10:00:00</td>\n",
       "      <td>0.261017</td>\n",
       "      <td>0.233179</td>\n",
       "      <td>0.267027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 11:00:00</td>\n",
       "      <td>0.294237</td>\n",
       "      <td>0.205786</td>\n",
       "      <td>0.229850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 12:00:00</td>\n",
       "      <td>0.188475</td>\n",
       "      <td>0.181427</td>\n",
       "      <td>0.237912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 13:00:00</td>\n",
       "      <td>0.178983</td>\n",
       "      <td>0.141212</td>\n",
       "      <td>0.228612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 14:00:00</td>\n",
       "      <td>0.165424</td>\n",
       "      <td>0.097631</td>\n",
       "      <td>0.239972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 15:00:00</td>\n",
       "      <td>0.181695</td>\n",
       "      <td>0.066532</td>\n",
       "      <td>0.240103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 16:00:00</td>\n",
       "      <td>0.194576</td>\n",
       "      <td>0.070046</td>\n",
       "      <td>0.260935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 17:00:00</td>\n",
       "      <td>0.243390</td>\n",
       "      <td>0.115448</td>\n",
       "      <td>0.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 18:00:00</td>\n",
       "      <td>0.444746</td>\n",
       "      <td>0.215865</td>\n",
       "      <td>0.276910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 19:00:00</td>\n",
       "      <td>0.612203</td>\n",
       "      <td>0.297230</td>\n",
       "      <td>0.274016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 20:00:00</td>\n",
       "      <td>0.430508</td>\n",
       "      <td>0.328643</td>\n",
       "      <td>0.258991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 21:00:00</td>\n",
       "      <td>0.386441</td>\n",
       "      <td>0.327659</td>\n",
       "      <td>0.277822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 22:00:00</td>\n",
       "      <td>0.250169</td>\n",
       "      <td>0.316827</td>\n",
       "      <td>0.285862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005-03-03 23:00:00</td>\n",
       "      <td>0.221017</td>\n",
       "      <td>0.296734</td>\n",
       "      <td>0.270345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      NOx(GT)  Prediction  Prediction-RF\n",
       "2005-03-02 00:00:00  0.161356    0.159827       0.192246\n",
       "2005-03-02 01:00:00  0.161356    0.164019       0.208730\n",
       "2005-03-02 02:00:00  0.127458    0.137327       0.225054\n",
       "2005-03-02 03:00:00  0.107797    0.149417       0.197708\n",
       "2005-03-02 04:00:00  0.088136    0.117571       0.198210\n",
       "2005-03-02 05:00:00  0.093559    0.068707       0.199122\n",
       "2005-03-02 06:00:00  0.128814    0.076009       0.248867\n",
       "2005-03-02 07:00:00  0.143729    0.157698       0.265267\n",
       "2005-03-02 08:00:00  0.490847    0.245482       0.266553\n",
       "2005-03-02 09:00:00  0.658305    0.259660       0.232260\n",
       "2005-03-02 10:00:00  0.639322    0.208366       0.214576\n",
       "2005-03-02 11:00:00  0.532881    0.145160       0.233052\n",
       "2005-03-02 12:00:00  0.370847    0.096861       0.214399\n",
       "2005-03-02 13:00:00  0.380339    0.041038       0.221013\n",
       "2005-03-02 14:00:00  0.357966    0.012355       0.209835\n",
       "2005-03-02 15:00:00  0.308475    0.001860       0.230783\n",
       "2005-03-02 16:00:00  0.273220    0.022259       0.238490\n",
       "2005-03-02 17:00:00  0.291525    0.104336       0.243805\n",
       "2005-03-02 18:00:00  0.661695    0.238887       0.247517\n",
       "2005-03-02 19:00:00  0.652203    0.333637       0.243415\n",
       "2005-03-02 20:00:00  0.435254    0.368069       0.224307\n",
       "2005-03-02 21:00:00  0.316610    0.324353       0.222851\n",
       "2005-03-02 22:00:00  0.355254    0.225962       0.243218\n",
       "2005-03-02 23:00:00  0.253559    0.189300       0.237307\n",
       "2005-03-03 00:00:00  0.137627    0.212201       0.223464\n",
