Walmart-Weekly-Sales-Prediction.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {
    "id": "6_5PFNPG5H43"
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   "source": [
    "<b>Null Hypothesis:</b> There is no difference between the Performance of Random Forest Model, Elastic Net Regression, XGBoost,\n",
    "    and Gradient Boosting Model on Walmart Retail Data.<br>\n",
    "<b>Alternate Hypothesis:</b> There is a difference between the Performance of Random Forest Model, Elastic Net Regression, XGBoost,and Gradient Boosting Model on Walmart Retail Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u>PreProcessing Phase</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <u>Reading all the Files</u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "nSmEW9Hk5H5-"
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')\n",
    "feature = pd.read_csv('features.csv')\n",
    "stores = pd.read_csv('stores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "-LX_2EXL5H6A",
    "outputId": "98d19b36-c65d-4a61-e361-470267a1f237",
    "scrolled": true
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   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2010-02-05</td>\n",
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       "      <td>False</td>\n",
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       "      <td>2012-09-28</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>421566</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>628.10</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>421567</th>\n",
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       "      <td>2012-10-12</td>\n",
       "      <td>1061.02</td>\n",
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       "      <td>2012-10-19</td>\n",
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       "      <th>421569</th>\n",
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       "      <td>2012-10-26</td>\n",
       "      <td>1076.80</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>421570 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "        Store  Dept        Date  Weekly_Sales  IsHoliday\n",
       "0           1     1  2010-02-05      24924.50      False\n",
       "1           1     1  2010-02-12      46039.49       True\n",
       "2           1     1  2010-02-19      41595.55      False\n",
       "3           1     1  2010-02-26      19403.54      False\n",
       "4           1     1  2010-03-05      21827.90      False\n",
       "...       ...   ...         ...           ...        ...\n",
       "421565     45    98  2012-09-28        508.37      False\n",
       "421566     45    98  2012-10-05        628.10      False\n",
       "421567     45    98  2012-10-12       1061.02      False\n",
       "421568     45    98  2012-10-19        760.01      False\n",
       "421569     45    98  2012-10-26       1076.80      False\n",
       "\n",
       "[421570 rows x 5 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
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   "source": [
    "data"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8190 entries, 0 to 8189\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Store         8190 non-null   int64  \n",
      " 1   Date          8190 non-null   object \n",
      " 2   Temperature   8190 non-null   float64\n",
      " 3   Fuel_Price    8190 non-null   float64\n",
      " 4   MarkDown1     4032 non-null   float64\n",
      " 5   MarkDown2     2921 non-null   float64\n",
      " 6   MarkDown3     3613 non-null   float64\n",
      " 7   MarkDown4     3464 non-null   float64\n",
      " 8   MarkDown5     4050 non-null   float64\n",
      " 9   CPI           7605 non-null   float64\n",
      " 10  Unemployment  7605 non-null   float64\n",
      " 11  IsHoliday     8190 non-null   bool   \n",
      "dtypes: bool(1), float64(9), int64(1), object(1)\n",
      "memory usage: 712.0+ KB\n"
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   "source": [
    "feature.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <td>76.05</td>\n",
       "      <td>3.639</td>\n",
       "      <td>4842.29</td>\n",
       "      <td>975.03</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2449.97</td>\n",
       "      <td>3169.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>45</td>\n",
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       "      <td>77.50</td>\n",
       "      <td>3.614</td>\n",
       "      <td>9090.48</td>\n",
       "      <td>2268.58</td>\n",
       "      <td>582.74</td>\n",
       "      <td>5797.47</td>\n",
       "      <td>1514.93</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1047.07</td>\n",
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       "      <td>363.00</td>\n",
       "      <td>1059.46</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
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       "      <td>76.06</td>\n",
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       "      <td>212.02</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
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      "text/plain": [
       "      Store        Date  Temperature  Fuel_Price  MarkDown1  MarkDown2  \\\n",
       "0         1  2010-02-05        42.31       2.572        NaN        NaN   \n",
       "1         1  2010-02-12        38.51       2.548        NaN        NaN   \n",
       "2         1  2010-02-19        39.93       2.514        NaN        NaN   \n",
       "3         1  2010-02-26        46.63       2.561        NaN        NaN   \n",
       "4         1  2010-03-05        46.50       2.625        NaN        NaN   \n",
       "...     ...         ...          ...         ...        ...        ...   \n",
       "8185     45  2013-06-28        76.05       3.639    4842.29     975.03   \n",
       "8186     45  2013-07-05        77.50       3.614    9090.48    2268.58   \n",
       "8187     45  2013-07-12        79.37       3.614    3789.94    1827.31   \n",
       "8188     45  2013-07-19        82.84       3.737    2961.49    1047.07   \n",
       "8189     45  2013-07-26        76.06       3.804     212.02     851.73   \n",
       "\n",
       "      MarkDown3  MarkDown4  MarkDown5         CPI  Unemployment  IsHoliday  \n",
       "0           NaN        NaN        NaN  211.096358         8.106      False  \n",
       "1           NaN        NaN        NaN  211.242170         8.106       True  \n",
       "2           NaN        NaN        NaN  211.289143         8.106      False  \n",
       "3           NaN        NaN        NaN  211.319643         8.106      False  \n",
       "4           NaN        NaN        NaN  211.350143         8.106      False  \n",
       "...         ...        ...        ...         ...           ...        ...  \n",
       "8185       3.00    2449.97    3169.69         NaN           NaN      False  \n",
       "8186     582.74    5797.47    1514.93         NaN           NaN      False  \n",
       "8187      85.72     744.84    2150.36         NaN           NaN      False  \n",
       "8188     204.19     363.00    1059.46         NaN           NaN      False  \n",
       "8189       2.06      10.88    1864.57         NaN           NaN      False  \n",
       "\n",
       "[8190 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "eHW6MPTc5H6M"
   },
   "outputs": [],
   "source": [
    "feature = feature.merge(stores, on = 'Store')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <u>Data Cleaning</u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "jwW3AuAS5H6N"
   },
   "outputs": [],
   "source": [
    "# Converting to datetime Object\n",
    "feature['Date'] = pd.to_datetime(feature['Date'],format = '%Y-%m-%d')\n",
    "data['Date'] = pd.to_datetime(data['Date'],format = '%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "I1t_ukdo5H6N"
   },
   "outputs": [],
   "source": [
    "# Merging all the Data\n",
    "Train_detail = data.merge(feature, \n",
    "                           how='inner',on=['Store','Date','IsHoliday']).sort_values(by=['Store','Dept','Date']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>MarkDown3</th>\n",
