1_1_RS_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "2pzy3-Oips4B"
      ],
      "authorship_tag": "ABX9TyMhzkodUVuv6TLrWJZRfJjv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pvanand07/Retail-Sales-Prediction-Regression/blob/master/1_1_RS_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Import Dataset"
      ],
      "metadata": {
        "id": "FpWjfLoRpBHw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3e6_Lk-MowYI"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.metrics import accuracy_score, auc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import tree"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8h7e1ScYpNUN",
        "outputId": "2d45d394-0dad-4d4b-aace-3380c3aba75b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Load_Dataset():\n",
        "  df = pd.read_csv('/content/drive/MyDrive/Almabetter/Almabetter Projects/Data & Resources/Rossmann Stores Data.csv')\n",
        "  df2 = pd.read_csv('/content/drive/MyDrive/Almabetter/Almabetter Projects/Data & Resources/store.csv')\n",
        "  return df,df2\n",
        "def merge_data(df,df2):\n",
        "  df.set_index('Store', inplace=True)\n",
        "  df2.set_index('Store', inplace=True)\n",
        "  df_m = df.join(df2, on='Store')\n",
        "  return df_m\n",
        "df,df2 = Load_Dataset()\n",
        "df_m = merge_data(df,df2)"
      ],
      "metadata": {
        "id": "muvrRAKapSah"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Data Cleaning"
      ],
      "metadata": {
        "id": "kJ0vC4WHqG43"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecting only the rows with Open = 1\n",
        "df_m2 = df_m[df_m['Open'] == 1].drop('Open', axis=1).copy()\n",
        "\n",
        "# Converting Date to Datetime for analysis and feature engineering\n",
        "df_m2['Date'] = pd.to_datetime(df_m2['Date'])\n",
        "\n",
        "# We can also convert object Dtype to category for reduced memory usage\n",
        "for col in df_m2.select_dtypes('object').columns:\n",
        "  df_m2[col] = df_m2[col].astype('category')\n",
        "\n",
        "# Replace all variations of \"0\" with a single representation\n",
        "df_m2['StateHoliday'] = df_m2['StateHoliday'].replace(['0', 0], '0')"
      ],
      "metadata": {
        "id": "Eb1uRYUqqItX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "dLLJgXPnSqNE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 1: Statsmodel (Linear Regression)"
      ],
      "metadata": {
        "id": "NQQ1RMm_qtZ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Assuming `pre_processing2` returns X_train, X_test, y_train, y_test\n",
        "X_train, X_test, y_train, y_test = pre_processing2((df_m2).drop(['Customers','Promo2','Promo2SinceYear'], axis=1))\n",
        "\n",
        "# Create the scaler and fit/transform X_train and X_test\n",
        "#scaler = StandardScaler()\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "X_train_s = pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns, index=X_train.index)\n",
        "X_test_s = pd.DataFrame(scaler.transform(X_test), columns=X_test.columns, index=X_test.index)\n",
        "\n",
        "# Apply square root transformation to y_train and y_test\n",
        "y_train_s = np.sqrt(y_train)\n",
        "y_test_s = np.sqrt(y_test)\n",
        "\n",
        "stats = {}\n",
        "\n",
        "# Perform regression analysis\n",
        "X = X_train_s\n",
        "y = y_train_s\n",
        "model = sm.OLS(y, sm.add_constant(X))\n",
        "results = model.fit()\n",
        "\n",
        "# Extract regression results\n",
        "stats['Regression Results'] = results.summary().tables[1]\n",
        "results.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 996
        },
        "id": "s_i_An5H-z_N",
        "outputId": "571c9e9e-c4d1-43c8-9d23-76e21ea01bb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                  Sales   R-squared:                       0.259\n",
              "Model:                            OLS   Adj. R-squared:                  0.259\n",
              "Method:                 Least Squares   F-statistic:                     3942.\n",
              "Date:                Wed, 14 Jun 2023   Prob (F-statistic):               0.00\n",
              "Time:                        17:33:03   Log-Likelihood:            -8.5372e+05\n",
              "No. Observations:              214747   AIC:                         1.707e+06\n",
              "Df Residuals:                  214727   BIC:                         1.708e+06\n",
              "Df Model:                          19                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==================================================================================================\n",
              "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
              "--------------------------------------------------------------------------------------------------\n",
              "const                           4.297e+12   5.45e+12      0.789      0.430   -6.38e+12     1.5e+13\n",
              "DayOfWeek                         -2.9377      0.086    -34.350      0.000      -3.105      -2.770\n",
              "Promo                             13.0844      0.059    223.568      0.000      12.970      13.199\n",
              "SchoolHoliday                      0.5789      0.073      7.968      0.000       0.436       0.721\n",
              "CompetitionDistance                0.5578      0.102      5.471      0.000       0.358       0.758\n",
              "CompetitionOpenSinceMonth         -3.3579      0.113    -29.647      0.000      -3.580      -3.136\n",
              "CompetitionOpenSinceYear          14.3899      0.596     24.144      0.000      13.222      15.558\n",
              "Promo2SinceWeek                    3.6530      0.084     43.424      0.000       3.488       3.818\n",
              "IsPromoMonth                      -0.4144      0.059     -7.053      0.000      -0.530      -0.299\n",
              "Month                          -5.325e+09   6.75e+09     -0.789      0.430   -1.86e+10    7.91e+09\n",
              "week_number                       -2.0200      0.351     -5.750      0.000      -2.709      -1.331\n",
              "month                           5.325e+09   6.75e+09      0.789      0.430   -7.91e+09    1.86e+10\n",
              "year                               2.1401      0.074     29.047      0.000       1.996       2.285\n",
              "StateHoliday_0                 -3.505e+11   4.45e+11     -0.789      0.430   -1.22e+12    5.21e+11\n",
              "StateHoliday_a                 -3.505e+11   4.45e+11     -0.789      0.430   -1.22e+12    5.21e+11\n",
              "StateHoliday_b                 -3.505e+11   4.45e+11     -0.789      0.430   -1.22e+12    5.21e+11\n",
              "StateHoliday_c                 -3.505e+11   4.45e+11     -0.789      0.430   -1.22e+12    5.21e+11\n",
              "StoreType_a                    -7.227e+11   9.17e+11     -0.789      0.430   -2.52e+12    1.07e+12\n",
              "StoreType_b                     2.855e+08   3.62e+08      0.789      0.430   -4.24e+08    9.95e+08\n",
              "StoreType_c                    -7.227e+11   9.17e+11     -0.789      0.430   -2.52e+12    1.07e+12\n",
              "StoreType_d                    -7.227e+11   9.17e+11     -0.789      0.430   -2.52e+12    1.07e+12\n",
              "Assortment_a                   -2.798e+12   3.55e+12     -0.789      0.430   -9.75e+12    4.16e+12\n",
              "Assortment_b                            0          0        nan        nan           0           0\n",
              "Assortment_c                   -2.798e+12   3.55e+12     -0.789      0.430   -9.75e+12    4.16e+12\n",
              "PromoInterval_Feb,May,Aug,Nov  -4.252e+11   5.39e+11     -0.789      0.430   -1.48e+12    6.32e+11\n",
              "PromoInterval_Jan,Apr,Jul,Oct  -4.252e+11   5.39e+11     -0.789      0.430   -1.48e+12    6.32e+11\n",
              "PromoInterval_Mar,Jun,Sept,Dec -4.252e+11   5.39e+11     -0.789      0.430   -1.48e+12    6.32e+11\n",
              "==============================================================================\n",
              "Omnibus:                      591.882   Durbin-Watson:                   2.002\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              596.751\n",
              "Skew:                           0.129   Prob(JB):                    2.61e-130\n",
              "Kurtosis:                       2.994   Cond. No.                     1.18e+16\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The smallest eigenvalue is 1.01e-26. This might indicate that there are\n",
              "strong multicollinearity problems or that the design matrix is singular.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th>  <td>   0.259</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.259</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   3942.</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Wed, 14 Jun 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>17:33:03</td>     <th>  Log-Likelihood:    </th> <td>-8.5372e+05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>214747</td>      <th>  AIC:               </th>  <td>1.707e+06</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>214727</td>      <th>  BIC:               </th>  <td>1.708e+06</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    19</td>      <th>                     </th>      <td> </td>     \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "                 <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                          <td> 4.297e+12</td> <td> 5.45e+12</td> <td>    0.789</td> <td> 0.430</td> <td>-6.38e+12</td> <td>  1.5e+13</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>DayOfWeek</th>                      <td>   -2.9377</td> <td>    0.086</td> <td>  -34.350</td> <td> 0.000</td> <td>   -3.105</td> <td>   -2.770</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Promo</th>                          <td>   13.0844</td> <td>    0.059</td> <td>  223.568</td> <td> 0.000</td> <td>   12.970</td> <td>   13.199</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>SchoolHoliday</th>                  <td>    0.5789</td> <td>    0.073</td> <td>    7.968</td> <td> 0.000</td> <td>    0.436</td> <td>    0.721</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>CompetitionDistance</th>            <td>    0.5578</td> <td>    0.102</td> <td>    5.471</td> <td> 0.000</td> <td>    0.358</td> <td>    0.758</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>CompetitionOpenSinceMonth</th>      <td>   -3.3579</td> <td>    0.113</td> <td>  -29.647</td> <td> 0.000</td> <td>   -3.580</td> <td>   -3.136</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>CompetitionOpenSinceYear</th>       <td>   14.3899</td> <td>    0.596</td> <td>   24.144</td> <td> 0.000</td> <td>   13.222</td> <td>   15.558</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Promo2SinceWeek</th>                <td>    3.6530</td> <td>    0.084</td> <td>   43.424</td> <td> 0.000</td> <td>    3.488</td> <td>    3.818</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>IsPromoMonth</th>                   <td>   -0.4144</td> <td>    0.059</td> <td>   -7.053</td> <td> 0.000</td> <td>   -0.530</td> <td>   -0.299</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Month</th>                          <td>-5.325e+09</td> <td> 6.75e+09</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.86e+10</td> <td> 7.91e+09</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>week_number</th>                    <td>   -2.0200</td> <td>    0.351</td> <td>   -5.750</td> <td> 0.000</td> <td>   -2.709</td> <td>   -1.331</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>month</th>                          <td> 5.325e+09</td> <td> 6.75e+09</td> <td>    0.789</td> <td> 0.430</td> <td>-7.91e+09</td> <td> 1.86e+10</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>year</th>                           <td>    2.1401</td> <td>    0.074</td> <td>   29.047</td> <td> 0.000</td> <td>    1.996</td> <td>    2.285</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StateHoliday_0</th>                 <td>-3.505e+11</td> <td> 4.45e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.22e+12</td> <td> 5.21e+11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StateHoliday_a</th>                 <td>-3.505e+11</td> <td> 4.45e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.22e+12</td> <td> 5.21e+11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StateHoliday_b</th>                 <td>-3.505e+11</td> <td> 4.45e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.22e+12</td> <td> 5.21e+11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StateHoliday_c</th>                 <td>-3.505e+11</td> <td> 4.45e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.22e+12</td> <td> 5.21e+11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StoreType_a</th>                    <td>-7.227e+11</td> <td> 9.17e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-2.52e+12</td> <td> 1.07e+12</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StoreType_b</th>                    <td> 2.855e+08</td> <td> 3.62e+08</td> <td>    0.789</td> <td> 0.430</td> <td>-4.24e+08</td> <td> 9.95e+08</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StoreType_c</th>                    <td>-7.227e+11</td> <td> 9.17e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-2.52e+12</td> <td> 1.07e+12</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>StoreType_d</th>                    <td>-7.227e+11</td> <td> 9.17e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-2.52e+12</td> <td> 1.07e+12</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Assortment_a</th>                   <td>-2.798e+12</td> <td> 3.55e+12</td> <td>   -0.789</td> <td> 0.430</td> <td>-9.75e+12</td> <td> 4.16e+12</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Assortment_b</th>                   <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0</td> <td>        0</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Assortment_c</th>                   <td>-2.798e+12</td> <td> 3.55e+12</td> <td>   -0.789</td> <td> 0.430</td> <td>-9.75e+12</td> <td> 4.16e+12</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PromoInterval_Feb,May,Aug,Nov</th>  <td>-4.252e+11</td> <td> 5.39e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.48e+12</td> <td> 6.32e+11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PromoInterval_Jan,Apr,Jul,Oct</th>  <td>-4.252e+11</td> <td> 5.39e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.48e+12</td> <td> 6.32e+11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>PromoInterval_Mar,Jun,Sept,Dec</th> <td>-4.252e+11</td> <td> 5.39e+11</td> <td>   -0.789</td> <td> 0.430</td> <td>-1.48e+12</td> <td> 6.32e+11</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>591.882</td> <th>  Durbin-Watson:     </th> <td>   2.002</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 596.751</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.129</td>  <th>  Prob(JB):          </th> <td>2.61e-130</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.994</td>  <th>  Cond. No.          </th> <td>1.18e+16</td> \n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.01e-26. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test.skew())\n",
        "y_test.hist(bins=50)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ND83odrAZ36_",
        "outputId": "077f2f4d-6b79-4450-ad09-def6fefe774f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4704502141002558\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insights (Non-Verified):\n",
        "\n",
        "R-squared and Adjusted R-squared: The R-squared value of 0.258 indicates that the model explains 25.8% of the variance in the dependent variable (Sales). The Adjusted R-squared value is the same, suggesting that the addition of independent variables did not significantly improve the model's fit.\n",
        "\n",
        "F-statistic and Prob (F-statistic): The F-statistic of 3928.0 and a p-value of 0.00 indicate that there is a statistically significant relationship between the independent variables as a whole and the dependent variable.\n",
        "\n",
        "Coefficients: The coefficients for each independent variable provide information about the direction and magnitude of their impact on the dependent variable. A positive coefficient suggests a positive relationship, while a negative coefficient suggests a negative relationship.\n",
        "\n",
        "Significance of coefficients: The t-value, P>|t|, and the confidence interval [0.025, 0.975] help determine the statistical significance of each coefficient. A smaller p-value indicates a stronger statistical significance. If the p-value is less than the chosen significance level (e.g., 0.05), you can reject the null hypothesis and conclude that the coefficient is statistically significant.\n",
        "\n",
        "Important variables: Based on the coefficient values, variables with significant coefficients (p-value less than 0.05) can be considered important in predicting sales. In this case, variables like Promo, CompetitionDistance, CompetitionOpenSinceMonth, CompetitionOpenSinceYear, Promo2SinceWeek, week_number, month, and year appear to be significant predictors.\n",
        "\n",
        "Assumptions: Assessing the model's assumptions is essential. Some assumptions to check include linearity, normality of residuals, homoscedasticity (constant variance), and absence of multicollinearity among independent variables.\n",
        "\n",
        "Omnibus: The Omnibus test is a combined statistical test for skewness and kurtosis of the residuals. The value of 549.481 suggests that the residuals are not normally distributed, indicating a departure from the assumption of normality.\n",
        "\n",
        "Prob(Omnibus): The Prob(Omnibus) value of 0.000 indicates that the null hypothesis of normal distribution of residuals is rejected. This further supports the observation that the residuals do not follow a normal distribution.\n",
        "\n",
        "Jarque-Bera (JB): The Jarque-Bera test is another test of normality for the residuals, focusing on skewness and kurtosis. The JB value of 553.645 is used to test the null hypothesis of normality. Similar to the Omnibus test, a significant JB statistic suggests non-normality.\n",
        "\n",
        "Prob(JB): The Prob(JB) value of 5.99e-121 indicates that the null hypothesis of normality is rejected, providing further evidence that the residuals are not normally distributed.\n",
        "\n",
        "Skew: Skewness measures the asymmetry of the residual distribution. A value of 0.124 suggests a slight positive skew, indicating a longer right tail in the distribution of residuals.\n",
        "\n",
        "Kurtosis: Kurtosis measures the degree of peakedness or flatness of the residual distribution. A kurtosis value of 2.985 suggests a moderate degree of peakedness compared to a normal distribution.\n",
        "\n",
        "Durbin-Watson: The Durbin-Watson statistic tests for autocorrelation in the residuals. The value of 2.004 is close to 2, indicating that there is little to no autocorrelation present in the residuals. A value between 1.5 and 2.5 generally suggests no significant autocorrelation.\n",
        "\n",
        "Cond. No.: The condition number assesses multicollinearity among the independent variables. A higher value indicates a stronger correlation between the independent variables. In this case, the value of 2.22e+16 suggests potential multicollinearity concerns."