       "2005-03-03 01:00:00  0.135593    0.209359       0.239841\n",
       "2005-03-03 02:00:00  0.102373    0.190563       0.230584\n",
       "2005-03-03 03:00:00  0.110508    0.181114       0.233256\n",
       "2005-03-03 04:00:00  0.118644    0.131703       0.209911\n",
       "2005-03-03 05:00:00  0.094237    0.089060       0.229048\n",
       "2005-03-03 06:00:00  0.141017    0.100126       0.238558\n",
       "2005-03-03 07:00:00  0.175593    0.201163       0.246626\n",
       "2005-03-03 08:00:00  0.436610    0.297262       0.229805\n",
       "2005-03-03 09:00:00  0.483390    0.293841       0.256105\n",
       "2005-03-03 10:00:00  0.261017    0.233179       0.267027\n",
       "2005-03-03 11:00:00  0.294237    0.205786       0.229850\n",
       "2005-03-03 12:00:00  0.188475    0.181427       0.237912\n",
       "2005-03-03 13:00:00  0.178983    0.141212       0.228612\n",
       "2005-03-03 14:00:00  0.165424    0.097631       0.239972\n",
       "2005-03-03 15:00:00  0.181695    0.066532       0.240103\n",
       "2005-03-03 16:00:00  0.194576    0.070046       0.260935\n",
       "2005-03-03 17:00:00  0.243390    0.115448       0.279999\n",
       "2005-03-03 18:00:00  0.444746    0.215865       0.276910\n",
       "2005-03-03 19:00:00  0.612203    0.297230       0.274016\n",
       "2005-03-03 20:00:00  0.430508    0.328643       0.258991\n",
       "2005-03-03 21:00:00  0.386441    0.327659       0.277822\n",
       "2005-03-03 22:00:00  0.250169    0.316827       0.285862\n",
       "2005-03-03 23:00:00  0.221017    0.296734       0.270345"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[['NOx(GT)','Prediction','Prediction-RF']].loc['2005-3-2':'2005-3-3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-2f71b8cf4547>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mattb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Prediction'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/projects/projects/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    219\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \"\"\"\n\u001b[0;32m--> 221\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/projects/projects/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m         return safe_sparse_dot(X, self.coef_.T,\n\u001b[1;32m    206\u001b[0m                                dense_output=True) + self.intercept_\n",
      "\u001b[0;32m~/Documents/projects/projects/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 542\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/projects/projects/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan)\u001b[0m\n\u001b[1;32m     54\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m     55\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'object'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "pred = RM.predict(test[attb])\n",
    "pred=pd.DataFrame(pred,columns=['Prediction'])\n",
    "pred.index=test.index\n",
    "test1=pd.concat([test,pred],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "#finding trend using rolling window operation\n",
    "dx.plot(y='T',ax=ax)\n",
    "test1.plot(y='Prediction',ax=ax)\n",
    "dx.plot(y='T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared_error(test1['T'], test1['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_score(test1['T'], test1['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg2 = RandomForestRegressor(random_state=1, n_estimators=100,max_depth=30,min_samples_leaf=3,min_samples_split=5)\n",
    "reg2.fit(train[attb],train['T'])\n",
    "pred = reg2.predict(test[attb])\n",
    "pred=pd.DataFrame(pred,columns=['Prediction'])\n",
    "pred.index=test.index\n",
    "test1=pd.concat([test,pred],axis=1)\n",
    "fig, ax = plt.subplots()\n",
    "#finding trend using rolling window operation\n",
    "dx.plot(y='T',ax=ax)\n",
    "test1.plot(y='Prediction',ax=ax)\n",
    "dx.plot(y='T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_squared_error(test1['T'], test1['Prediction'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_score(test1['T'], test1['Prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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