       "      <th>MarkDown4</th>\n",
       "      <th>MarkDown5</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>False</td>\n",
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       "      <td>2.572</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2.548</td>\n",
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       "      <td>41595.55</td>\n",
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       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>19403.54</td>\n",
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       "      <td>2.561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.319643</td>\n",
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       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>False</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>421565</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>508.37</td>\n",
       "      <td>False</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>4556.61</td>\n",
       "      <td>20.64</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1601.01</td>\n",
       "      <td>3288.25</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
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       "    <tr>\n",
       "      <th>421566</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2012-10-05</td>\n",
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       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>18.82</td>\n",
       "      <td>2253.43</td>\n",
       "      <td>2340.01</td>\n",
       "      <td>192.170412</td>\n",
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       "    <tr>\n",
       "      <th>421567</th>\n",
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       "      <td>1061.02</td>\n",
       "      <td>False</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1956.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.89</td>\n",
       "      <td>599.32</td>\n",
       "      <td>3990.54</td>\n",
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       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>421568</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>760.01</td>\n",
       "      <td>False</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>2004.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>437.73</td>\n",
       "      <td>1537.49</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421569</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2012-10-26</td>\n",
       "      <td>1076.80</td>\n",
       "      <td>False</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>4018.91</td>\n",
       "      <td>58.08</td>\n",
       "      <td>100.00</td>\n",
       "      <td>211.94</td>\n",
       "      <td>858.33</td>\n",
       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>421570 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  \\\n",
       "0           1     1 2010-02-05      24924.50      False        42.31   \n",
       "1           1     1 2010-02-12      46039.49       True        38.51   \n",
       "2           1     1 2010-02-19      41595.55      False        39.93   \n",
       "3           1     1 2010-02-26      19403.54      False        46.63   \n",
       "4           1     1 2010-03-05      21827.90      False        46.50   \n",
       "...       ...   ...        ...           ...        ...          ...   \n",
       "421565     45    98 2012-09-28        508.37      False        64.88   \n",
       "421566     45    98 2012-10-05        628.10      False        64.89   \n",
       "421567     45    98 2012-10-12       1061.02      False        54.47   \n",
       "421568     45    98 2012-10-19        760.01      False        56.47   \n",
       "421569     45    98 2012-10-26       1076.80      False        58.85   \n",
       "\n",
       "        Fuel_Price  MarkDown1  MarkDown2  MarkDown3  MarkDown4  MarkDown5  \\\n",
       "0            2.572        NaN        NaN        NaN        NaN        NaN   \n",
       "1            2.548        NaN        NaN        NaN        NaN        NaN   \n",
       "2            2.514        NaN        NaN        NaN        NaN        NaN   \n",
       "3            2.561        NaN        NaN        NaN        NaN        NaN   \n",
       "4            2.625        NaN        NaN        NaN        NaN        NaN   \n",
       "...            ...        ...        ...        ...        ...        ...   \n",
       "421565       3.997    4556.61      20.64       1.50    1601.01    3288.25   \n",
       "421566       3.985    5046.74        NaN      18.82    2253.43    2340.01   \n",
       "421567       4.000    1956.28        NaN       7.89     599.32    3990.54   \n",
       "421568       3.969    2004.02        NaN       3.18     437.73    1537.49   \n",
       "421569       3.882    4018.91      58.08     100.00     211.94     858.33   \n",
       "\n",
       "               CPI  Unemployment Type    Size  \n",
       "0       211.096358         8.106    A  151315  \n",
       "1       211.242170         8.106    A  151315  \n",
       "2       211.289143         8.106    A  151315  \n",
       "3       211.319643         8.106    A  151315  \n",
       "4       211.350143         8.106    A  151315  \n",
       "...            ...           ...  ...     ...  \n",
       "421565  192.013558         8.684    B  118221  \n",
       "421566  192.170412         8.667    B  118221  \n",
       "421567  192.327265         8.667    B  118221  \n",
       "421568  192.330854         8.667    B  118221  \n",
       "421569  192.308899         8.667    B  118221  \n",
       "\n",
       "[421570 rows x 16 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oTrM7vab5H6P",
    "outputId": "76aa0e01-b3d7-415c-dac9-c469e67a541b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                0\n",
       "Dept                 0\n",
       "Date                 0\n",
       "Weekly_Sales         0\n",
       "IsHoliday            0\n",
       "Temperature          0\n",
       "Fuel_Price           0\n",
       "MarkDown1       270889\n",
       "MarkDown2       310322\n",
       "MarkDown3       284479\n",
       "MarkDown4       286603\n",
       "MarkDown5       270138\n",
       "CPI                  0\n",
       "Unemployment         0\n",
       "Type                 0\n",
       "Size                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "Train_detail.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <i>Dropping columns 'MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5'  as we don't require it our analysis and it contains a lot of Null Values</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "KSgY9tkU5H6Q"
   },
   "outputs": [],
   "source": [
    "Train_detail.drop(columns = ['MarkDown1','MarkDown2','MarkDown3','MarkDown4','MarkDown5'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "6DLq2Zvl5H6Q",
    "outputId": "e300d761-6580-4f6f-82fe-e98eb201d2e1"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "        Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  \\\n",
       "0           1     1 2010-02-05      24924.50      False        42.31   \n",
       "1           1     1 2010-02-12      46039.49       True        38.51   \n",
       "2           1     1 2010-02-19      41595.55      False        39.93   \n",
       "3           1     1 2010-02-26      19403.54      False        46.63   \n",
       "4           1     1 2010-03-05      21827.90      False        46.50   \n",
       "...       ...   ...        ...           ...        ...          ...   \n",
       "421565     45    98 2012-09-28        508.37      False        64.88   \n",
       "421566     45    98 2012-10-05        628.10      False        64.89   \n",
       "421567     45    98 2012-10-12       1061.02      False        54.47   \n",
       "421568     45    98 2012-10-19        760.01      False        56.47   \n",
       "421569     45    98 2012-10-26       1076.80      False        58.85   \n",
       "\n",
       "        Fuel_Price         CPI  Unemployment Type    Size  \n",