      ],
      "metadata": {
        "id": "vrqx_sI1CXrN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Based on the OLS regression results, we can generate the following insights:\n",
        "\n",
        "- The R-squared value is 0.258, which means that the model explains 25.8% of the variance in sales. This indicates that there is still a significant amount of unexplained variance, and other factors not included in the model may be influencing sales.\n",
        "The most significant positive impact on sales comes from running a promotion (Promo), with a coefficient of 13.1032. This suggests that running a promotion can increase sales by an average of 13.1 units.\n",
        "\n",
        "- The day of the week (DayOfWeek) has a negative impact on sales, with a coefficient of -3.0158. This indicates that sales tend to decrease as the week progresses.\n",
        "\n",
        "- School holidays (SchoolHoliday) have a positive impact on sales, with a coefficient of 0.5611. This suggests that sales increase by an average of 0.56 units during school holidays.\n",
        "\n",
        "- The presence of competition (CompetitionDistance) has a positive impact on sales, with a coefficient of 0.5193. This could indicate that stores located near competitors may experience higher sales due to increased foot traffic or other factors.\n",
        "The longer a competitor has been open (CompetitionOpenSinceMonth and CompetitionOpenSinceYear), the more negative the impact on sales, with coefficients of -2.9449 and -3.9209, respectively. This suggests that well-established competitors may have a stronger negative effect on sales.\n",
        "\n",
        "- The Promo2 variable, which indicates whether a store is participating in a continuous and consecutive promotion, has a positive but not statistically significant impact on sales, with a coefficient of 3.46e+09.\n",
        "\n",
        "- The month of the year has a positive impact on sales, with a coefficient of 6.4807. This suggests that sales tend to increase as the year progresses, possibly due to seasonal factors.\n",
        "Store type and assortment type have mixed effects on sales, with some types having positive impacts and others having negative or non-significant impacts.\n"
      ],
      "metadata": {
        "id": "1jN9-l51O60V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Stats Model Results"
      ],
      "metadata": {
        "id": "1--1lmkbQqyH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results.summary().tables[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "M4EhvqHkQo5k",
        "outputId": "cc0742bb-1837-4b7c-987d-427a526bb9d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.table.SimpleTable'>"
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th>  <td>   0.829</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.829</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>5.480e+04</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 09 Jun 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>13:41:25</td>     <th>  Log-Likelihood:    </th> <td>-6.9430e+05</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>214249</td>      <th>  AIC:               </th>  <td>1.389e+06</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>214229</td>      <th>  BIC:               </th>  <td>1.389e+06</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    19</td>      <th>                     </th>      <td> </td>     \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
              "</tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 2: Linear Regression(sklearn)"
      ],
      "metadata": {
        "id": "TTz0AJcVtlfe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_m3.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VHxcL_42Sga",
        "outputId": "428919b3-27fd-4733-d49e-f4d91e826ad8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['DayOfWeek', 'Date', 'Sales', 'Customers', 'Promo', 'StateHoliday',\n",
              "       'SchoolHoliday', 'StoreType', 'Assortment', 'CompetitionDistance',\n",
              "       'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2',\n",
              "       'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval', 'Month',\n",
              "       'Av_sales/Assorment/StoreType/Month',\n",
              "       'Av_customers/Assorment/StoreType/Month', 'CompetitionDistanceBin',\n",
              "       'CompetitionOpenSinceDay'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = pre_processing2(df_m3.drop(['Customers','Promo2','Promo2SinceYear','CompetitionOpenSinceYear'], axis=1))#.sample(100000, random_state=42))\n",
        "scaler = MinMaxScaler()\n",
        "X_train_s = scaler.fit_transform(X_train)\n",
        "X_test_s = scaler.transform(X_test)\n",
        "y_train_s = np.sqrt(y_train)\n",
        "y_test_s = np.sqrt(y_test)\n",
        "\n",
        "\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train_s, y_train_s)\n",
        "\n",
        "y_pred = regressor.predict(X_test_s)\n",
        "R2_score = regressor.score(X_test_s, y_test_s)\n",
        "print(R2_score)"
      ],
      "metadata": {
        "id": "TiQ2Ld-ptrEO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f09a0e05-80ec-4f62-bbb3-c02495bea622"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.28422001579842193\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regressor.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PeQD7z4BYCag",
        "outputId": "bf76c6fb-4906-4341-84e7-54fd2968a2d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([60.46308613])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(regressor.coef_, X_train.columns, columns=['Coefficient']).sort_values('Coefficient', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "mxsUOcz_YLTj",
        "outputId": "2e2e334c-04c0-42d8-b7cc-98810f00135a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Coefficient\n",
              "StoreType_d                     8.644395e+14\n",
              "StoreType_a                     8.644395e+14\n",
              "StoreType_c                     8.644395e+14\n",
              "Assortment_c                    2.274457e+14\n",
              "Assortment_a                    2.274457e+14\n",
              "PromoInterval_Jan,Apr,Jul,Oct   2.243115e+14\n",
              "PromoInterval_Mar,Jun,Sept,Dec  2.243115e+14\n",
              "PromoInterval_Feb,May,Aug,Nov   2.243115e+14\n",
              "StateHoliday_a                  4.944778e+12\n",
              "month                           1.399040e+01\n",
              "Promo                           1.327532e+01\n",
              "Promo2SinceWeek                 2.627560e+00\n",
              "year                            2.595232e+00\n",
              "CompetitionDistance             1.428664e+00\n",
              "StoreType_b                     1.250000e-01\n",
              "Assortment_b                    0.000000e+00\n",
              "SchoolHoliday                  -1.252510e-01\n",
              "IsPromoMonth                   -2.155958e-01\n",
              "CompetitionOpenSinceMonth      -3.762104e+00\n",
              "DayOfWeek                      -4.942436e+00\n",
              "week_number                    -9.279355e+00\n",
              "StateHoliday_b                 -3.905632e+12\n",
              "StateHoliday_c                 -1.270074e+13\n",
              "StateHoliday_0                 -1.781044e+13"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc86afd7-9b04-4881-8ce9-ddb493a1a663\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>StoreType_d</th>\n",
              "      <td>8.644395e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StoreType_a</th>\n",
              "      <td>8.644395e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StoreType_c</th>\n",
              "      <td>8.644395e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assortment_c</th>\n",
              "      <td>2.274457e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assortment_a</th>\n",
              "      <td>2.274457e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PromoInterval_Jan,Apr,Jul,Oct</th>\n",
              "      <td>2.243115e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PromoInterval_Mar,Jun,Sept,Dec</th>\n",
              "      <td>2.243115e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PromoInterval_Feb,May,Aug,Nov</th>\n",
              "      <td>2.243115e+14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StateHoliday_a</th>\n",
              "      <td>4.944778e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>1.399040e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Promo</th>\n",
              "      <td>1.327532e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Promo2SinceWeek</th>\n",
              "      <td>2.627560e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>2.595232e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CompetitionDistance</th>\n",
              "      <td>1.428664e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StoreType_b</th>\n",
              "      <td>1.250000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Assortment_b</th>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <td>-1.252510e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IsPromoMonth</th>\n",
              "      <td>-2.155958e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CompetitionOpenSinceMonth</th>\n",
              "      <td>-3.762104e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DayOfWeek</th>\n",
              "      <td>-4.942436e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>week_number</th>\n",
              "      <td>-9.279355e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StateHoliday_b</th>\n",
              "      <td>-3.905632e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StateHoliday_c</th>\n",
              "      <td>-1.270074e+13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StateHoliday_0</th>\n",
              "      <td>-1.781044e+13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc86afd7-9b04-4881-8ce9-ddb493a1a663')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc86afd7-9b04-4881-8ce9-ddb493a1a663 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc86afd7-9b04-4881-8ce9-ddb493a1a663');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_pred,y_test_s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "EPgxCC2iP6Zw",
        "outputId": "c8ef041c-ea75-4331-a31e-ea698063e512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f40185372e0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABBdklEQVR4nO3df3yT9b3//2dS+otCAy3StApSkX2h8lsmVNx2m5YDykFUNo8OdpzzqxNhCp7bRDaRcdShbm5OUZh+NucOopu34y/cxr4IfuSgFZDKJqsiw4ocacqgkGChLTTX94+a2LRpcqW9klxJHvfbrbcbTa5cebdXSV55v1+v19thGIYhAAAAG3EmewAAAACdEaAAAADbIUABAAC2Q4ACAABshwAFAADYDgEKAACwHQIUAABgOwQoAADAdvokewA94ff7dfDgQfXv318OhyPZwwEAACYYhqHjx4+rrKxMTmfkOZKUDFAOHjyoIUOGJHsYAACgBw4cOKCzzjor4jEpGaD0799fUvsPWFhYmOTRAAAAM3w+n4YMGRJ8H48kJQOUwLJOYWEhAQoAACnGTHoGSbIAAMB2CFAAAIDtEKAAAADbIUABAAC2Q4ACAABshwAFAADYDgEKAACwHQIUAABgOynZqA2IRZvf0Pa6Rh063qzB/fN0QXmRspzx28MpXs8X7ryS9Pa+I6r+6LAkhyqHF2vKOcURn6/jeQb1y5UM6XBTiyVjjcfP3ttztvkNvfWPw3qh5n/1WctpDS7M1YQhA3X0RKuOnTglOaQB+Tka1C9Hg/vnyW8Y2lZ3RIHf55eHFWnn/qM6dLxZRX1z9IHHp/2NJyRD6pfTR57jzRpcmKum5tPy+FrU4DupPg7ptCG5Xfkae1ahXqs9pA88x9XmN+SU5Dekth78LpyS/D14HOyljySHUzrtl4zPvzccUpvx+f0OacTgfrr9ki9p35EmvfZBg7wnWzWoIFdnFOZpSFFfTRoyUH9536NtdY3KcTp15YQz9d2vnKOcPk61nvbrv6o/1keHm3TI16zifjk6/Fmr/IZfLacMjSrtr72ez1Rz4KhOnmpTfrZTXzl3kCrOdOnNvf/UR4dPKDvLqYlDB+qbk4bownMHxfU1szsOwzCMhD9rL/l8PrlcLnm9XjrJIqINu+u1Yn2t6r3NwdtKXXlaPqtCM0aXpszzhTvvgL7Zaj3t14nW0Le6AX2zdf9VY8I+X7jzdNSbscbjZ+/tOTfsrtd//OGvamrtSTjQzuGQUu9VEplq7FmF2v2pT34L/2YLcrL00NXjLHnNjOX9mwAFaWvD7nrNX1ujzn/ggc8Bq+dNtDRIidfzdXfeaNZ0ej4z5+npWOPxs/f2nBt21+vmtTUxPSeA7nV+TemJWN6/yUFBWmrzG1qxvjbsm3HgthXra9Vm0ceMeD1fpPNG0/H5zJ6nJ2O14mdv8xuq3ndEL+/6VNX7jqj1tL9X52zzG/rxK7Wmxg/AHCtfM80gBwVpaXtdY7fLGFL7m1y9t1nb6xpVObzYts8X7byRdHy+WM4T61h7+7Nv2F2vH79SK4/vi3MUFeSosanV1DkvKC/qkqOyva4x5HwAes/K10wzCFCQlg4dN/fmZPa4ZD1fb8cXeHxPzmP1zxTuuO6WYSIFJx29VuvR7X/Y1SVH5bLRblOPBxAbq14zzSBAQVoa3D/P0uOS9Xy9HV/g8T05j9U/U+fj2vyG7nzhvZjH1dGv3/y4y20eb3PY2wH0nlWvmWaQg4K0dEF5kUpdeequMM6h9k/agVJduz5ftPNG0vH5YjlPrGPt6c/+9kdH2st8LZZyWf9AirDyNdMMAhSkpSynQ8tnVUhSlzfOwPfLZ1VYVtsfr+eLdN5oOj6f2fP0ZKw9/dnXvr3f1PkB2IOVr5lmEKAgbc0YXarV8ybK7QqdknS78iwvMY7n83V33oF9s9U3J6vL8QP7ZoctB+zuPFaMNdafvc1vaMuH/zR17oIwPyOA8MaeVSirY4iC3CxLSoxjRR8UpD06yXZ/nmR1kv3lax/qF6/tNXXOpZeO1PHm0/rfoyf00q6DPR4bkIr65/bR4P65cjqNtOgkS6M2AEkTLUihgRoQ2Y8uG6XBhbkJ+UCVaLG8f1PFA6SgRM8KmRWtNX2gqRuA7g0uzNXs8WcmexhJR4ACpJhE7y8Uy7jCtab3eJs1f22NVs+bKFd+To8bzwGZIpGlvHZGkiyQQgJBQOc3+UAQsGF3fVLGZbbdvcd7MpHDAlKK1e0PUh0BCpAiEr2/UKRxdNw3J7DcZKbdvdkOsUCmiUf7g1THEg+QIhK9v1A43S0vmW0tX9QvV6WuPJZ5kPGcDqnjZwm3DZZp7YYABUgRid5fqLNIOSZmW8u7C9tfhKniQSZzSFp17QQNLMi1XaK7nRCgACki0fsLdWRmecnpkAwjfKt5h9o/IQZehB//1kQtfLZGCdy5HYi7or7ZajMk78nut3BwOtqDk8vGliVwZKmJHBQgRSR6f6GOoi0vSe3T1YbMtbu/bGypVl07wfJxAsk09dziiMGJ1P7/ZGBBboJGlNoIUIAUkej9hToyu2z03anDora7DyTZnvIbWlw1Qu7C0Bfr4oIcXTzyDBUV5FgzeCBB/mfvEVPHxWsZNt2wxAOkkMCeN50TVeOdYGd22WhahVs/mlnRbRO5cEm27sI8La76knwnW/Xirk91pKlVmz9o36cnr49Tzaf91v9AQBwcizJ7EkCfE3MIUIAUM2N0qaZVuBPaSTawvOTxNpvKMQlXRdRdkm2Dr1m/eO3DsM9LcIJUMyA/W96Tp6L+P0F0LPEAKSgQBMwef6Yqh0feHNCq57t8XGnYF92ASMtLZpJsgXRw/dRySYlfhk1HBCgAotqwu15PbKnr9v6bvloecXnJTJItkMoCSeoLLz5Xq+dNjJqLhehiDlC2bNmiWbNmqaysTA6HQy+99FLI/YZh6O6771Zpaany8/NVVVWlvXtDt1VvbGzU3LlzVVhYqAEDBuiGG27QZ5991qsfBEB8RJr9CHjlr/URO9iSFIh01nl2ZMboUm1dcrGevXGKfnnNeD174xRtXXIxwUmMYg5QmpqaNG7cOD322GNh73/wwQf1yCOPaM2aNdq2bZsKCgo0ffp0NTd/8QI1d+5c/f3vf9fGjRv16quvasuWLbrpppt6/lMAiBszsx+BDrbdISkQ6Szc7Eiil2HTUcxJspdeeqkuvfTSsPcZhqGHH35Yd911l2bPni1J+t3vfqeSkhK99NJLuuaaa/T+++9rw4YN2rFjhyZNmiRJevTRR3XZZZfpZz/7mcrKaF4DJFpgP51wSbdWdLCNlmQLpJrFVV/SsEF96QIbR5ZW8dTV1cnj8aiqqip4m8vl0uTJk1VdXa1rrrlG1dXVGjBgQDA4kaSqqio5nU5t27ZNV155ZZfztrS0qKWlJfi9z+ezctiALUUKGqzU3f46gbJlqzrY/tukIXp4096IxwCpYHHVl3Rb1YhkDyPtWRqgeDweSVJJSUnI7SUlJcH7PB6PBg8eHDqIPn1UVFQUPKazlStXasWKFVYOFbC1aEGDlc/T3f4689fWaPW8iZpW4TZdYix1DayONrXqhy+9p2MnzPWIAOzMXZirhRefm+xhZISU6IOydOlS3X777cHvfT6fhgwZksQRAfFjJmiwIkiJVvrrkLRifa2mVbi1fFaF5q+tkUOhZcGdkwPDBVZAOgj8rf/48vNYzkkQS8uM3e72LdcbGhpCbm9oaAje53a7dejQoZD7T58+rcbGxuAxneXm5qqwsDDkC0hHZvqFrFhfG7Fixqxoya+Gvkh+DXSwjVQ6GQisCE6QjigTTjxLZ1DKy8vldru1adMmjR8/XlL7bMe2bds0f/58SVJlZaWOHTumnTt36vzzz5ckbd68WX6/X5MnT7ZyOEDKiSVoCNetNRaxJr927GDr8TWr8bMWFRXkyJWfo9bT/qilyECqKi7I0Rs/+Lpy+tA6LJFiDlA+++wz/eMf/wh+X1dXp127dqmoqEhDhw7VokWLdO+992rEiBEqLy/XsmXLVFZWpiuuuEKSNGrUKM2YMUM33nij1qxZo1OnTmnhwoW65pprqOBBxrOiYsasniS/Zjkd8p5s1YMbPggJpIoKstXYRI4J0tORplbt3H+01x8KEJuYA5R33nlHX//614PfB3JDrrvuOv32t7/VHXfcoaamJt100006duyYLrroIm3YsEF5eV+8yD3zzDNauHChLrnkEjmdTs2ZM0ePPPKIBT8OkNqsqpgxI5b9dQK6y48hOEG6o9lg4jkMw0i5WVmfzyeXyyWv10s+CtLKn/5Wr4XP1qi7FJNA0LB1ycWWJOoFAg4pfPJrxzX3Nr+hix7YTI4JMtKzN05hBsUCsbx/s6AG2MSG3fVasK774CTAys3GzCS/BrCfDjJVKTsQJ0VKlBkD6c7MfjdOh7Tq2gmWVxF0TH6N1BRuY234PkVAOnOIHYiThQAFsAEzsxN+QxpYkNvldis6zgb2DenOht31+s2bH8d0TiDVDeybrZVXjaG0OEkIUAAb6Gn1Tjw7zgYCH4+vWfe8+vdenQtIJQPys3X91GFaePEIZk6SiAAFsIGeVO/Es+MsHWGRaYoLcjR7fJmmVbjZ/M8mCFAAG4i15DeWNvWxvtB2F/gA6aggx6knvv1lTRleTFBiM1TxADaQ5XRo+awKSV+U+AZ03u9Giq3jbCza/IbufOE9ghNkjJ9+Y5ymjhhEcGJDBCiATcRS8huvjrOrNu9l12FkjEtGnqHLxtLB3K5Y4gFsxGzJbzw6zrb5DT1FpQ4yyP/7leHJHgIiIEABbCZaya/Us5yVaEHP9rpGHTvJ7AkyxxHa19saAQqQggI5K/PX1sih8G3qAzkrZkuR2WsEmeb7v98lp9Opy8bS58SOyEEBUpSZnJVARU7nhNpAKfKG3fXB26zYgBBIJYYh3bIu9P8B7IMZFCCFRcpZiVaKLEk/fPE9nTzll7swT+efPVClrjx6nyDj9LQkH/FFgAKkuO5yVsy0z29sOqXFv98lSXIX5unMgQQoyDyBknx2K7YXAhQgTcWaU+LxNcvjIzhBZiIHy34IUIAUEeumgOSUAObx/8V+CFCAFNCTTQGjlSIDaFfaoSQf9kEVD2BzsVTidBSpfT6AL3TcRgL2QYAC2JiZSpwV62vV5g8/R9JdKTIAaWDfbK3pxa7fiC+WeAAbi2VTwO4qEDqWIm/d+0899n/3xWm0gL3d+vVz5f98v+/K4cWacg47GNsZAQpgY1ZtChgoRaZSAZns+Zr/1dYlFxOUpAiWeAAbs3pTQCoVkMkCs42dtfkNVe87opd3farqfUe6XTJFYjGDAthYrJsCRsPmaMh0nWcRe1Ihh8RgBgWwsUiVOJ03BYzmT3+r162fd40FMlXHWcSeVsghMQhQAJszsylgdwJT1/+5/u+6ZV2NmLlGJuvY76S3FXKIP5Z4gBQQaVPAzgIdZzfWevTSroNqbGpNwogBe3EodLbRigo5xBcBCpACzLa5D7eeDkBaVPWlkNlGqyrkED8EKIDNmU3iC6ynMyENdDW0KD/keyrf7I8ABUiCWGZEwgUdHm+zbl5bo8VVIzRsUIEG9cvVj1/5O8EJ0I3OS51WV8jBegQoQIKZnRExk8T3i9f2xnewQJoo6pcb8n2gQm7+2ho5pJD/Z7FWyCE+qOIBEiiWssZoSXwAzHMXdl2q6U2FHOKPGRQgQaLNiDjUXtY4rcKtLKeD5DzAIqURlmpiqZBDYhGgAAlitqzxt2/W6TtTy0nOA3rJ7FJNYK8q2AtLPECCmJ0RueeP7+uiBzbraFOrSl15XTrIApnG4ZCuv/BsLfz6uTE9jqWa1EaAAiRILDMiHm+zFqyr0b+OdVOZg4xnGNJv39qv7Cxz4fqlo9169sYp2rrkYoKTFMYSD5Ag0coaOwrc/+utH8d5VEDqeHb7J3IX5qnB1/3/oVJXnlZ9ayI5JGmAGRQgQSJt/NcdtgEB2hmSPL4WXXvBUEnhN8/s3M4eqY0ABUiQNr8hV36Ovjt1mAYWZCd7OEBKGjaob9TS4MAmmS/v+lTV+46w4V+KYokHSIBwzdnysp1qPuVP4qiA1DO4f54qhxd3WxpsthEi7I8ABYiz7trVE5wA5nVuPR+uNDjS1hDz19ZQ0ZNiWOIB4ihSczYA5pjpZ2Jma4gV62tZ7kkhBChAHNGuHug9M/1MzDZC3F7XGIcRIh5Y4gEs1HmXYo+P4AToiW9MPFNf+dIZplvPm22EyBYSqYMABbBIuOS8gpysJI4ISF1f+dIZmj3+TNPHm22EyBYSqYMABeik8yyImU9v3SXnNbW2xW+gQBqLNZCI1gixc5It7I8ABeigJyWKJMIC5vXLzdJnLdED96NNrTGdN9AIcf7aGjmkkP+PZjcNhL2QJAt8LjAL0jnRLlCiuGF3fdjHkQgLmGcmOJGke/4Ye8XNjNGlUZu4IXUwgwIoeomiQ+0litMq3F0+gZF0B1gvUHHTuddJNDNGl3bbxA2phQAFUGwlip1fMEm6A+Kjp8F/uCZuSD0s8QDqXYliIDkPgLUI/jMbAQqg3pUoBpLzmEBGpuqXa305fSkVNxnP8gClra1Ny5YtU3l5ufLz8zV8+HDdc889MowvVvcNw9Ddd9+t0tJS5efnq6qqSnv37rV6KIBpZioGIr1gzhhdqse+NZEgBRnFIamoINt04mss56XiBpbnoDzwwANavXq1nn76aZ133nl65513dP3118vlcunWW2+VJD344IN65JFH9PTTT6u8vFzLli3T9OnTVVtbq7w8pvSQWG1+Q/f8sTbqcctmjpIkVe87okPHmzWoIFdySIc/a9Hg/nly5WdTaoyMEQgdrhx/pn795seWnXdAfh/dP2csFTewPkB56623NHv2bM2cOVOSNGzYMD377LPavn27pPbZk4cfflh33XWXZs+eLUn63e9+p5KSEr300ku65pprrB4SEJHZMuE9Dcd118u71dh0Kuz9edmsmCI95WU7ldcnS8dOfvG37/68P5ArP8fSAOWxuedr6rmDLDsfUpflr6gXXnihNm3apA8//FCS9Ne//lVbt27VpZdeKkmqq6uTx+NRVVVV8DEul0uTJ09WdXV12HO2tLTI5/OFfAFWMZsg+8tN/+g2OJGk5lN+q4YE2ErzKb+8J09pcdWX9MtrxuvZG6fojR98Xa78HHl8zSoqyO72sQ61L49ef+HZpp7r8GctFo0aqc7yGZQ777xTPp9PI0eOVFZWltra2nTfffdp7ty5kiSPxyNJKikpCXlcSUlJ8L7OVq5cqRUrVlg9VEASlQKAWc/t+ERbl1ysjbUefe2nr0edeezYwdWVn6On3tof9Tn4/4gAy2dQ/vCHP+iZZ57RunXrVFNTo6efflo/+9nP9PTTT/f4nEuXLpXX6w1+HThwwMIRI9NdUF4U8RMggC96Aa3a/I+wHZfD6djBNVCO313aa2CmhcodBFg+g/KDH/xAd955ZzCXZMyYMdq/f79Wrlyp6667Tm63W5LU0NCg0tIvkqAaGho0fvz4sOfMzc1Vbm6u1UMFJLWXCVud6Aekq6ferOs2Gby9qidHd80cJbcrP6SDK3vlIFaWz6CcOHFCTmfoabOysuT3t6/Pl5eXy+12a9OmTcH7fT6ftm3bpsrKSquHA5hSVeFO9hCAlNAxUbYzQ9KRpla5XfmqHF7cJdhgrxzEwvIZlFmzZum+++7T0KFDdd555+ndd9/Vz3/+c333u9+VJDkcDi1atEj33nuvRowYESwzLisr0xVXXGH1cABTom3VDmQ6hyRXfnbEACUgUuI5e+XALMsDlEcffVTLli3TLbfcokOHDqmsrEzf+973dPfddwePueOOO9TU1KSbbrpJx44d00UXXaQNGzbQAwVJE2n6Gch0gdDh+qnD9IvXojfVjJboyl45MMNhdGzxmiJ8Pp9cLpe8Xq8KCwuTPRykuDa/Efw09/HhE3rqrTodOxH9UyKQrpwOyd/hnaH0854n0yrcuuiBzd3ONDrUvlyzdcnFzIggrFjev9nNGBltw+56rVhfG1KRwMsqMlXgb3/VtRM0sCA37BIMia5IFAIUZKwNu+s1f21Nl0+CKTeliIyUm+VQS5u1f62B7rCRklUDia6dA3szjwViQYCCjNFxKWdQQa5+/EotwQhS1k/njNVtf/irJGuC6mUzR+k7U8tNzX6Q6IpEIEBBRgi3lAOksjNc+WFnMnqqqCAnpgCDRFfEGwEKUl7HmZFwn+S6W8oBUtmh482aPf5MTatw6629h3XzMzvV1NrW4/M1NrVaODqg9whQkNLCzYyUdlgLb/MbWrGepRykn0Ap78Zaj1asr+1VcCJJRf3o1g17IUBByupuZsTjbdb8tTVaPW+iXPk5LOsg7RTkOHVBeZGls4PuQvpQwV4sb3UPJEKkmZHAbSvW18rjPZnIYQEJcdof+f9ArNikD3bEDApS0va6xogzI4GdV1lXRzpqOe3Xf1V/3OvZQXqXwM6YQUFKirTXR0eNTa1yF3a/xTuQqrbVNfb6HEUFOWzSB9siQEFKirbXR8Bj/3efmk+3kSSLtPP/1Tb0+hx3zRxFcALbIkBBSgrsPmxmZsTLvjpAWG5XfrKHAHSLAAUpKbD7sBR97xxmT4BQDpEYC/sjQEHKCuwJ4nZRHgmYRWIsUgUBClLajNGl2rrkYi38+vBkDwWQJOX2cWhAfnZMj3Eo8kzggPxsLbDob9ztygsmxrb5DVXvO6KXd32q6n1H1OZnvhH2QZkxUl6W06Gp556hVa/vS/ZQALWcNtRyOra8p0BY4FDokmQgaLl/zhi58nP0mAV/44bR/gzRujADyUaAgpQTbu+dQNIsXWORylx9s3WsQ1K3u9O2DVb8jTf4WnTz2pqw93XswkyQgmQjQEFKifSp77yyQgIUpLT87Cw9dsNEHW5q6bLxZSAxvLvgwqxIiziG2mdtVqyv1bQKNzkqSCpyUJAyAvuOdA5CPN5m3by2Rq+9fyhJIwOsUe9tltPp0OzxZ6pyeHGXAGHG6FI9/q2JimfcEOjCvN2CRnBAbxCgwJY6J++1nvZH3XsHSAfRuiRfNrZUq66dkPRxAPHGEg9sI5Bb8lqtRy/u+lSNTV+sxRcVZId8D6QrM12SLxtbpjVOR5flzkSPA4gnAhTYQrjcko4ITpDuHGpPijXbPG3G6FJNq3AHE8YHFeRKDunQ8RYdPt6iVZv3ytt8Ou7jAOKFAAVJF8gtYakG6cyh9iqdwNYL4cqJY22eluV0qHJ4cdj7hhTla/7nCbWdnytaWTNN3GAH5KAgqdr8Rre5JUC6CPYzuWpM2O7HHZunWaW7TstuV57WzJuoNQkaB9BTzKAgqbbXNVIajLRTkJulppa24PfuTg3QOi7NdC4ntlLnZaDOz5WocQA9QYCCpKJSAOmm1JWnN37wde3cf7TbN/5ISzORhGtSGC2giPRcPR0HkAgEKEgqKgWQbpbPqlBOH6flb/y0pkemIQcFCdexx4nfb8hdmBdxozQgFTgd0uPfik/+RqQmhfPX1mjD7nrLnxNINmZQkFDhPgUO6JsdbLFNsixS1aprJ+iysdYHJ5ESyWlNj3TGDAoSprtPgYGyS1ff2LaoB+yg9POqmMvGlsXl/NESyWlNj3TFDAoSwsynwLY2f4JHBcSuqCBH11UO07BBfRNS+WI2kZyEc6QbAhQkhJlPgcc7lGUCdrVs5ihdOfGshD2f2URyEs6RbljiQULw6Q7pwu3KT+jzXVBepFJX94nkDrUvM9GaHumGAAUJwac7pLpkBQJZToeWz6oIjqHzmCRa0yM9EaAgIaJ9CgTsYODnidp2CwQita2nNT3SlcMwjJSr7PT5fHK5XPJ6vSosLEz2cGBSoIpHopwY9jLK3V/fOP8sfbtymDZ/0GDbhmg96SQL2Eks798EKEiocH1QALsoKsjWvbNHa/roUgIBIA4IUGBrbX5Dv9i4R6te35fsoQBhfe+r5Vp6WUWyhwGknVjev8lBQcJlOR2aeu4ZyR4G0K1fbanTn/5G+3ggmQhQkBQXlBdpQD6dY2Ffy17erTZ/yk0wA2mDRm1IiM7JfUebWnXs5KlkDwsZqG9Olk60Rm8KeKSpVdvrGi3flRiAOQQoiDsSY5FsedlO/eSKMSodkC+/39DcX28z9TgaDALJQ4CCuPrT3w7qlnXvJnsYyHAP/9v4YIlwm99QUUG2Gpuiz+DRYBBIHnJQEDd/+lu9Fj5LcILE6VwJHNhpuGP/kiynQ/fOHh31XLSPB5KLGRTExYbd9bplXU2yh4EM4pC06tqJGliQE7V/yWVjy/S9/z2mX22p6/ZctI8HkosABZZr8xtasb422cNABhmQn63754yJqdPr0ssqNO6sgbrr5d1qbGoN3m6XrrFApiNAgaXa/IZ++2YdCbFIqMfmTtTUcwfF/LjLxpZq+mg3XWMBGyJAQUwi7QVCtQ4SzaH2DfOmnNPzUuAsp4NSYsCGCFBgWrgAJDAdLknz19awCSASJtk7DAOILwIUmBLYibhzAOLxNmv+2hq5+mYTnCCh3OSKAGmNAAVRBZJewwUggduOnaArLBJncdWXtPDic5k5AdIYfVAQ1fa6RvJKYBsOSc/t+CTZwwAQZ3EJUD799FPNmzdPxcXFys/P15gxY/TOO+8E7zcMQ3fffbdKS0uVn5+vqqoq7d27Nx5DgQVo9w07MSTVe5u1va4x2UMBEEeWByhHjx7V1KlTlZ2drT//+c+qra3VQw89pIEDBwaPefDBB/XII49ozZo12rZtmwoKCjR9+nQ1N/NGaEe0+4bVrFiYIXAG0pvlOSgPPPCAhgwZoqeeeip4W3l5efDfhmHo4Ycf1l133aXZs2dLkn73u9+ppKREL730kq655hqrh4ReuqC8SKWuPHm8zWHzUBySBvTN1lHyUGBSUUGOZo8vU2Fetp7c+pGaWqLvLtwZgTOQ3iyfQXnllVc0adIkffOb39TgwYM1YcIEPfnkk8H76+rq5PF4VFVVFbzN5XJp8uTJqq6uDnvOlpYW+Xy+kC8kTpbTESwl7vzJN/D9yqvG6IapwxI5LKSgS0aeoaKCbB1patVv3vxYD2/aq5ys2F6GHGKfHCATWB6gfPTRR1q9erVGjBihv/zlL5o/f75uvfVWPf3005Ikj8cjSSopKQl5XElJSfC+zlauXCmXyxX8GjJkiNXDRhQzRpdq9byJcrtCP7W6XXla/flmbFUV7iSNDqlgWsVgbf7gn112EY6lAozeJ0DmcBiGYWn7ipycHE2aNElvvfVW8LZbb71VO3bsUHV1td566y1NnTpVBw8eVGnpF/0Lrr76ajkcDv3+97/vcs6Wlha1tLQEv/f5fBoyZIi8Xq8KCwutHD6iiNRJts1v6KIHNlPxg7AcDqm3rzbskwOkNp/PJ5fLZer92/IclNLSUlVUVITcNmrUKP33f/+3JMntbv+U3dDQEBKgNDQ0aPz48WHPmZubq9zcXKuHih6I1BY8y+nQ5eNKu90hFpnNTHBSVJAdMsPiLszVtRcM1bBBBeyTA2QYywOUqVOnas+ePSG3ffjhhzr77LMltSfMut1ubdq0KRiQ+Hw+bdu2TfPnz7d6OEigNr+h37/zv8keBlLYsn89T+7CPDbuA2B9gLJ48WJdeOGF+slPfqKrr75a27dv1xNPPKEnnnhCkuRwOLRo0SLde++9GjFihMrLy7Vs2TKVlZXpiiuusHo4sECkZZ2O3v7oCB1l0Svuwjw27gMgKQ4Bype//GW9+OKLWrp0qf7zP/9T5eXlevjhhzV37tzgMXfccYeampp000036dixY7rooou0YcMG5eVRNmg3kTYIDOQBBAKY377J0g56JrArMZU5AAIsT5JNhFiSbNBzf/rbQd2y7t0utwfmTlbPmyhJXQIYIBYd/55IfgXSW1KTZJEe/vS3ei18tmtwIrW3GndIWvrCezRnQ6+xKzGAcAhQ0MWG3fW6ZV1NxGMMieAEPeJQeyfZu2aOktuVTyIsgLAIUBCizW9oxfraZA8DKW7g51sfOKSQ7RECYch9V45mxgRARHHZzRipa3tdI/kk6OK2S85VqSt6EvsNU4fp2Run6J27pmlNlM7DABAJMygIwQ6xCOfpt/brJ1eO0cCCHG2s9eilXQfV2NQavD9ch9cZo0s1rcJtqkQdADqjigchqvcd0bVPvp3sYcCGHPqi0sZsbxwA6IgqHvTYBeVFKnXlyeNtVspFroi7FetrNa3CHXHLAwCwAjkoCJHldGj5rIroByLjGJLqvc3aXteY7KEAyAAEKOhixuhSPfatCXIwY48wyFMCkAgEKAhrYEGuqd1nkR4Wfn24fnTZKFPHDu7PlhQA4o8cFITFp+TMENgDZ/G0/0eS9Js367rNP2K/HACJxAwKwuJTcuZYPqtCWU5HSP5R59U9R6djASDeCFAQ1uYPGpI9BMRZaZimaTNGl2o1DdYA2ABLPAgK9Lb4P/+zT5s++Geyh4Neys92ypWfI4/vi+W64oIczR5fpmkV7m57l9BgDYAdEKBAUvsGgSvW19LmPo08+I1xumxMaY8CDfqcAEg2AhRow+56zV9bQ2O2NDKtYrBmjSuTJAINACmJHJQM13rarx+++B7BSQrKz3aGTWa98SvD9OS/fzkZQwIAyzCDksE27K7XD1/crcamU8keCmLkkPSLfxuvi0eW6L+qP9b+xhM6u6ivvl05TDl9+NwBIPURoGQolnVSV+edg2/4yjlJHhEAWI8AJQO1+Q2tWF9LcJIiXPl99Pi3ztfhphYqagBkDAKUDLS9rpFqnRTiPXlaTqdDs8efmeyhAEDCsFidgWhjn3q4ZgAyDQFKBqKNferhmgHINCzxZKALyotU6spjmSdFFBVkBzfoC3T7pcMrgHRHgJKBApvC3by2JtlDgQn3zh6tLKcjbLffzhU9AJAuWOLJUDNGl2rRJSOSPQxE8b2vluuysWXBsvDOs14eb7Pmr63Rht31SRohAMQHAUoG23voeLKHkPEKcpxy5XWdyOyX20ePf2uill5WEbEsPHDbivW1avNTOA4gfbDEk6FaT/v1p/c8yR5GxgpkjTx09XhNq3Dr7X1HVP3RYUntm/RNOadYWU6H2vyGfvtmXcR8IUNSvbdZ2+sa2XcHQNogQMlQ/1X9MY3aksjdKXdk6ohBmjpiUMgxse4wTSkygHRCgJKh9jeeSPYQMoq7ME/XXjBUwwb1NVV905OtCChFBpBOCFAySMcSVT/5Cgn10DfHdZkh6U6sWxE41D4jEyhFBoB0QICSIWJdLoC1Dje1mD42lq0IAnMwy2dV0A8FQFohQMkA7FycfLEsv8SSS9I5lwUA0gUBSppj52Jr9c3J0onWtuD37sJcNZ/2y3viVNjfcU+WX8wGM8tmjtJ3ppYzcwIgLRGgpDl2LrbWk/8+SU6HI6TV/MZaj+avrZFDCglSerr8EtiKwONtjhj0EJwASGc0aktzlJ5ap7ggR1POKVbl8GLNHn+mKoe39yqZMbpUq+dNlNsVOvPhduVp9byJMS+/BLYikL4IcgLIOQGQKZhBSXMfH25K9hDSxj2f74kTzozRpZpW4bZsI79A0NM5sZmcEwCZggAljXTe6fb8swfq2e2fJHtYaaF9T5zIQUGW02FpJ1czQQ+7GwNIVwQoaSJcGXFRQbYam04lcVSpzyHpl1eP0xmufL2869OEBwGRgh52NwaQzhyGYaRcgYfP55PL5ZLX61VhYWGyh5N0lBHHV1FBjhqbWoPf2yEI6O6aB8KmnuS+AEC8xfL+TZJsiqOMOP46BieS5PE2a/7aGm3YXZ+U8bC7MYBMQICS4igjTrxkBwHRrnnH3Y0BIFURoKQ4yoiTI5lBgNlrzt8GgFRGgJLi2ME2uZIRBJi95vxtAEhlBCgp7oLyIrnyKcbqjX65WRrQNzvktqKC7G6ODpWMICDQaba7OiKH2hN52d0YQCrjnS3Fbaz1yHvydLKHkZIKcrL0xLcnacrnZbyde8h87aevR203n4wgINBp1sr2+gBgN8ygpLBANQd65qGrx2nqiEHKcjqC/UYCLexz+jht3W7e6vb6AGA3zKCkMCp4esZsHxO7t5u3ur0+ANgJAUoKo0rDvOKCHM0eX6ZpFe6Y3sTjEQRY2Z7e6vb6AGAXBCgpjCqNyP51bKmmVZTYKgigPT0AmEOAksKONrXK6ZBoGNrVgPw++uU1E2y13NFde/pAZ1pyRwDgC3FPkr3//vvlcDi0aNGi4G3Nzc1asGCBiouL1a9fP82ZM0cNDQ3xHkpa2bC7XgvW1RCcdOPYydO26qRKe3oAiE1cA5QdO3boV7/6lcaOHRty++LFi7V+/Xo9//zzeuONN3Tw4EFdddVV8RxKWmH/HXPM5Oi0+Q1V7zuil3d9qup9R+IWINCeHgBiE7clns8++0xz587Vk08+qXvvvTd4u9fr1a9//WutW7dOF198sSTpqaee0qhRo/T2229rypQp8RpS2qB6x5xoOTqJzAehPT0AxCZuMygLFizQzJkzVVVVFXL7zp07derUqZDbR44cqaFDh6q6ujrsuVpaWuTz+UK+MhlvYtFF66QayAfpHOh13qnYqhkW2tMDQGziMoPy3HPPqaamRjt27Ohyn8fjUU5OjgYMGBBye0lJiTweT9jzrVy5UitWrIjHUFMSb2KRORS5iVq0fBCH2vNB/H7pnj9aM8MSaE9vx860AGBHls+gHDhwQLfddpueeeYZ5eVZ80a6dOlSeb3e4NeBAwcsOW8q6fhJ3m8YKumfm+wh2VJxQU7Uahiz+SC3rIs+w2JWoD29ZM/OtABgN5bPoOzcuVOHDh3SxIkTg7e1tbVpy5YtWrVqlf7yl7+otbVVx44dC5lFaWhokNvtDnvO3Nxc5eZm7htyuFwJhHfXzFFRZzd6s0TWcYZlWoU7poDC7p1pAcBOLA9QLrnkEr333nsht11//fUaOXKklixZoiFDhig7O1ubNm3SnDlzJEl79uzRJ598osrKSquHk/K6652B8Nyu/KjH9HaJrGPFTawN3GhPDwDmWB6g9O/fX6NHjw65raCgQMXFxcHbb7jhBt1+++0qKipSYWGhvv/976uyspIKnk4oJzYvlhyOaPkgZvV0Job29AAQXVI6yf7iF7+Q0+nUnDlz1NLSounTp+vxxx9PxlBsKbBXy5v/+CfLOibEmsMRyAeZv7ZGDikkSOn8fSQkKwNA/DgMw0i5D+g+n08ul0ter1eFhYXJHo6lyDeRBvbN1tETp0wf39PKmu76oCybOUr3/PH9qBU3W5dczNIMAMQglvdv9uKxkUzNN7mucqhmjC4LycnYWOvRnS+8p2OdApWBfbN13xVjNLAgp9c5HJHyQZxOR7czLBIVNwAQb8yg2ESb39BFD2zOyJmTZ2+cEjYno81v6O19R1T90WFJ7XkbU84pTlhgwM7DAGAtZlBSUKa2r4/U8TXL6dDUEYM0dcSgBI+qHRU3AJA8BCg2kant6+2+VELFDQAkR1x3M4Z5mVYRUpCbpTVROr4CADIXAYpNBHpz2HcuwVr3XjGG4AQA0C0CFJuItFdLOnIXZtaMEQAgNgQoNhLYq8XtSt83b4ciJ8YCACCRJJtUgY6xHStEZowuld9v6JZ17yZ7eHFhyP6JsQCA5CNASZLuu5hW6J4/vp/EkcXXd6cOI/cEABAVSzxJEOgY27nvicfbrFvWdb09FfxLRYmp46ZVuOM8EgBAOiBASbBIOxSnXEvfDq67cFjEKiRyTwAAsSBASbB07BhbXJCjKecUd1uFxP41AIBYEaAkWDp2jJ09vkxZTke3VUhuV55W05QNABADkmQTLB07xnbMK2H/GgCAFQhQEizQMdbjbbZ9zknfbKccToeaWtq6PSZcXgn71wAAeoslngTr2DHWjgpys3Tp6BI9c8Nkvbdihh765jg5FD6vxCHySgAA8UGAkgSBXI2igpxkD6WLEy1t2rC7QcdbTpFXAgBIGodhGHZfaejC5/PJ5XLJ6/WqsLAw2cPpsdbTfk3+yUYdPXE62UMJ4VB7ALJ1ycXB2ZFwXW+ZOQEAxCKW929mUJIop49T37mwPNnD6MKQVO9t1va6xuBtgbyS2ePPVOXwYoITAEBcEaAk2bBBBXE9f9+crLA5JGakY0k0ACA1EKAk2ceHm+J6/tw+Tj32rQk92iE5HUuiAQCpgTLjJAjkc3i8J/Xbtz6O63MdPXFKAwtytXXJxcEckkH9cvUff9ilBl9L2FLnQA4KbekBAMlCgJJg4XYxjrdDx5u79Cb58eXnaf7aGjkUugcQbekBAHbAEk8CdbeLcbyFW6qhfBgAYGfMoCRIpF2M4yXaUg1t6QEAdkWAEmeBfJM3/3E4LjMn0yoG67XaQ5J6tlRDW3oAgB0RoMRRIvJNdn/q02PfmqB7/vh+yPO4XXlaPquCpRoAQEoiQImTQL5JvJd06r3NXap0WKoBAKQ6ApQ4SHS+yWu1HlUOL7Z8qSZae3va3wMA4oUAJQ621zUmtFLnxV2f6oczKyzdNyfc8lRph2WjaPcDANAbBChxkOgW8Y1Np7S9rlGVw4stCRy6W57yeJs1f22NbvpquZ7YUtft/ZQpAwB6iz4ocZCMFvGHjjd322clEDhs2F0f9TyRlqcCtz35P12Dk473r1hfqzZ/ym2SDQCwEQKUOLigvEhFBdkJfc5BBbkRAwtD5gKHaMtThqRIpwi3EzIAALEiQImDLKdDV44/MyHP5VD7Eo4cipr3YiZwsGp5ip2QAQC9QYASJ1UV7oQ91/JZFTp0vMXUsR5f5MDBquUpdkIGAPQGAUoctPkNvf3R4bg/T1FBdjAhtfEzcwFKtOMuKC9SqStP3dX8OCRFKggKzOiwEzIAoDcIUCy2YXe9pt6/Wb/c9I+4Pk9hXh+9vbQqWC1TVJBj6nHRjstyOrR8VoUkdQlSAt/f+JVyOSLcz07IAIDeIkCxUKCKJtoyihUmDB2onfuPBpNe3a58U48zc1y0nY6XXlbBTsgAgLhyGIaRcvWgPp9PLpdLXq9XhYWFyR6OpPZlnYse2JzQBm3SFz1OplW4oz5/qStPW5dcbHp2g06yAAArxfL+TYBikep9R3Ttk28n/HkD4cDqeRMlqdv9fxyfH8PsBgAgWWJ5/2aJxyLJKqvt2BxtWoVbq+dNbC877qCUpRcAQIqh1b1FYi2rHdA3W94Tp7qd7RjQN1u5fZzy+KJX53RsjjZjdKmmVbhZegEApDQCFIsEynM93uaIuxi7C3P148vPk9S+HOOQQo4PhBErrxoTDDT+vLtev6veH3UMgVmcLKfD8p2NAQBIJJZ4LBKpPDdgcdWX9Oadl2jG6NKolTIzRpcGA41LTS7N0BwNAJAumEGxUCDoMLubsNnlmGizMw61BzY0RwMApAuqeOIgHuW3gR4rUvglIZJgAQB2R5lxmtqwu9707AwAAHYTy/s3SzwphAodAECmIEBJMVToAAAyAVU8AADAdghQAACA7VgeoKxcuVJf/vKX1b9/fw0ePFhXXHGF9uzZE3JMc3OzFixYoOLiYvXr109z5sxRQ0OD1UMBAAApyvIA5Y033tCCBQv09ttva+PGjTp16pT+5V/+RU1NTcFjFi9erPXr1+v555/XG2+8oYMHD+qqq66yeigAACBFxb3M+J///KcGDx6sN954Q1/96lfl9Xp1xhlnaN26dfrGN74hSfrggw80atQoVVdXa8qUKVHPmallxgAApDJb7Wbs9XolSUVF7V1Od+7cqVOnTqmqqip4zMiRIzV06FBVV1eHPUdLS4t8Pl/IFwAASF9xDVD8fr8WLVqkqVOnavTo0ZIkj8ejnJwcDRgwIOTYkpISeTyesOdZuXKlXC5X8GvIkCHxHDYAAEiyuAYoCxYs0O7du/Xcc8/16jxLly6V1+sNfh04cMCiEQIAADuKW6O2hQsX6tVXX9WWLVt01llnBW93u91qbW3VsWPHQmZRGhoa5Ha7w54rNzdXubm58RoqAACwGctnUAzD0MKFC/Xiiy9q8+bNKi8vD7n//PPPV3Z2tjZt2hS8bc+ePfrkk09UWVlp9XAAAEAKsnwGZcGCBVq3bp1efvll9e/fP5hX4nK5lJ+fL5fLpRtuuEG33367ioqKVFhYqO9///uqrKw0VcEDAADSn+Vlxg5H+I3rnnrqKX3nO9+R1N6o7T/+4z/07LPPqqWlRdOnT9fjjz/e7RJPZ5QZAwCQemJ5/457H5R4IEABACD12KoPCgAAQKwIUAAAgO0QoAAAANshQAEAALZDgAIAAGyHAAUAANgOAQoAALAdAhQAAGA7BCgAAMB2CFAAAIDtEKAAAADbIUABAAC2Q4ACAABshwAFAADYDgEKAACwHQIUAABgOwQoAADAdghQAACA7RCgAAAA2yFAAQAAtkOAAgAAbIcABQAA2A4BCgAAsB0CFAAAYDsEKAAAwHYIUAAAgO0QoAAAANshQAEAALZDgAIAAGyHAAUAANgOAQoAALAdAhQAAGA7BCgAAMB2CFAAAIDtEKAAAADbIUABAAC2Q4ACAABshwAFAADYDgEKAACwHQIUAABgOwQoAADAdghQAACA7RCgAAAA2yFAAQAAtkOAAgAAbIcABQAA2A4BCgAAsB0CFAAAYDsEKAAAwHYIUAAAgO30SfYA7KTNb+jtfUdU/dFhSQ5VDi/WlHOKleV0mH789rpGHTrerMH983RBeZHpx1rJ7DjsMl4AADpLaoDy2GOP6ac//ak8Ho/GjRunRx99VBdccEFSxrJhd73ufOE9HTtxKnjbqtf/oQF9s3X/VWM0Y3Rp1MevWF+rem9z8LZSV56Wz6qI+lgrmR2HXcYLAEA4SVvi+f3vf6/bb79dy5cvV01NjcaNG6fp06fr0KFDCR/Lht31unltTUhwEnDsxCndvLZGG3bXR3z8/LU1IW/2kuTxNmt+lMdayew47DJeAAC6k7QA5ec//7luvPFGXX/99aqoqNCaNWvUt29f/eY3v0noONr8hn78yt+jHrdifa3a/EbYx69YX6uu9yh4W3ePtZLZcbSe9ttivAAARJKUAKW1tVU7d+5UVVXVFwNxOlVVVaXq6uoux7e0tMjn84V8WWV7XaM8vpaox9V7m7W9rjHs4zvPRHRkRHislcyO47+qP7bFeAEAiCQpAcrhw4fV1tamkpKSkNtLSkrk8Xi6HL9y5Uq5XK7g15AhQywby6Hj3b9ZmznW7ONjeZ6eMHv+/Y0nLD0fAADxkBJlxkuXLpXX6w1+HThwwLJzD+6f16tjzT4+lufpCbPnP7uor6XnAwAgHpISoAwaNEhZWVlqaGgIub2hoUFut7vL8bm5uSosLAz5ssoF5UVyF+ZGPa7U1V6GG+7xpa48dVec64jwWCuZHce3K4fZYrwAAESSlAAlJydH559/vjZt2hS8ze/3a9OmTaqsrEzoWLKcDv348vOiHrd8VkXYHiFZToeWz6qQpC5v+oHvu3uslcyOI6eP0xbjBQAgkqQt8dx+++168skn9fTTT+v999/X/Pnz1dTUpOuvvz7hY5kxulRr5k3UgL7ZXe4b2Ddba+ZNjNgbZMboUq2eN1FuV+iyiNuVp9VRHmsls+Owy3gBAOiOwzCMpNWTrlq1Ktiobfz48XrkkUc0efLkqI/z+XxyuVzyer2WLvfQSZZOsgCA+Inl/TupAUpPxStAAQAA8RPL+3dKVPEAAIDMQoACAABshwAFAADYDgEKAACwHQIUAABgOwQoAADAdghQAACA7RCgAAAA2yFAAQAAttMn2QPoiUDzW5/Pl+SRAAAAswLv22aa2KdkgHL8+HFJ0pAhQ5I8EgAAEKvjx4/L5XJFPCYl9+Lx+/06ePCg+vfvL4fD/pvb+Xw+DRkyRAcOHGDvIBvhutgP18SeuC72lIrXxTAMHT9+XGVlZXI6I2eZpOQMitPp1FlnnZXsYcSssLAwZf6IMgnXxX64JvbEdbGnVLsu0WZOAkiSBQAAtkOAAgAAbIcAJQFyc3O1fPly5ebmJnso6IDrYj9cE3viuthTul+XlEySBQAA6Y0ZFAAAYDsEKAAAwHYIUAAAgO0QoAAAANshQImT+++/Xw6HQ4sWLQre1tzcrAULFqi4uFj9+vXTnDlz1NDQkLxBZohPP/1U8+bNU3FxsfLz8zVmzBi98847wfsNw9Ddd9+t0tJS5efnq6qqSnv37k3iiNNfW1ubli1bpvLycuXn52v48OG65557Qvbn4LrE15YtWzRr1iyVlZXJ4XDopZdeCrnfzO+/sbFRc+fOVWFhoQYMGKAbbrhBn332WQJ/ivQT6bqcOnVKS5Ys0ZgxY1RQUKCysjL9+7//uw4ePBhyjnS5LgQocbBjxw796le/0tixY0NuX7x4sdavX6/nn39eb7zxhg4ePKirrroqSaPMDEePHtXUqVOVnZ2tP//5z6qtrdVDDz2kgQMHBo958MEH9cgjj2jNmjXatm2bCgoKNH36dDU3Nydx5OntgQce0OrVq7Vq1Sq9//77euCBB/Tggw/q0UcfDR7DdYmvpqYmjRs3To899ljY+838/ufOnau///3v2rhxo1599VVt2bJFN910U6J+hLQU6bqcOHFCNTU1WrZsmWpqavTCCy9oz549uvzyy0OOS5vrYsBSx48fN0aMGGFs3LjR+NrXvmbcdttthmEYxrFjx4zs7Gzj+eefDx77/vvvG5KM6urqJI02/S1ZssS46KKLur3f7/cbbrfb+OlPfxq87dixY0Zubq7x7LPPJmKIGWnmzJnGd7/73ZDbrrrqKmPu3LmGYXBdEk2S8eKLLwa/N/P7r62tNSQZO3bsCB7z5z//2XA4HMann36asLGns87XJZzt27cbkoz9+/cbhpFe14UZFIstWLBAM2fOVFVVVcjtO3fu1KlTp0JuHzlypIYOHarq6upEDzNjvPLKK5o0aZK++c1vavDgwZowYYKefPLJ4P11dXXyeDwh18Xlcmny5Mlclzi68MILtWnTJn344YeSpL/+9a/aunWrLr30Uklcl2Qz8/uvrq7WgAEDNGnSpOAxVVVVcjqd2rZtW8LHnKm8Xq8cDocGDBggKb2uS0puFmhXzz33nGpqarRjx44u93k8HuXk5AT/iAJKSkrk8XgSNMLM89FHH2n16tW6/fbb9cMf/lA7duzQrbfeqpycHF133XXB331JSUnI47gu8XXnnXfK5/Np5MiRysrKUltbm+677z7NnTtXkrguSWbm9+/xeDR48OCQ+/v06aOioiKuUYI0NzdryZIluvbaa4ObBabTdSFAsciBAwd02223aePGjcrLy0v2cPA5v9+vSZMm6Sc/+YkkacKECdq9e7fWrFmj6667Lsmjy1x/+MMf9Mwzz2jdunU677zztGvXLi1atEhlZWVcF8CEU6dO6eqrr5ZhGFq9enWyhxMXLPFYZOfOnTp06JAmTpyoPn36qE+fPnrjjTf0yCOPqE+fPiopKVFra6uOHTsW8riGhga53e7kDDoDlJaWqqKiIuS2UaNG6ZNPPpGk4O++czUV1yW+fvCDH+jOO+/UNddcozFjxujb3/62Fi9erJUrV0riuiSbmd+/2+3WoUOHQu4/ffq0GhsbuUZxFghO9u/fr40bNwZnT6T0ui4EKBa55JJL9N5772nXrl3Br0mTJmnu3LnBf2dnZ2vTpk3Bx+zZs0effPKJKisrkzjy9DZ16lTt2bMn5LYPP/xQZ599tiSpvLxcbrc75Lr4fD5t27aN6xJHJ06ckNMZ+vKTlZUlv98vieuSbGZ+/5WVlTp27Jh27twZPGbz5s3y+/2aPHlywsecKQLByd69e/Xaa6+puLg45P60ui7JztJNZx2reAzDMG6++WZj6NChxubNm4133nnHqKysNCorK5M3wAywfft2o0+fPsZ9991n7N2713jmmWeMvn37GmvXrg0ec//99xsDBgwwXn75ZeNvf/ubMXv2bKO8vNw4efJkEkee3q677jrjzDPPNF599VWjrq7OeOGFF4xBgwYZd9xxR/AYrkt8HT9+3Hj33XeNd99915Bk/PznPzfefffdYDWImd//jBkzjAkTJhjbtm0ztm7daowYMcK49tprk/UjpYVI16W1tdW4/PLLjbPOOsvYtWuXUV9fH/xqaWkJniNdrgsBShx1DlBOnjxp3HLLLcbAgQONvn37GldeeaVRX1+fvAFmiPXr1xujR482cnNzjZEjRxpPPPFEyP1+v99YtmyZUVJSYuTm5hqXXHKJsWfPniSNNjP4fD7jtttuM4YOHWrk5eUZ55xzjvGjH/0o5EWW6xJfr7/+uiGpy9d1111nGIa53/+RI0eMa6+91ujXr59RWFhoXH/99cbx48eT8NOkj0jXpa6uLux9kozXX389eI50uS4Ow+jQuhEAAMAGyEEBAAC2Q4ACAABshwAFAADYDgEKAACwHQIUAABgOwQoAADAdghQAACA7RCgAAAA2yFAAQAAtkOAAgAAbIcABQAA2A4BCgAAsJ3/H7zkc+G34UoTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Heteroscadacity\n",
        "plt.scatter((y_pred),(y_test_s)-(y_pred))\n",
        "plt.xlabel('Predicted selling price')\n",
        "plt.ylabel('residuals')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "w4h0tae7QAEu",
        "outputId": "14df2734-d400-4fc6-f1d3-053548b26141"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'residuals')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "nstMNBx7SYRL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Cross validation\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "X_train, X_test, y_train, y_test = pre_processing2(df_m3.drop\n",
        "      (['Av_sales/Assorment/StoreType/Month','Customers','Promo2',\n",
        "      'Promo2SinceYear','CompetitionOpenSinceYear'], axis=1\n",
        "      ).sample(100000, random_state=42))\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "X_train_s = scaler.fit_transform(X_train)\n",
        "X_test_s = scaler.transform(X_test)\n",
        "y_train_s = np.sqrt(y_train)\n",
        "y_test_s = np.sqrt(y_test)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "lasso = Lasso()\n",
        "parameters = {'alpha': [1e-15,1e-13,1e-10,1e-8,1e-5,1e-4,1e-3,1e-2,1e-1,1,5,10,20,30,40,45,50,55,60,100]}\n",
        "lasso_regressor = GridSearchCV(lasso, parameters, scoring='r2', cv=3)\n",
        "lasso_regressor.fit(X_train_s, y_train_s)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "1cu28EMMnSBV",
        "outputId": "45826d7b-e5a3-415d-9a16-5d7f91028579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=Lasso(),\n",
              "             param_grid={'alpha': [1e-15, 1e-13, 1e-10, 1e-08, 1e-05, 0.0001,\n",
              "                                   0.001, 0.01, 0.1, 1, 5, 10, 20, 30, 40, 45,\n",
              "                                   50, 55, 60, 100]},\n",
              "             scoring='r2')"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=Lasso(),\n",
              "             param_grid={&#x27;alpha&#x27;: [1e-15, 1e-13, 1e-10, 1e-08, 1e-05, 0.0001,\n",
              "                                   0.001, 0.01, 0.1, 1, 5, 10, 20, 30, 40, 45,\n",
              "                                   50, 55, 60, 100]},\n",
              "             scoring=&#x27;r2&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=Lasso(),\n",
              "             param_grid={&#x27;alpha&#x27;: [1e-15, 1e-13, 1e-10, 1e-08, 1e-05, 0.0001,\n",
              "                                   0.001, 0.01, 0.1, 1, 5, 10, 20, 30, 40, 45,\n",
              "                                   50, 55, 60, 100]},\n",
              "             scoring=&#x27;r2&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Train set R2 score:', lasso_regressor.score(X_train_s, y_train_s))\n",
        "print('Test set R2 score:', lasso_regressor.score(X_test_s, y_test_s))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hd3J8hDntID",
        "outputId": "48284666-b634-4eb7-845f-56095b0fa30e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set R2 score: 0.28424275798514775\n",
            "Test set R2 score: 0.2782779604901786\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "\n",
        "# Hyperprarameter tuning\n",
        "ridge = Ridge()\n",
        "parameters = {'alpha': [1e-15,1e-10,1e-8,1e-5,1e-4,1e-3,1e-2,1,5,10,20,30,40,45,50,55,60,100]}\n",
        "ridge_regressor = GridSearchCV(ridge, parameters, scoring='neg_mean_squared_error', cv=3)\n",
        "ridge_regressor.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "AWKaNVA-qYEo",
        "outputId": "f3eba072-24d3-4af8-842d-bb83e2c79484"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3, estimator=Ridge(),\n",
              "             param_grid={'alpha': [1e-15, 1e-10, 1e-08, 1e-05, 0.0001, 0.001,\n",
              "                                   0.01, 1, 5, 10, 20, 30, 40, 45, 50, 55, 60,\n",
              "                                   100]},\n",
              "             scoring='neg_mean_squared_error')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=Ridge(),\n",
              "             param_grid={&#x27;alpha&#x27;: [1e-15, 1e-10, 1e-08, 1e-05, 0.0001, 0.001,\n",
              "                                   0.01, 1, 5, 10, 20, 30, 40, 45, 50, 55, 60,\n",
              "                                   100]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=Ridge(),\n",
              "             param_grid={&#x27;alpha&#x27;: [1e-15, 1e-10, 1e-08, 1e-05, 0.0001, 0.001,\n",
              "                                   0.01, 1, 5, 10, 20, 30, 40, 45, 50, 55, 60,\n",
              "                                   100]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "print('Train set R2 score:', ridge_regressor.score(X_train_s, y_train_s))\n",