       "0            2.572  211.096358         8.106    A  151315  \n",
       "1            2.548  211.242170         8.106    A  151315  \n",
       "2            2.514  211.289143         8.106    A  151315  \n",
       "3            2.561  211.319643         8.106    A  151315  \n",
       "4            2.625  211.350143         8.106    A  151315  \n",
       "...            ...         ...           ...  ...     ...  \n",
       "421565       3.997  192.013558         8.684    B  118221  \n",
       "421566       3.985  192.170412         8.667    B  118221  \n",
       "421567       4.000  192.327265         8.667    B  118221  \n",
       "421568       3.969  192.330854         8.667    B  118221  \n",
       "421569       3.882  192.308899         8.667    B  118221  \n",
       "\n",
       "[421570 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kp2Tr3ZO5H6R",
    "outputId": "8b990a10-b0c6-4618-9065-513186e870ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store           0\n",
       "Dept            0\n",
       "Date            0\n",
       "Weekly_Sales    0\n",
       "IsHoliday       0\n",
       "Temperature     0\n",
       "Fuel_Price      0\n",
       "CPI             0\n",
       "Unemployment    0\n",
       "Type            0\n",
       "Size            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_detail.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "id": "Wcjy8jsE5H6S",
    "outputId": "f58892a4-17cc-4fdb-b685-b7be8c7738fd"
   },
   "outputs": [
    {
     "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": [
    "# Correlation betweeen different Values\n",
    "correlation = Train_detail.corr()\n",
    "sns.heatmap(correlation)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "RCr-CrT75H6T",
    "outputId": "69f7fdb8-7898-4225-8961-c60c7dfda94e"
   },
   "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>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-05</td>\n",
       "      <td>24924.50</td>\n",
       "      <td>False</td>\n",
       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>46039.49</td>\n",
       "      <td>True</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>41595.55</td>\n",
       "      <td>False</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-26</td>\n",
       "      <td>19403.54</td>\n",
       "      <td>False</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-03-05</td>\n",
       "      <td>21827.90</td>\n",
       "      <td>False</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>A</td>\n",
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       "        Store  Dept       Date  Weekly_Sales  IsHoliday  Temperature  \\\n",
       "0           1     1 2010-02-05      24924.50      False        42.31   \n",
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       "\n",
       "        Fuel_Price         CPI  Unemployment Type    Size  \n",
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       "...            ...         ...           ...  ...     ...  \n",
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       "421568       3.969  192.330854         8.667    B  118221  \n",
       "421569       3.882  192.308899         8.667    B  118221  \n",
       "\n",
       "[421570 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train_detail"
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  {
   "cell_type": "code",
   "execution_count": 17,
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    },
    "id": "rabC8HIv5H7a",
    "outputId": "ab6d2b6c-9e92-4519-d479-1d49550333de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store           0\n",
       "Dept            0\n",
       "Date            0\n",
       "Weekly_Sales    0\n",
       "IsHoliday       0\n",
       "Temperature     0\n",
       "Fuel_Price      0\n",
       "CPI             0\n",
       "Unemployment    0\n",
       "Type            0\n",
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       "dtype: int64"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "Train_detail.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "CUW9PAQY5H7b"
   },
   "outputs": [],
   "source": [
    "Evaluate = Train_detail[['Store','Dept','IsHoliday','Temperature','Fuel_Price','CPI','Unemployment','Type','Size','Weekly_Sales']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
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    "id": "x1d6A3ko5H7c",
    "outputId": "e2239ae1-0c74-435a-de1e-0c9b4db2d8f0"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-f267ab124aee>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  Evaluate['Weekly_Sales'] = np.round(Evaluate['Weekly_Sales'])\n"
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    "# Rounding the Weekly Sales values to get a better prediction\n",
    "Evaluate['Weekly_Sales'] = np.round(Evaluate['Weekly_Sales'])"
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       "        Store  Dept  IsHoliday  Temperature  Fuel_Price         CPI  \\\n",
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       "421569         8.667    B  118221        1077.0  \n",
       "\n",
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     "execution_count": 20,
     "metadata": {},
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   "source": [
    "Evaluate"
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
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    },
    "id": "p4VMFXRe5H7d",
    "outputId": "df22ec1c-16e9-4d17-89e4-ad9ec5b6f356"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\satyaki dixit\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4163: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
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    "list1 = Evaluate['Weekly_Sales'].loc[Evaluate['Weekly_Sales'] < 0].index\n",
    "rows = Evaluate.index[list1]\n",
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  {
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   "execution_count": 22,
   "metadata": {
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "1sNh7o5B5H7f"
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   "source": [
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  {
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   "execution_count": 24,
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       "      <td>19404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>A</td>\n",
       "      <td>151315</td>\n",
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       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420300</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420301</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>192.327265</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420302</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420303</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>1077.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420304 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept  IsHoliday  Temperature  Fuel_Price         CPI  \\\n",
       "0           1     1      False        42.31       2.572  211.096358   \n",
       "1           1     1       True        38.51       2.548  211.242170   \n",
       "2           1     1      False        39.93       2.514  211.289143   \n",
       "3           1     1      False        46.63       2.561  211.319643   \n",
       "4           1     1      False        46.50       2.625  211.350143   \n",
       "...       ...   ...        ...          ...         ...         ...   \n",
       "420299     45    98      False        64.88       3.997  192.013558   \n",
       "420300     45    98      False        64.89       3.985  192.170412   \n",
       "420301     45    98      False        54.47       4.000  192.327265   \n",
       "420302     45    98      False        56.47       3.969  192.330854   \n",
       "420303     45    98      False        58.85       3.882  192.308899   \n",
       "\n",
       "        Unemployment Type    Size  Weekly_Sales  \n",
       "0              8.106    A  151315       24924.0  \n",
       "1              8.106    A  151315       46039.0  \n",
       "2              8.106    A  151315       41596.0  \n",
       "3              8.106    A  151315       19404.0  \n",
       "4              8.106    A  151315       21828.0  \n",
       "...              ...  ...     ...           ...  \n",
       "420299         8.684    B  118221         508.0  \n",
       "420300         8.667    B  118221         628.0  \n",
       "420301         8.667    B  118221        1061.0  \n",
       "420302         8.667    B  118221         760.0  \n",
       "420303         8.667    B  118221        1077.0  \n",
       "\n",
       "[420304 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_vl07He85H7g",
    "outputId": "f7552493-a498-49c4-8898-6553c45a3d97"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 420304 entries, 0 to 420303\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   Store         420304 non-null  int64  \n",
      " 1   Dept          420304 non-null  int64  \n",
      " 2   IsHoliday     420304 non-null  bool   \n",
      " 3   Temperature   420304 non-null  float64\n",
      " 4   Fuel_Price    420304 non-null  float64\n",
      " 5   CPI           420304 non-null  float64\n",
      " 6   Unemployment  420304 non-null  float64\n",
      " 7   Type          420304 non-null  object \n",
      " 8   Size          420304 non-null  int64  \n",
      " 9   Weekly_Sales  420304 non-null  float64\n",
      "dtypes: bool(1), float64(5), int64(3), object(1)\n",
      "memory usage: 29.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking Column Type\n",
    "Evaluate.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TPyIIBiq5H7h",
    "outputId": "3513dadd-0370-4ffc-b3c1-49559412ff54"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store           0\n",
       "Dept            0\n",
       "IsHoliday       0\n",
       "Temperature     0\n",
       "Fuel_Price      0\n",
       "CPI             0\n",
       "Unemployment    0\n",
       "Type            0\n",
       "Size            0\n",
       "Weekly_Sales    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Null Values\n",
    "Evaluate.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "Ig0jRld55H7i",
    "outputId": "7d464ad5-17dc-4579-fac4-356de6f191ab"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "        Store  Dept  IsHoliday  Temperature  Fuel_Price         CPI  \\\n",
       "0           1     1      False        42.31       2.572  211.096358   \n",
       "1           1     1       True        38.51       2.548  211.242170   \n",
       "2           1     1      False        39.93       2.514  211.289143   \n",
       "3           1     1      False        46.63       2.561  211.319643   \n",
       "4           1     1      False        46.50       2.625  211.350143   \n",
       "...       ...   ...        ...          ...         ...         ...   \n",
       "420299     45    98      False        64.88       3.997  192.013558   \n",
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       "420302     45    98      False        56.47       3.969  192.330854   \n",
       "420303     45    98      False        58.85       3.882  192.308899   \n",
       "\n",
       "        Unemployment Type    Size  \n",
       "0              8.106    A  151315  \n",
       "1              8.106    A  151315  \n",
       "2              8.106    A  151315  \n",
       "3              8.106    A  151315  \n",
       "4              8.106    A  151315  \n",
       "...              ...  ...     ...  \n",
       "420299         8.684    B  118221  \n",
       "420300         8.667    B  118221  \n",
       "420301         8.667    B  118221  \n",
       "420302         8.667    B  118221  \n",
       "420303         8.667    B  118221  \n",
       "\n",
       "[420304 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Evaluate.iloc[:,0:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dNsrYekp5H7j",
    "outputId": "647c1c57-77b6-4407-b637-3514b8b8a608"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "693099.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(Evaluate['Weekly_Sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "ZKxeRTYE5H7k"
   },
   "outputs": [],
   "source": [
    "value_between_0_and_1 = Evaluate['Weekly_Sales'].loc[Evaluate['Weekly_Sales'] <= 1].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "G5MN3FIE5H7l"
   },
   "outputs": [],
   "source": [
    "rows = Evaluate.index[value_between_0_and_1]\n",
    "Evaluate.drop(rows, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "6yJ36vxn5H7m"
   },
   "outputs": [],
   "source": [
    "Evaluate = Evaluate.reset_index()\n",
    "Evaluate.drop(['index'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "Kez6V-6s5H7m",
    "outputId": "36f6c868-7d2d-414c-b426-46efe2ec33c9"
   },
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419232</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419233</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419234</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>192.327265</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419235</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419236</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>False</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
       "      <td>B</td>\n",
       "      <td>118221</td>\n",
       "      <td>1077.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>419237 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept  IsHoliday  Temperature  Fuel_Price         CPI  \\\n",
       "0           1     1      False        42.31       2.572  211.096358   \n",
       "1           1     1       True        38.51       2.548  211.242170   \n",
       "2           1     1      False        39.93       2.514  211.289143   \n",
       "3           1     1      False        46.63       2.561  211.319643   \n",
       "4           1     1      False        46.50       2.625  211.350143   \n",
       "...       ...   ...        ...          ...         ...         ...   \n",
       "419232     45    98      False        64.88       3.997  192.013558   \n",
       "419233     45    98      False        64.89       3.985  192.170412   \n",
       "419234     45    98      False        54.47       4.000  192.327265   \n",
       "419235     45    98      False        56.47       3.969  192.330854   \n",
       "419236     45    98      False        58.85       3.882  192.308899   \n",
       "\n",
       "        Unemployment Type    Size  Weekly_Sales  \n",
       "0              8.106    A  151315       24924.0  \n",
       "1              8.106    A  151315       46039.0  \n",
       "2              8.106    A  151315       41596.0  \n",
       "3              8.106    A  151315       19404.0  \n",
       "4              8.106    A  151315       21828.0  \n",
       "...              ...  ...     ...           ...  \n",
       "419232         8.684    B  118221         508.0  \n",
       "419233         8.667    B  118221         628.0  \n",
       "419234         8.667    B  118221        1061.0  \n",
       "419235         8.667    B  118221         760.0  \n",
       "419236         8.667    B  118221        1077.0  \n",
       "\n",
       "[419237 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "Im2nwKdM5H7o"
   },
   "outputs": [],
   "source": [
    "# Replacing Type A,B,C with 0,1,2\n",
    "Evaluate['Type'].replace(['A'],0, inplace = True)\n",
    "Evaluate['Type'].replace(['B'],1, inplace = True)\n",
    "Evaluate['Type'].replace(['C'],2, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "0XE8zEio5H7p"
   },
   "outputs": [],
   "source": [
    "Evaluate.drop(['IsHoliday'] ,axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weekly_Sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>42.31</td>\n",
       "      <td>2.572</td>\n",
       "      <td>211.096358</td>\n",
       "      <td>8.106</td>\n",
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       "      <td>151315</td>\n",
       "      <td>46039.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.93</td>\n",
       "      <td>2.514</td>\n",