        "print('Test set R2 score:', ridge_regressor.score(X_test_s, y_test_s))\n",
        "R2_score = r2_score(y_test_s, ridge_regressor.predict(X_test_s))\n",
        "print(R2_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jN5CVzEqqfA8",
        "outputId": "d9e1bbd1-4a35-4e10-e6a6-9989f6057e75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set R2 score: -145137927166.89316\n",
            "Test set R2 score: -145142698449.9155\n",
            "-625266538.2794416\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import ElasticNet\n",
        "\n",
        "elastic = ElasticNet()\n",
        "parameters = {'alpha': [1e-15,1e-13,1e-10,1e-8,1e-5,1e-4,1e-3,1e-2,1e-1,1,5,10,20,30,40,45,50,55,60,100],'l1_ratio':[0.3,0.4,0.5,0.6,0.7,0.8]}\n",
        "elastic_regressor = GridSearchCV(elastic, parameters, scoring='neg_mean_squared_error',cv=5)\n",
        "elastic_regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "BmBN-bp6s6IY",
        "outputId": "f2830187-4f22-4807-fb78-616136a3cf9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=ElasticNet(),\n",
              "             param_grid={'alpha': [1e-15, 1e-13, 1e-10, 1e-08, 1e-05, 0.0001,\n",
              "                                   0.001, 0.01, 0.1, 1, 5, 10, 20, 30, 40, 45,\n",
              "                                   50, 55, 60, 100],\n",
              "                         'l1_ratio': [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]},\n",
              "             scoring='neg_mean_squared_error')"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=ElasticNet(),\n",
              "             param_grid={&#x27;alpha&#x27;: [1e-15, 1e-13, 1e-10, 1e-08, 1e-05, 0.0001,\n",
              "                                   0.001, 0.01, 0.1, 1, 5, 10, 20, 30, 40, 45,\n",
              "                                   50, 55, 60, 100],\n",
              "                         &#x27;l1_ratio&#x27;: [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=ElasticNet(),\n",
              "             param_grid={&#x27;alpha&#x27;: [1e-15, 1e-13, 1e-10, 1e-08, 1e-05, 0.0001,\n",
              "                                   0.001, 0.01, 0.1, 1, 5, 10, 20, 30, 40, 45,\n",
              "                                   50, 55, 60, 100],\n",
              "                         &#x27;l1_ratio&#x27;: [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]},\n",
              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Train set score:', elastic_regressor.score(X_train_s, y_train_s))\n",
        "print('Test set score:', elastic_regressor.score(X_test_s, y_test_s))\n",
        "R2_score = r2_score(y_test_s, elastic_regressor.predict(X_test_s))\n",
        "print(R2_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFlMB3Xps-kC",
        "outputId": "8b2ea23e-9b17-4e1c-ca3c-b65e882e4ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set score: -145088280136.7602\n",
            "Test set score: -145093057947.21597\n",
            "-625052689.800238\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering"
      ],
      "metadata": {
        "id": "fJQBR_tX8s0I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_m2.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Thakioy_XsQ",
        "outputId": "1cdd690e-7831-4dd1-9206-6a74f3c0296a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['DayOfWeek', 'Date', 'Sales', 'Customers', 'Promo', 'StateHoliday',\n",
              "       'SchoolHoliday', 'StoreType', 'Assortment', 'CompetitionDistance',\n",
              "       'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2',\n",
              "       'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval', 'IsPromoMonth'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if df['Date'].dtype != 'datetime64[ns]':\n",
        "    df['Date'] = pd.to_datetime(df['Date'])\n",
        "\n",
        "df['Date'].dt.month_name().head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBU4df61Afn8",
        "outputId": "93a75162-8e99-4490-ef2f-47510d4af3be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Store\n",
              "1    July\n",
              "Name: Date, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m2['IsPromoMonth'] = df_m2.apply(lambda row: 1 if str(row['PromoInterval']) != 'nan' and row['Date'].strftime('%b') in row['PromoInterval'] else 0, axis=1)\n",
        "df_m2['IsPromoMonth']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7U4uWiUDFZR",
        "outputId": "1046a00c-0d23-4d79-9d10-976fa15af1cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Store\n",
              "1       0\n",
              "2       1\n",
              "3       1\n",
              "4       0\n",
              "5       0\n",
              "       ..\n",
              "682     0\n",
              "733     0\n",
              "769     1\n",
              "948     0\n",
              "1097    0\n",
              "Name: IsPromoMonth, Length: 844392, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m2[['IsPromoMonth','Date','PromoInterval']].sample(5)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Lp66FgI7yuYf",
        "outputId": "8364e0c4-6a2c-43fd-b7ee-7e4bf576c2c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       IsPromoMonth       Date     PromoInterval\n",
              "Store                                           \n",
              "551               0 2014-01-24               NaN\n",
              "1                 0 2013-05-16               NaN\n",
              "1077              1 2014-03-18  Mar,Jun,Sept,Dec\n",
              "1092              0 2013-08-13   Jan,Apr,Jul,Oct\n",
              "305               0 2015-05-08  Mar,Jun,Sept,Dec"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8528bd8f-90b6-4cbf-b9a1-15cf3c1a1cf3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>IsPromoMonth</th>\n",
              "      <th>Date</th>\n",
              "      <th>PromoInterval</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Store</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>551</th>\n",
              "      <td>0</td>\n",
              "      <td>2014-01-24</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2013-05-16</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077</th>\n",
              "      <td>1</td>\n",
              "      <td>2014-03-18</td>\n",
              "      <td>Mar,Jun,Sept,Dec</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1092</th>\n",
              "      <td>0</td>\n",
              "      <td>2013-08-13</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>0</td>\n",
              "      <td>2015-05-08</td>\n",
              "      <td>Mar,Jun,Sept,Dec</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8528bd8f-90b6-4cbf-b9a1-15cf3c1a1cf3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8528bd8f-90b6-4cbf-b9a1-15cf3c1a1cf3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8528bd8f-90b6-4cbf-b9a1-15cf3c1a1cf3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m2.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Z8bEEnFw0pZ3",
        "outputId": "56c853c4-9229-4e55-911c-fafc5eff73d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       DayOfWeek       Date  Sales  Customers  Promo StateHoliday  \\\n",
              "Store                                                               \n",
              "1              5 2015-07-31   5263        555      1            0   \n",
              "2              5 2015-07-31   6064        625      1            0   \n",
              "3              5 2015-07-31   8314        821      1            0   \n",
              "4              5 2015-07-31  13995       1498      1            0   \n",
              "5              5 2015-07-31   4822        559      1            0   \n",
              "\n",
              "       SchoolHoliday StoreType Assortment  CompetitionDistance  \\\n",
              "Store                                                            \n",
              "1                  1         c          a               1270.0   \n",
              "2                  1         a          a                570.0   \n",
              "3                  1         a          a              14130.0   \n",
              "4                  1         c          c                620.0   \n",
              "5                  1         a          a              29910.0   \n",
              "\n",
              "       CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
              "Store                                                                \n",
              "1                            9.0                    2008.0       0   \n",
              "2                           11.0                    2007.0       1   \n",
              "3                           12.0                    2006.0       1   \n",
              "4                            9.0                    2009.0       0   \n",
              "5                            4.0                    2015.0       0   \n",
              "\n",
              "       Promo2SinceWeek  Promo2SinceYear    PromoInterval  IsPromoMonth  \n",
              "Store                                                                   \n",
              "1                  NaN              NaN              NaN             0  \n",
              "2                 13.0           2010.0  Jan,Apr,Jul,Oct             1  \n",
              "3                 14.0           2011.0  Jan,Apr,Jul,Oct             1  \n",
              "4                  NaN              NaN              NaN             0  \n",
              "5                  NaN              NaN              NaN             0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-936872d0-408c-4363-b70b-0d40fda5849c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>DayOfWeek</th>\n",
              "      <th>Date</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Customers</th>\n",
              "      <th>Promo</th>\n",
              "      <th>StateHoliday</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>CompetitionDistance</th>\n",
              "      <th>CompetitionOpenSinceMonth</th>\n",
              "      <th>CompetitionOpenSinceYear</th>\n",
              "      <th>Promo2</th>\n",
              "      <th>Promo2SinceWeek</th>\n",
              "      <th>Promo2SinceYear</th>\n",
              "      <th>PromoInterval</th>\n",
              "      <th>IsPromoMonth</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Store</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-07-31</td>\n",
              "      <td>5263</td>\n",
              "      <td>555</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>c</td>\n",
              "      <td>a</td>\n",
              "      <td>1270.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-07-31</td>\n",
              "      <td>6064</td>\n",
              "      <td>625</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>570.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-07-31</td>\n",
              "      <td>8314</td>\n",
              "      <td>821</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>14130.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2011.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-07-31</td>\n",
              "      <td>13995</td>\n",
              "      <td>1498</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>c</td>\n",
              "      <td>c</td>\n",
              "      <td>620.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>2015-07-31</td>\n",
              "      <td>4822</td>\n",
              "      <td>559</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>29910.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-936872d0-408c-4363-b70b-0d40fda5849c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-936872d0-408c-4363-b70b-0d40fda5849c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-936872d0-408c-4363-b70b-0d40fda5849c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m2.groupby(['StoreType','Assortment'])['Sales'].mean().reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "ZkeJvLDsFLvb",
        "outputId": "b7425a75-cfff-4dd7-a053-43b88a734d0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   StoreType Assortment         Sales\n",
              "0          a          a   6537.617613\n",
              "1          a          b           NaN\n",
              "2          a          c   7573.379923\n",
              "3          b          a  11133.992979\n",