       "      <td>211.289143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>41596.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46.63</td>\n",
       "      <td>2.561</td>\n",
       "      <td>211.319643</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>19404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46.50</td>\n",
       "      <td>2.625</td>\n",
       "      <td>211.350143</td>\n",
       "      <td>8.106</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>21828.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>419232</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>64.88</td>\n",
       "      <td>3.997</td>\n",
       "      <td>192.013558</td>\n",
       "      <td>8.684</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419233</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>64.89</td>\n",
       "      <td>3.985</td>\n",
       "      <td>192.170412</td>\n",
       "      <td>8.667</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419234</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>54.47</td>\n",
       "      <td>4.000</td>\n",
       "      <td>192.327265</td>\n",
       "      <td>8.667</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>1061.0</td>\n",
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       "    <tr>\n",
       "      <th>419235</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>56.47</td>\n",
       "      <td>3.969</td>\n",
       "      <td>192.330854</td>\n",
       "      <td>8.667</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419236</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>58.85</td>\n",
       "      <td>3.882</td>\n",
       "      <td>192.308899</td>\n",
       "      <td>8.667</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>1077.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>419237 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept  Temperature  Fuel_Price         CPI  Unemployment  Type  \\\n",
       "0           1     1        42.31       2.572  211.096358         8.106     0   \n",
       "1           1     1        38.51       2.548  211.242170         8.106     0   \n",
       "2           1     1        39.93       2.514  211.289143         8.106     0   \n",
       "3           1     1        46.63       2.561  211.319643         8.106     0   \n",
       "4           1     1        46.50       2.625  211.350143         8.106     0   \n",
       "...       ...   ...          ...         ...         ...           ...   ...   \n",
       "419232     45    98        64.88       3.997  192.013558         8.684     1   \n",
       "419233     45    98        64.89       3.985  192.170412         8.667     1   \n",
       "419234     45    98        54.47       4.000  192.327265         8.667     1   \n",
       "419235     45    98        56.47       3.969  192.330854         8.667     1   \n",
       "419236     45    98        58.85       3.882  192.308899         8.667     1   \n",
       "\n",
       "          Size  Weekly_Sales  \n",
       "0       151315       24924.0  \n",
       "1       151315       46039.0  \n",
       "2       151315       41596.0  \n",
       "3       151315       19404.0  \n",
       "4       151315       21828.0  \n",
       "...        ...           ...  \n",
       "419232  118221         508.0  \n",
       "419233  118221         628.0  \n",
       "419234  118221        1061.0  \n",
       "419235  118221         760.0  \n",
       "419236  118221        1077.0  \n",
       "\n",
       "[419237 rows x 9 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <u>Scaling</u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Vuag24HP5H7p",
    "outputId": "4fe8291d-f667-452c-c703-3a8b12bc34cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = StandardScaler()\n",
    "scale.fit(Evaluate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "fCaXjeeW5H7q"
   },
   "outputs": [],
   "source": [
    "first_part = Evaluate.iloc[:,:].reset_index().drop(columns = ['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "Ru1VoB945H7r"
   },
   "outputs": [],
   "source": [
    "train_x, test_x, train_y,test_y = train_test_split(first_part.iloc[:,0:8], first_part['Weekly_Sales'], test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>368818</th>\n",
       "      <td>39</td>\n",
       "      <td>98</td>\n",
       "      <td>73.19</td>\n",
       "      <td>3.743</td>\n",
       "      <td>214.026217</td>\n",
       "      <td>8.300</td>\n",
       "      <td>0</td>\n",
       "      <td>184109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372037</th>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>27.84</td>\n",
       "      <td>2.747</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>5.892</td>\n",
       "      <td>0</td>\n",
       "      <td>155083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268299</th>\n",
       "      <td>28</td>\n",
       "      <td>21</td>\n",
       "      <td>53.77</td>\n",
       "      <td>2.987</td>\n",
       "      <td>126.578286</td>\n",
       "      <td>13.975</td>\n",
       "      <td>0</td>\n",
       "      <td>206302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256669</th>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>69.65</td>\n",
       "      <td>3.937</td>\n",
       "      <td>140.273500</td>\n",
       "      <td>7.850</td>\n",
       "      <td>0</td>\n",
       "      <td>204184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77617</th>\n",
       "      <td>8</td>\n",
       "      <td>93</td>\n",
       "      <td>33.34</td>\n",
       "      <td>2.548</td>\n",
       "      <td>214.621419</td>\n",
       "      <td>6.299</td>\n",
       "      <td>0</td>\n",
       "      <td>155078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>259178</th>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>30.83</td>\n",
       "      <td>3.255</td>\n",
       "      <td>136.525571</td>\n",
       "      <td>8.021</td>\n",
       "      <td>0</td>\n",
       "      <td>204184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365838</th>\n",
       "      <td>39</td>\n",
       "      <td>59</td>\n",
       "      <td>84.31</td>\n",
       "      <td>2.619</td>\n",
       "      <td>210.329211</td>\n",
       "      <td>8.360</td>\n",
       "      <td>0</td>\n",
       "      <td>184109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131932</th>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>77.84</td>\n",
       "      <td>2.783</td>\n",
       "      <td>182.260441</td>\n",
       "      <td>8.743</td>\n",
       "      <td>0</td>\n",
       "      <td>200898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146867</th>\n",
       "      <td>15</td>\n",
       "      <td>95</td>\n",
       "      <td>45.67</td>\n",
       "      <td>3.666</td>\n",
       "      <td>136.490933</td>\n",
       "      <td>7.866</td>\n",
       "      <td>1</td>\n",
       "      <td>123737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>13</td>\n",
       "      <td>36</td>\n",
       "      <td>47.75</td>\n",
       "      <td>3.744</td>\n",
       "      <td>129.044433</td>\n",
       "      <td>7.193</td>\n",
       "      <td>0</td>\n",
       "      <td>219622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>293465 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept  Temperature  Fuel_Price         CPI  Unemployment  Type  \\\n",
       "368818     39    98        73.19       3.743  214.026217         8.300     0   \n",
       "372037     40    25        27.84       2.747  131.637000         5.892     0   \n",
       "268299     28    21        53.77       2.987  126.578286        13.975     0   \n",
       "256669     27     9        69.65       3.937  140.273500         7.850     0   \n",
       "77617       8    93        33.34       2.548  214.621419         6.299     0   \n",
       "...       ...   ...          ...         ...         ...           ...   ...   \n",
       "259178     27    28        30.83       3.255  136.525571         8.021     0   \n",
       "365838     39    59        84.31       2.619  210.329211         8.360     0   \n",