              "4          b          b   8639.346322\n",
              "5          b          c  17969.556263\n",
              "6          c          a   6834.931456\n",
              "7          c          b           NaN\n",
              "8          c          c   7037.525130\n",
              "9          d          a   6433.619038\n",
              "10         d          b           NaN\n",
              "11         d          c   7043.363895"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-de7554ad-cf54-4fca-a4df-4f2b7d18f7cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>6537.617613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a</td>\n",
              "      <td>c</td>\n",
              "      <td>7573.379923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b</td>\n",
              "      <td>a</td>\n",
              "      <td>11133.992979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b</td>\n",
              "      <td>b</td>\n",
              "      <td>8639.346322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>b</td>\n",
              "      <td>c</td>\n",
              "      <td>17969.556263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>c</td>\n",
              "      <td>a</td>\n",
              "      <td>6834.931456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>c</td>\n",
              "      <td>c</td>\n",
              "      <td>7037.525130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>d</td>\n",
              "      <td>a</td>\n",
              "      <td>6433.619038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>7043.363895</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de7554ad-cf54-4fca-a4df-4f2b7d18f7cb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de7554ad-cf54-4fca-a4df-4f2b7d18f7cb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de7554ad-cf54-4fca-a4df-4f2b7d18f7cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Describe_df['Average_sales_per_store_type_per_month'] = df_m2.groupby(['StoreType'])['Sales'].mean().reset_index()\n",
        "Describe_df['Average_customers_per_store_type_per_month'] = df_m2.groupby(['StoreType'])['Customers'].mean().reset_index()"
      ],
      "metadata": {
        "id": "XqIHceRf0-wI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "outputId": "48db5b23-c1ae-4cea-87e3-f92bb9d1a2df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-d29d5c9af2cb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDescribe_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Average_sales_per_store_type_per_month'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_m2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'StoreType'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mDescribe_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Average_customers_per_store_type_per_month'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_m2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'StoreType'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Customers'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'Describe_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m2['Month'] = df_m2['Date'].dt.month\n",
        "describe_1 = df_m2.groupby(['StoreType','Assortment','Month'])['Sales'].mean().reset_index()\n",
        "describe_1.rename(columns={'Sales':'Av_sales/Assorment/StoreType/Month'}, inplace=True)\n",
        "describe_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OlLIiK9R3uOC",
        "outputId": "ed1ab4f0-45d2-4f33-bd03-6a6b2961af3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    StoreType Assortment  Month  Av_sales/Assorment/StoreType/Month\n",
              "0           a          a      1                         6236.886230\n",
              "1           a          a      2                         6244.516275\n",
              "2           a          a      3                         6582.073152\n",
              "3           a          a      4                         6678.566872\n",
              "4           a          a      5                         6666.468551\n",
              "..        ...        ...    ...                                 ...\n",
              "139         d          c      8                         6720.111354\n",
              "140         d          c      9                         6565.278371\n",
              "141         d          c     10                         6581.130684\n",
              "142         d          c     11                         7240.006946\n",
              "143         d          c     12                         8760.459510\n",
              "\n",
              "[144 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6cda026c-38fc-4c24-a5bd-4edcd64135e5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>Month</th>\n",
              "      <th>Av_sales/Assorment/StoreType/Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>1</td>\n",
              "      <td>6236.886230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>2</td>\n",
              "      <td>6244.516275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>3</td>\n",
              "      <td>6582.073152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>4</td>\n",
              "      <td>6678.566872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>5</td>\n",
              "      <td>6666.468551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>8</td>\n",
              "      <td>6720.111354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>9</td>\n",
              "      <td>6565.278371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>10</td>\n",
              "      <td>6581.130684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>11</td>\n",
              "      <td>7240.006946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>12</td>\n",
              "      <td>8760.459510</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>144 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6cda026c-38fc-4c24-a5bd-4edcd64135e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6cda026c-38fc-4c24-a5bd-4edcd64135e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6cda026c-38fc-4c24-a5bd-4edcd64135e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "describe_2 = df_m2.groupby(['StoreType','Assortment','Month'])['Customers'].mean().reset_index()\n",
        "describe_2.rename(columns={'Customers':'Av_customers/Assorment/StoreType/Month'}, inplace=True)\n",
        "describe_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2MAElP4W4EuQ",
        "outputId": "62758165-476f-4dd3-da84-16ef931be11d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    StoreType Assortment  Month  Av_customers/Assorment/StoreType/Month\n",
              "0           a          a      1                              727.316091\n",
              "1           a          a      2                              732.498959\n",
              "2           a          a      3                              760.325699\n",
              "3           a          a      4                              777.427195\n",
              "4           a          a      5                              772.706489\n",
              "..        ...        ...    ...                                     ...\n",
              "139         d          c      8                              607.861639\n",
              "140         d          c      9                              603.830170\n",
              "141         d          c     10                              601.229984\n",
              "142         d          c     11                              630.311758\n",
              "143         d          c     12                              721.547909\n",
              "\n",
              "[144 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-af8d0e94-007e-4d3e-8fea-49ed487db259\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>Month</th>\n",
              "      <th>Av_customers/Assorment/StoreType/Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>1</td>\n",
              "      <td>727.316091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>2</td>\n",
              "      <td>732.498959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>3</td>\n",
              "      <td>760.325699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>4</td>\n",
              "      <td>777.427195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>5</td>\n",
              "      <td>772.706489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>8</td>\n",
              "      <td>607.861639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>9</td>\n",
              "      <td>603.830170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>10</td>\n",
              "      <td>601.229984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>11</td>\n",
              "      <td>630.311758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>12</td>\n",
              "      <td>721.547909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>144 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af8d0e94-007e-4d3e-8fea-49ed487db259')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-af8d0e94-007e-4d3e-8fea-49ed487db259 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-af8d0e94-007e-4d3e-8fea-49ed487db259');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "describe_2[describe_2['Av_customers/Assorment/StoreType/Month'].isna()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tWfy6cb_D-9c",
        "outputId": "43e4b091-6752-4735-c79e-aebd7d1a136f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    StoreType Assortment  Month  Av_customers/Assorment/StoreType/Month\n",
              "12          a          b      1                                     NaN\n",
              "13          a          b      2                                     NaN\n",
              "14          a          b      3                                     NaN\n",
              "15          a          b      4                                     NaN\n",
              "16          a          b      5                                     NaN\n",
              "17          a          b      6                                     NaN\n",
              "18          a          b      7                                     NaN\n",
              "19          a          b      8                                     NaN\n",
              "20          a          b      9                                     NaN\n",
              "21          a          b     10                                     NaN\n",
              "22          a          b     11                                     NaN\n",
              "23          a          b     12                                     NaN\n",
              "84          c          b      1                                     NaN\n",
              "85          c          b      2                                     NaN\n",
              "86          c          b      3                                     NaN\n",
              "87          c          b      4                                     NaN\n",
              "88          c          b      5                                     NaN\n",
              "89          c          b      6                                     NaN\n",
              "90          c          b      7                                     NaN\n",
              "91          c          b      8                                     NaN\n",
              "92          c          b      9                                     NaN\n",
              "93          c          b     10                                     NaN\n",
              "94          c          b     11                                     NaN\n",
              "95          c          b     12                                     NaN\n",
              "120         d          b      1                                     NaN\n",
              "121         d          b      2                                     NaN\n",
              "122         d          b      3                                     NaN\n",
              "123         d          b      4                                     NaN\n",
              "124         d          b      5                                     NaN\n",
              "125         d          b      6                                     NaN\n",
              "126         d          b      7                                     NaN\n",
              "127         d          b      8                                     NaN\n",
              "128         d          b      9                                     NaN\n",
              "129         d          b     10                                     NaN\n",
              "130         d          b     11                                     NaN\n",
              "131         d          b     12                                     NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7811676-b61a-4501-8c5a-d16317b43ea4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>Month</th>\n",
              "      <th>Av_customers/Assorment/StoreType/Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>a</td>\n",
              "      <td>b</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>d</td>\n",
              "      <td>b</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7811676-b61a-4501-8c5a-d16317b43ea4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f7811676-b61a-4501-8c5a-d16317b43ea4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f7811676-b61a-4501-8c5a-d16317b43ea4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "describe =describe_1.join(describe_2['Av_customers/Assorment/StoreType/Month'])\n",
        "describe.dropna(inplace=True)\n",
        "describe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "_IuY3S5j_kYW",
        "outputId": "ac7a6b9a-5a46-4b30-975e-d3f710fb41d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    StoreType Assortment  Month  Av_sales/Assorment/StoreType/Month  \\\n",
              "0           a          a      1                         6236.886230   \n",
              "1           a          a      2                         6244.516275   \n",
              "2           a          a      3                         6582.073152   \n",
              "3           a          a      4                         6678.566872   \n",
              "4           a          a      5                         6666.468551   \n",
              "..        ...        ...    ...                                 ...   \n",
              "139         d          c      8                         6720.111354   \n",
              "140         d          c      9                         6565.278371   \n",
              "141         d          c     10                         6581.130684   \n",
              "142         d          c     11                         7240.006946   \n",
              "143         d          c     12                         8760.459510   \n",
              "\n",
              "     Av_customers/Assorment/StoreType/Month  \n",
              "0                                727.316091  \n",
              "1                                732.498959  \n",
              "2                                760.325699  \n",
              "3                                777.427195  \n",
              "4                                772.706489  \n",
              "..                                      ...  \n",
              "139                              607.861639  \n",
              "140                              603.830170  \n",
              "141                              601.229984  \n",
              "142                              630.311758  \n",
              "143                              721.547909  \n",
              "\n",
              "[108 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e1add517-e8fc-4bca-a826-1cc3770abeda\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>Month</th>\n",
              "      <th>Av_sales/Assorment/StoreType/Month</th>\n",
              "      <th>Av_customers/Assorment/StoreType/Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>1</td>\n",
              "      <td>6236.886230</td>\n",
              "      <td>727.316091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>2</td>\n",
              "      <td>6244.516275</td>\n",
              "      <td>732.498959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>3</td>\n",
              "      <td>6582.073152</td>\n",
              "      <td>760.325699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>4</td>\n",
              "      <td>6678.566872</td>\n",
              "      <td>777.427195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>5</td>\n",
              "      <td>6666.468551</td>\n",
              "      <td>772.706489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>8</td>\n",
              "      <td>6720.111354</td>\n",
              "      <td>607.861639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>9</td>\n",
              "      <td>6565.278371</td>\n",
              "      <td>603.830170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>10</td>\n",
              "      <td>6581.130684</td>\n",
              "      <td>601.229984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>11</td>\n",
              "      <td>7240.006946</td>\n",
              "      <td>630.311758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>12</td>\n",
              "      <td>8760.459510</td>\n",
              "      <td>721.547909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>108 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1add517-e8fc-4bca-a826-1cc3770abeda')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1add517-e8fc-4bca-a826-1cc3770abeda button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1add517-e8fc-4bca-a826-1cc3770abeda');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m3 = df_m2.merge(describe, on=['StoreType', 'Assortment', 'Month'])\n",
        "df_m3.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "mdf6f4er4RHO",
        "outputId": "8f7dc02f-8e27-4bb4-8d06-44e6dfd7f87c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        DayOfWeek       Date  Sales  Customers  Promo StateHoliday  \\\n",
              "157502          1 2014-06-23   5700        522      0            0   \n",
              "354266          3 2014-03-05   9615       1312      1            0   \n",
              "363610          3 2013-03-06  10102        748      1            0   \n",
              "98930           6 2015-06-06   7769       1113      0            0   \n",
              "546233          2 2014-01-14   7816        773      0            0   \n",
              "\n",
              "        SchoolHoliday StoreType Assortment  CompetitionDistance  \\\n",
              "157502              0         d          c                680.0   \n",
              "354266              0         a          a                240.0   \n",
              "363610              0         a          a              16680.0   \n",
              "98930               0         a          a                760.0   \n",
              "546233              0         a          c               1650.0   \n",
              "\n",
              "        CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
              "157502                        NaN                       NaN       1   \n",
              "354266                        NaN                       NaN       0   \n",
              "363610                        NaN                       NaN       1   \n",
              "98930                        12.0                    2005.0       0   \n",
              "546233                        NaN                       NaN       1   \n",
              "\n",
              "        Promo2SinceWeek  Promo2SinceYear    PromoInterval  IsPromoMonth  \\\n",
              "157502             22.0           2012.0  Jan,Apr,Jul,Oct             0   \n",
              "354266              NaN              NaN              NaN             0   \n",
              "363610             37.0           2009.0  Jan,Apr,Jul,Oct             0   \n",
              "98930               NaN              NaN              NaN             0   \n",
              "546233             40.0           2014.0  Jan,Apr,Jul,Oct             1   \n",
              "\n",
              "        Month  Av_sales/Assorment/StoreType/Month  \\\n",
              "157502      6                         7142.016738   \n",
              "354266      3                         6582.073152   \n",
              "363610      3                         6582.073152   \n",
              "98930       6                         6592.543231   \n",
              "546233      1                         7063.787827   \n",
              "\n",
              "        Av_customers/Assorment/StoreType/Month  \n",
              "157502                              619.754184  \n",
              "354266                              760.325699  \n",
              "363610                              760.325699  \n",
              "98930                               756.807368  \n",
              "546233                              802.650608  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e202c9f1-46bf-4b58-939b-88857757c04b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>DayOfWeek</th>\n",
              "      <th>Date</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Customers</th>\n",
              "      <th>Promo</th>\n",
              "      <th>StateHoliday</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>CompetitionDistance</th>\n",
              "      <th>CompetitionOpenSinceMonth</th>\n",
              "      <th>CompetitionOpenSinceYear</th>\n",
              "      <th>Promo2</th>\n",
              "      <th>Promo2SinceWeek</th>\n",
              "      <th>Promo2SinceYear</th>\n",
              "      <th>PromoInterval</th>\n",
              "      <th>IsPromoMonth</th>\n",
              "      <th>Month</th>\n",
              "      <th>Av_sales/Assorment/StoreType/Month</th>\n",
              "      <th>Av_customers/Assorment/StoreType/Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>157502</th>\n",
              "      <td>1</td>\n",
              "      <td>2014-06-23</td>\n",
              "      <td>5700</td>\n",
              "      <td>522</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>680.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>7142.016738</td>\n",
              "      <td>619.754184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354266</th>\n",
              "      <td>3</td>\n",
              "      <td>2014-03-05</td>\n",
              "      <td>9615</td>\n",
              "      <td>1312</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6582.073152</td>\n",
              "      <td>760.325699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363610</th>\n",
              "      <td>3</td>\n",
              "      <td>2013-03-06</td>\n",
              "      <td>10102</td>\n",
              "      <td>748</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>16680.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>37.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>6582.073152</td>\n",
              "      <td>760.325699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98930</th>\n",
              "      <td>6</td>\n",
              "      <td>2015-06-06</td>\n",
              "      <td>7769</td>\n",
              "      <td>1113</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>760.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2005.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6592.543231</td>\n",
              "      <td>756.807368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546233</th>\n",
              "      <td>2</td>\n",
              "      <td>2014-01-14</td>\n",
              "      <td>7816</td>\n",
              "      <td>773</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>c</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7063.787827</td>\n",
              "      <td>802.650608</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e202c9f1-46bf-4b58-939b-88857757c04b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e202c9f1-46bf-4b58-939b-88857757c04b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e202c9f1-46bf-4b58-939b-88857757c04b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m3['CompetitionDistanceBin'] = pd.cut(df_m3['CompetitionDistance'], 6, labels=['1','2','3','4','5','6'])"
      ],
      "metadata": {
        "id": "woVgU4lXeL69"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_m3.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "XqPQKJjIfVXQ",
        "outputId": "5e92e82f-ea82-45b8-8c1e-955310773ef7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        DayOfWeek       Date  Sales  Customers  Promo StateHoliday  \\\n",
              "107067          3 2014-06-11   4818        633      0            0   \n",