       "131932     14    33        77.84       2.783  182.260441         8.743     0   \n",
       "146867     15    95        45.67       3.666  136.490933         7.866     1   \n",
       "121958     13    36        47.75       3.744  129.044433         7.193     0   \n",
       "\n",
       "          Size  \n",
       "368818  184109  \n",
       "372037  155083  \n",
       "268299  206302  \n",
       "256669  204184  \n",
       "77617   155078  \n",
       "...        ...  \n",
       "259178  204184  \n",
       "365838  184109  \n",
       "131932  200898  \n",
       "146867  123737  \n",
       "121958  219622  \n",
       "\n",
       "[293465 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "368818     9286.0\n",
       "372037     6190.0\n",
       "268299     7141.0\n",
       "256669    25200.0\n",
       "77617     25200.0\n",
       "           ...   \n",
       "259178     1561.0\n",
       "365838      618.0\n",
       "131932     8057.0\n",
       "146867    23912.0\n",
       "121958     1867.0\n",
       "Name: Weekly_Sales, Length: 293465, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u>Learning Phase </u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <u>Models_Selected</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qTz130JP5H7t"
   },
   "source": [
    "## Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "mk1yyMhe5H7u"
   },
   "outputs": [],
   "source": [
    "regr = RandomForestRegressor(max_depth=10, random_state=20, n_estimators = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Gn5m00hs5H7u",
    "outputId": "fdd16f83-b9ab-4a72-fa5d-47cc1bb07471"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=10, random_state=20)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the data into the model\n",
    "regr.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "PNkK_eaD5H7v"
   },
   "outputs": [],
   "source": [
    "feature_imp = pd.Series(regr.feature_importances_,index=['Store','Dept','Temperature','Fuel_Price','CPI','Unemployment','Type','Size']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "AKjzTDim5H7v",
    "outputId": "0c99bc71-06cd-42ed-8428-d4b8b03272c9"
   },
   "outputs": [
    {
     "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": [
    "# Calculating the Feature importance\n",
    "feature_imp.sort_values().plot.barh(color = 'blue' )\n",
    "plt.xlabel('Gini Values')\n",
    "plt.ylabel('Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nPTVrQmc_LpA",
    "outputId": "b76a361e-6d4b-46bc-8f42-eabb7f493dd4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dept            0.702014\n",
       "Size            0.201962\n",
       "Store           0.048271\n",
       "CPI             0.017335\n",
       "Type            0.014824\n",
       "Temperature     0.008987\n",
       "Unemployment    0.005011\n",
       "Fuel_Price      0.001596\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_train_x =  train_x.drop(['Type','Unemployment','Fuel_Price','Temperature','CPI'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=10, random_state=20)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.fit(mod_train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2NBYgihL5H7w",
    "outputId": "ae2d1b6f-c875-4b70-928d-513e6dd37da1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.estimators_[0].tree_.max_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WfIqO7OY5H7x",
    "outputId": "030e21ad-9245-4769-d2da-3c1664a3667d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Store', 'Dept', 'Size'], dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_train_x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 709
    },
    "id": "FusJSnNy5H7x",
    "outputId": "9906ac2b-20fb-4d93-e1c8-aa6e19eb2860"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "tree.plot_tree(regr.estimators_[0], feature_names = mod_train_x.columns , max_depth = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "-ZvFnp3C5H7y"
   },
   "outputs": [],
   "source": [
    "pred_y = regr.predict(mod_train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_test_x = test_x.drop(['Type','Unemployment','Fuel_Price','Temperature','CPI'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "2rf2qYro5H7y"
   },
   "outputs": [],
   "source": [
    "pred_y2 = regr.predict(mod_test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "skpRmURv5H7z",
    "outputId": "6ae82940-5ad8-459a-dcfd-130d3a5dcd63"
   },
   "outputs": [],
   "source": [
    "# r2score_test\n",
    "l=r2_score(test_y,pred_y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D9iFLOZz5H70",
    "outputId": "2491a2d1-7e95-4774-b0f6-102b0fde3aab"
   },
   "outputs": [],
   "source": [
    "# r2score_train\n",
    "m=r2_score(train_y,pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JSeVuBfq5H71",
    "outputId": "a94acfca-825c-4fc2-f1fe-ee8844ef2a9c"
   },
   "outputs": [],
   "source": [
    "# mean_absolute_error\n",
    "n=mean_absolute_error(test_y,pred_y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# root_mean_square_error\n",
    "from sklearn import metrics\n",
    "h=np.sqrt(metrics.mean_squared_error(test_y,pred_y2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kp77AGAo5H73"
   },
   "source": [
    "## Gradient Boosting Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "wDpFOsu45H74"
   },
   "outputs": [],
   "source": [
    "model = GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qchHpBnB5H75",
    "outputId": "62ae34dc-32d4-4c93-e419-94bc5b6b5830"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting the data into the model\n",
    "model.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "RjmsBS_35H77"
   },
   "outputs": [],
   "source": [
    "feature_imp = pd.Series(model.feature_importances_,index=['Store','Dept','Temperature','Fuel_Price','CPI','Unemployment','Type','Size']).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "2LC73AxF5H77",
    "outputId": "90771fc7-da39-4c33-d604-8209c5dad3dc"
   },
   "outputs": [
    {
     "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": [
    "#getting the feature Importance\n",
    "feature_imp.sort_values().plot.barh(color = 'blue' )\n",
    "plt.xlabel('Gini Values')\n",
    "plt.ylabel('Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dept            0.748928\n",
       "Size            0.205448\n",
       "Store           0.029346\n",
       "CPI             0.008890\n",
       "Type            0.005265\n",
       "Unemployment    0.001276\n",
       "Temperature     0.000779\n",
       "Fuel_Price      0.000068\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_train =  train_x.drop(['Type','Unemployment','Fuel_Price','Temperature','CPI'], axis = 1)\n",
    "mod_test = test_x.drop(['Type','Unemployment','Fuel_Price','Temperature','CPI'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(mod_train,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "RgdUZhlD5H79"
   },
   "outputs": [],
   "source": [
    "y_pred2 = model.predict(mod_train)\n",
    "y_pred = model.predict(mod_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 722
    },
    "id": "sCZFPvc85H8p",
    "outputId": "7e05bba5-bbd1-420e-c6b9-47393720749c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "tree.plot_tree(model.estimators_[99,0], feature_names = mod_train.columns , max_depth = 3)\n",
    "plt.title('Strong Tree')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1VeQRHN15H8r",
    "outputId": "4f668814-d3ac-4473-8e88-15b8f7881a0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7326079827822458\n"
     ]
    }
   ],
   "source": [
    "# r2score_test\n",
    "p=r2_score(test_y,y_pred)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5tNc9IAg5H8s",
    "outputId": "6cc54d5c-a3c3-4286-8e30-beaf15164330"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.748604510708317\n"
     ]
    }
   ],
   "source": [
    "# r2score_train\n",
    "q=r2_score(train_y,y_pred2)\n",
    "print(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7XD5140L5H8t",
    "outputId": "985e0cd4-8ddd-4bd1-9a1e-84a465397206"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6818.611433755185\n"
     ]
    }
   ],
   "source": [
    "# mean_absolute_error\n",
    "r=mean_absolute_error(test_y,y_pred)\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# root_mean_square_error\n",
    "i = np.sqrt(metrics.mean_squared_error(test_y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hqbStXAQ5H8u"
   },
   "source": [
    "## Extreme Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "bnf4kkCG5H8v"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "KFold = KFold(n_splits=10 ,shuffle = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y, data_yhat,data_y2,data_yhat_2 = list(), list(), list(), list()\n",
    "def Extreme_Gradient_Boosting_Model(data_y, data_yhat,data_y2,data_yhat_2,Data):\n",
    "    for train_ix, test_ix in KFold.split(Data.iloc[:,0:8]):\n",
    "    # get data\n",
    "        train_X, test_X = Data[['Store','Dept','Temperature','Fuel_Price','Type','Size']].loc[train_ix], Data[['Store','Dept','Temperature','Fuel_Price','Type','Size']].loc[test_ix]\n",
    "        train_y, test_y = Data.iloc[:,-1].loc[train_ix], Data.iloc[:,-1].loc[test_ix]    \n",
    "    # fit model\n",
    "        model = XGBRegressor()\n",
    "        model.fit(train_X, train_y)\n",
    "    # make predictions \n",
    "        yhat = model.predict(test_X)\n",
    "        yhat_2 = model.predict(train_X)\n",
    "    # store\n",
    "        data_y.extend(test_y)\n",
    "        data_yhat.extend(yhat)\n",
    "        data_y2.extend(train_y)\n",
    "        data_yhat_2.extend(yhat_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.909\n"
     ]
    }
   ],
   "source": [
    "Extreme_Gradient_Boosting_Model(data_y, data_yhat,data_y2,data_yhat_2, Evaluate)\n",
    "b = r2_score(data_y, data_yhat)\n",
    "print('r2_score: %.3f' % (b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.916\n"
     ]
    }
   ],
   "source": [
    "a = r2_score(data_y2, data_yhat_2)\n",
    "print('r2_score: %.3f' % (a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3279.488177169475\n"
     ]
    }
   ],
   "source": [
    "c=mean_absolute_error(data_y, data_yhat)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6878.492355158188\n"
     ]
    }
   ],
   "source": [
    "# mean_squared_error\n",
    "from sklearn import metrics\n",
    "j = np.sqrt(metrics.mean_squared_error(data_y, data_yhat))\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5AOMHhmdYmSc"
   },
   "source": [
    "## Elastic Net Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "KFold = KFold(n_splits=10 ,shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "id": "_WG0_AT1YjaS"
   },
   "outputs": [],
   "source": [
    "data_y, data_yhat,data_y2,data_yhat_2 = list(), list(), list(),list()\n",
    "for train_ix, test_ix in KFold.split(Evaluate.iloc[:,0:8]):\n",
    "# get data\n",
    "    train_X, test_X = Evaluate[['Store','Dept','Temperature','Fuel_Price','Type','Size']].loc[train_ix], Evaluate[['Store','Dept','Temperature','Fuel_Price','Type','Size']].loc[test_ix]\n",
    "    train_y, test_y = Evaluate.iloc[:,-1].loc[train_ix], Evaluate.iloc[:,-1].loc[test_ix]    \n",
    "# fit model\n",
    "    model = ElasticNet(random_state=20, alpha = 100)\n",
    "    model.fit(train_X, train_y)\n",
    "# make predictions \n",
    "    yhat = model.predict(test_X)\n",
    "    yhat_2 = model.predict(train_X)\n",
    "# store\n",
    "    data_y.extend(test_y)\n",
    "    data_yhat.extend(yhat)\n",
    "    data_y2.extend(train_y)\n",
    "    data_yhat_2.extend(yhat_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O--UW4q7a5Ye",
    "outputId": "86b94ea5-dc72-40bc-8b07-419d4fb59f0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08419184146726033\n"
     ]
    }
   ],
   "source": [
    "# r2_score_train\n",
    "x=r2_score(data_y2, data_yhat_2)\n",
    "print (x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dz3CY0iWa-f2",
    "outputId": "ac78819a-55b6-4494-ab70-c96fa0b94bc1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08416544032538575\n"
     ]
    }
   ],
   "source": [
    "# r2_score_test\n",
    "y=r2_score(data_y, data_yhat)\n",
    "print (y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ubub-8MVboHx",
    "outputId": "445cf2a3-eedf-4c41-968c-30464ec9304a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14584.597815440988\n"
     ]
    }
   ],
   "source": [
    "# mean_absolute_error\n",
    "z=mean_absolute_error(data_y, data_yhat)  \n",
    "print (z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# root_mean_square_error\n",
    "from sklearn import metrics\n",
    "k = np.sqrt(metrics.mean_squared_error(data_y, data_yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u>Evaluation Phase</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prettytable import prettytable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------------+---------------------+---------------------+---------------------+--------------------+\n",
      "|            Model            | mean_absolute_error |    r2_score_test    |    r2_score_train   |        RMSE        |\n",
      "+-----------------------------+---------------------+---------------------+---------------------+--------------------+\n",
      "|    Elastic Net Regression   |  14584.597815440988 | 0.08416544032538575 | 0.08419184146726033 | 21764.601522013523 |\n",
      "|  Extreme Gradient Boosting  |  3279.488177169475  |  0.9085250659086465 |  0.9164392524604019 | 6878.492355158188  |\n",
      "| Gradient Boosting Algorithm |  6818.611433755185  |  0.7326079827822458 |  0.748604510708317  | 11796.113731559799 |\n",
      "|   Random Forest Algorithm   |   4345.77429925441  |  0.8434556429406255 |  0.8584056169682128 | 9025.752928819114  |\n",
      "+-----------------------------+---------------------+---------------------+---------------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Performances of the Models\n",
    "from prettytable import PrettyTable\n",
    "myTable = PrettyTable([\"Model\", \"mean_absolute_error\", \"r2_score_test\", \"r2_score_train\",\"RMSE\"])\n",
    "myTable.add_row([\"Elastic Net Regression\", z, y, x,k])\n",
    "myTable.add_row([\"Extreme Gradient Boosting\",c,b,a,j])\n",
    "myTable.add_row([\"Gradient Boosting Algorithm\",r,p,q,i])\n",
    "myTable.add_row([\"Random Forest Algorithm\",n, l, m,h])\n",
    "print(myTable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <u>Conclusion</u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a performance difference between Elastic Net Regression, Extreme Gradient Boosting, Gradient Boosting Algorithm, and Random Forest Algorithm based on the above Performance Metrics, and the most efficient model in this set is Extreme Gradient Boosting Algorithm as it learns sequentially that is it transforms from weak learners to Strong Learners by learning at each step similar to Gradient Boosting, but regular gradient boosting uses the loss function of the base model as a proxy for minimizing the error of the overall model, XGBoost uses the 2nd order derivative as an approximation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <u>Prediction Phase</u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the test file\n",
    "predict_for = pd.read_csv('test.csv')"
   ]
  },
  {
   "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 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>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>IsHoliday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-23</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2012-11-30</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>115059</th>\n",
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       "      <td>98</td>\n",
       "      <td>2013-06-28</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115060</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2013-07-05</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115061</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2013-07-12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115062</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2013-07-19</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>115063</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>2013-07-26</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115064 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept        Date  IsHoliday\n",