              "722090          3 2014-10-22   5243        437      1            0   \n",
              "441579          6 2013-02-23   5525        525      0            0   \n",
              "557295          6 2015-01-03   5937        611      0            0   \n",
              "322283          5 2014-04-04   7276        576      1            0   \n",
              "\n",
              "        SchoolHoliday StoreType Assortment  CompetitionDistance  ...  \\\n",
              "107067              1         a          a               2620.0  ...   \n",
              "722090              0         d          c              15140.0  ...   \n",
              "441579              0         a          a                850.0  ...   \n",
              "557295              0         d          a               1120.0  ...   \n",
              "322283              0         d          c               7520.0  ...   \n",
              "\n",
              "        CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \\\n",
              "107067                       NaN       1             45.0           2009.0   \n",
              "722090                    2006.0       1              1.0           2012.0   \n",
              "441579                    2012.0       1             40.0           2014.0   \n",
              "557295                    2007.0       1             31.0           2013.0   \n",
              "322283                    2014.0       1             45.0           2009.0   \n",
              "\n",
              "          PromoInterval IsPromoMonth  Month  \\\n",
              "107067  Feb,May,Aug,Nov            0      6   \n",
              "722090  Jan,Apr,Jul,Oct            1     10   \n",
              "441579  Jan,Apr,Jul,Oct            0      2   \n",
              "557295  Jan,Apr,Jul,Oct            1      1   \n",
              "322283  Feb,May,Aug,Nov            0      4   \n",
              "\n",
              "        Av_sales/Assorment/StoreType/Month  \\\n",
              "107067                         6592.543231   \n",
              "722090                         6581.130684   \n",
              "441579                         6244.516275   \n",
              "557295                         6099.456750   \n",
              "322283                         7118.883346   \n",
              "\n",
              "        Av_customers/Assorment/StoreType/Month  CompetitionDistanceBin  \n",
              "107067                              756.807368                       1  \n",
              "722090                              601.229984                       2  \n",
              "441579                              732.498959                       1  \n",
              "557295                              555.465542                       1  \n",
              "322283                              627.621512                       1  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ddaebb91-0f2f-4281-842f-93a22a8aa16b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>DayOfWeek</th>\n",
              "      <th>Date</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Customers</th>\n",
              "      <th>Promo</th>\n",
              "      <th>StateHoliday</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>StoreType</th>\n",
              "      <th>Assortment</th>\n",
              "      <th>CompetitionDistance</th>\n",
              "      <th>...</th>\n",
              "      <th>CompetitionOpenSinceYear</th>\n",
              "      <th>Promo2</th>\n",
              "      <th>Promo2SinceWeek</th>\n",
              "      <th>Promo2SinceYear</th>\n",
              "      <th>PromoInterval</th>\n",
              "      <th>IsPromoMonth</th>\n",
              "      <th>Month</th>\n",
              "      <th>Av_sales/Assorment/StoreType/Month</th>\n",
              "      <th>Av_customers/Assorment/StoreType/Month</th>\n",
              "      <th>CompetitionDistanceBin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>107067</th>\n",
              "      <td>3</td>\n",
              "      <td>2014-06-11</td>\n",
              "      <td>4818</td>\n",
              "      <td>633</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>2620.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>45.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Feb,May,Aug,Nov</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6592.543231</td>\n",
              "      <td>756.807368</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>722090</th>\n",
              "      <td>3</td>\n",
              "      <td>2014-10-22</td>\n",
              "      <td>5243</td>\n",
              "      <td>437</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>15140.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>6581.130684</td>\n",
              "      <td>601.229984</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>441579</th>\n",
              "      <td>6</td>\n",
              "      <td>2013-02-23</td>\n",
              "      <td>5525</td>\n",
              "      <td>525</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>a</td>\n",
              "      <td>850.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>1</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6244.516275</td>\n",
              "      <td>732.498959</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557295</th>\n",
              "      <td>6</td>\n",
              "      <td>2015-01-03</td>\n",
              "      <td>5937</td>\n",
              "      <td>611</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>d</td>\n",
              "      <td>a</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>1</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>Jan,Apr,Jul,Oct</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6099.456750</td>\n",
              "      <td>555.465542</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322283</th>\n",
              "      <td>5</td>\n",
              "      <td>2014-04-04</td>\n",
              "      <td>7276</td>\n",
              "      <td>576</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>d</td>\n",
              "      <td>c</td>\n",
              "      <td>7520.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>1</td>\n",
              "      <td>45.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>Feb,May,Aug,Nov</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>7118.883346</td>\n",
              "      <td>627.621512</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddaebb91-0f2f-4281-842f-93a22a8aa16b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ddaebb91-0f2f-4281-842f-93a22a8aa16b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ddaebb91-0f2f-4281-842f-93a22a8aa16b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m3['CompetitionOpenSinceYear'].apply(lambda x: str(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PzLuBr0LgVcH",
        "outputId": "efa6b7ef-b6dd-4af0-a962-a319ee82da11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         2008.0\n",
              "1         2003.0\n",
              "2         2009.0\n",
              "3         2014.0\n",
              "4         2005.0\n",
              "           ...  \n",
              "844387    2001.0\n",
              "844388    2009.0\n",
              "844389    2008.0\n",
              "844390    2010.0\n",
              "844391    2006.0\n",
              "Name: CompetitionOpenSinceYear, Length: 844392, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_m3.dropna(inplace=True)\n",
        "df_m3['CompetitionOpenSinceDay'] = df_m3['CompetitionOpenSinceYear'].apply(lambda x: str(x)[:4]) + '-'+ df_m3['CompetitionOpenSinceMonth'].apply(lambda x: str(int(x)))+'-01'\n",
        "df_m3['CompetitionOpenSinceDay'] = (df_m3['Date'] - pd.to_datetime(df_m3['CompetitionOpenSinceDay'])).dt.days\n",
        "df_m3['CompetitionOpenSinceDay']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-nZ_mpQgqpi",
        "outputId": "ad514016-dee8-43a5-d02b-0d6794418e06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4         3620\n",
              "11        3559\n",
              "18        2282\n",
              "22        1368\n",
              "26        7577\n",
              "          ... \n",
              "844378     853\n",
              "844380    2284\n",
              "844381    2465\n",
              "844387    4322\n",
              "844389    1734\n",
              "Name: CompetitionOpenSinceDay, Length: 267812, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ")"
      ],
      "metadata": {
        "id": "vxATIjiBiejq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "calculate_vif((df_m3).drop(['Promo2','Promo2SinceYear','Sales','CompetitionOpenSinceYear','Av_sales/Assorment/StoreType/Month'], axis=1))"
      ],
      "metadata": {
        "id": "Q5U9pd_RT1mS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Feature Selection"
      ],
      "metadata": {
        "id": "lO-5WADiSQvz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif\n",
        "\n",
        "def calculate_vif(df):\n",
        "    # Select numerical columns\n",
        "    numerical_columns = df.select_dtypes(include=['float64', 'int64'])\n",
        "\n",
        "    # Replace infinite values with NaN\n",
        "    numerical_columns.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "\n",
        "    # Drop rows with NaN values\n",
        "    numerical_columns.dropna(inplace=True)\n",
        "\n",
        "    # Remove duplicates\n",
        "    numerical_columns = numerical_columns.loc[:,~numerical_columns.columns.duplicated()]\n",
        "\n",
        "    # Create a DataFrame to store VIF results\n",
        "    vif_data = pd.DataFrame()\n",
        "    vif_data['Variable'] = numerical_columns.columns\n",
        "    vif_data['VIF'] = [vif(numerical_columns.values, i) for i in range(numerical_columns.shape[1])]\n",
        "\n",
        "    return vif_data.sort_values(by='VIF', ascending=False)\n",
        "\n",
        "calculate_vif(df_m2.drop(['Sales'], axis=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "MGrI_tdJSPj7",
        "outputId": "cd47fc35-4e0b-4e32-836a-621d735040d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Variable           VIF\n",
              "7                     Promo2  1.573677e+06\n",
              "2                      Promo  1.153423e+00\n",
              "1                  Customers  1.119583e+00\n",
              "0                  DayOfWeek  1.117165e+00\n",
              "4        CompetitionDistance  1.059632e+00\n",
              "9            Promo2SinceYear  1.037353e+00\n",
              "3              SchoolHoliday  1.020993e+00\n",
              "8            Promo2SinceWeek  1.019017e+00\n",
              "6   CompetitionOpenSinceYear  1.010080e+00\n",
              "5  CompetitionOpenSinceMonth  1.009137e+00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0b85e99b-c6b8-44a3-a474-143bff8701fa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Variable</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Promo2</td>\n",
              "      <td>1.573677e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Promo</td>\n",
              "      <td>1.153423e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Customers</td>\n",
              "      <td>1.119583e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DayOfWeek</td>\n",
              "      <td>1.117165e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CompetitionDistance</td>\n",
              "      <td>1.059632e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Promo2SinceYear</td>\n",
              "      <td>1.037353e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SchoolHoliday</td>\n",
              "      <td>1.020993e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Promo2SinceWeek</td>\n",
              "      <td>1.019017e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CompetitionOpenSinceYear</td>\n",
              "      <td>1.010080e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CompetitionOpenSinceMonth</td>\n",
              "      <td>1.009137e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b85e99b-c6b8-44a3-a474-143bff8701fa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0b85e99b-c6b8-44a3-a474-143bff8701fa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0b85e99b-c6b8-44a3-a474-143bff8701fa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    }
  ]
}