       "0           1     1  2012-11-02      False\n",
       "1           1     1  2012-11-09      False\n",
       "2           1     1  2012-11-16      False\n",
       "3           1     1  2012-11-23       True\n",
       "4           1     1  2012-11-30      False\n",
       "...       ...   ...         ...        ...\n",
       "115059     45    98  2013-06-28      False\n",
       "115060     45    98  2013-07-05      False\n",
       "115061     45    98  2013-07-12      False\n",
       "115062     45    98  2013-07-19      False\n",
       "115063     45    98  2013-07-26      False\n",
       "\n",
       "[115064 rows x 4 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store          0\n",
       "Date           0\n",
       "Temperature    0\n",
       "Fuel_Price     0\n",
       "Type           0\n",
       "Size           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "feature.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing Type A,B,C with 0,1,2\n",
    "feature['Type'].replace(['A'],0, inplace = True)\n",
    "feature['Type'].replace(['B'],1, inplace = True)\n",
    "feature['Type'].replace(['C'],2, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_for.drop(['IsHoliday'],inplace = True , axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_for['Date'] = pd.to_datetime(predict_for['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mod = pd.merge(predict_for, feature, how=\"inner\", on=[\"Store\", \"Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mod.drop(['Date'],inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Dept</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>55.32</td>\n",
       "      <td>3.386</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
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       "      <td>55.32</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>115061</th>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115062</th>\n",
       "      <td>45</td>\n",
       "      <td>97</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115063</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115064 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept  Temperature  Fuel_Price  Type    Size\n",
       "0           1     1        55.32       3.386     0  151315\n",
       "1           1     2        55.32       3.386     0  151315\n",
       "2           1     3        55.32       3.386     0  151315\n",
       "3           1     4        55.32       3.386     0  151315\n",
       "4           1     5        55.32       3.386     0  151315\n",
       "...       ...   ...          ...         ...   ...     ...\n",
       "115059     45    93        76.06       3.804     1  118221\n",
       "115060     45    94        76.06       3.804     1  118221\n",
       "115061     45    95        76.06       3.804     1  118221\n",
       "115062     45    97        76.06       3.804     1  118221\n",
       "115063     45    98        76.06       3.804     1  118221\n",
       "\n",
       "[115064 rows x 6 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_y, data_yhat,data_y2,data_yhat_2,predict_14 = list(), list(), list(), list(), list()\n",
    "# Defining the function\n",
    "def Extreme_Gradient_Boosting_Model(data_y, data_yhat,data_y2,data_yhat_2,predict_14,predict_13,Data):\n",
    "    for train_ix, test_ix in KFold.split(Data.iloc[:,0:8]):\n",
    "    # get data\n",
    "        train_X, test_X = Data[['Store','Dept','Temperature','Fuel_Price','Type','Size']].loc[train_ix], Data[['Store','Dept','Temperature','Fuel_Price','Type','Size']].loc[test_ix]\n",
    "        train_y, test_y = Data.iloc[:,-1].loc[train_ix], Data.iloc[:,-1].loc[test_ix]    \n",
    "    # fit model\n",
    "        model = XGBRegressor()\n",
    "        model.fit(train_X, train_y)\n",
    "    # make predictions \n",
    "        yhat = model.predict(test_X)\n",
    "        yhat_2 = model.predict(train_X)\n",
    "        yhat_3 = model.predict(predict_13)\n",
    "    # store\n",
    "        data_y.extend(test_y)\n",
    "        data_yhat.extend(yhat)\n",
    "        data_y2.extend(train_y)\n",
    "        data_yhat_2.extend(yhat_2)\n",
    "        predict_14.append(tuple(yhat_3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# calling the model function \n",
    "Extreme_Gradient_Boosting_Model(data_y, data_yhat,data_y2,data_yhat_2,predict_14,df_mod,Evaluate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating final prediction list\n",
    "Final_prediction = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# looping for mean\n",
    "for j in range(0,115064):    \n",
    "    Final_prediction.append(np.mean([i[j] for i in predict_14]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mod['Weekly_Sales'] = Final_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>Type</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>55.32</td>\n",
       "      <td>3.386</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>33857.082031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>55.32</td>\n",
       "      <td>3.386</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>53708.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>55.32</td>\n",
       "      <td>3.386</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>19525.572266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>55.32</td>\n",
       "      <td>3.386</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>43195.335938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>55.32</td>\n",
       "      <td>3.386</td>\n",
       "      <td>0</td>\n",
       "      <td>151315</td>\n",
       "      <td>37633.210938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115059</th>\n",
       "      <td>45</td>\n",
       "      <td>93</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>2373.282471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115060</th>\n",
       "      <td>45</td>\n",
       "      <td>94</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>9177.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115061</th>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>56848.230469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115062</th>\n",
       "      <td>45</td>\n",
       "      <td>97</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>6130.561523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115063</th>\n",
       "      <td>45</td>\n",
       "      <td>98</td>\n",
       "      <td>76.06</td>\n",
       "      <td>3.804</td>\n",
       "      <td>1</td>\n",
       "      <td>118221</td>\n",
       "      <td>731.830200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115064 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  Dept  Temperature  Fuel_Price  Type    Size  Weekly_Sales\n",
       "0           1     1        55.32       3.386     0  151315  33857.082031\n",
       "1           1     2        55.32       3.386     0  151315  53708.750000\n",
       "2           1     3        55.32       3.386     0  151315  19525.572266\n",
       "3           1     4        55.32       3.386     0  151315  43195.335938\n",
       "4           1     5        55.32       3.386     0  151315  37633.210938\n",
       "...       ...   ...          ...         ...   ...     ...           ...\n",
       "115059     45    93        76.06       3.804     1  118221   2373.282471\n",
       "115060     45    94        76.06       3.804     1  118221   9177.968750\n",
       "115061     45    95        76.06       3.804     1  118221  56848.230469\n",
       "115062     45    97        76.06       3.804     1  118221   6130.561523\n",
       "115063     45    98        76.06       3.804     1  118221    731.830200\n",
       "\n",
       "[115064 rows x 7 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Prediction\n",
    "df_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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