01_Walmart_sales.ipynb

{
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  "metadata": {
    "colab": {
      "name": "01-Walmart_sales.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/caroheymes/Jedha-walmart-project/blob/main/01_Walmart_sales.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mi-CDGeZy0w"
      },
      "source": [
        "<img src=\"https://www.bestdesigns.co/uploads/inspiration_images/4350/990__1511457498_404_walmart.png\" alt=\"WALMART LOGO\" />\n",
        "\n",
        "# Walmart : predict weekly sales\n",
        "\n",
        "## Company's Description 📇\n",
        "\n",
        "Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores from the United States, headquartered in Bentonville, Arkansas. The company was founded by Sam Walton in 1962.\n",
        "\n",
        "## Project 🚧\n",
        "\n",
        "Walmart's marketing service has asked you to build a machine learning model able to estimate the weekly sales in their stores, with the best precision possible on the predictions made. Such a model would help them understand better how the sales are influenced by economic indicators, and might be used to plan future marketing campaigns.\n",
        "\n",
        "## Goals 🎯\n",
        "\n",
        "The project can be divided into three steps:\n",
        "\n",
        "- Part 1 : make an EDA and all the necessary preprocessings to prepare data for machine learning\n",
        "- Part 2 : train a **linear regression model** (baseline)\n",
        "- Part 3 : avoid overfitting by training a **regularized regression model**\n",
        "\n",
        "## Scope of this project 🖼️\n",
        "\n",
        "For this project, you'll work with a dataset that contains information about weekly sales achieved by different Walmart stores, and other variables such as the unemployment rate or the fuel price, that might be useful for predicting the amount of sales. The dataset has been taken from a Kaggle competition, but we made some changes compared to the original data. Please make sure that you're using **our** custom dataset (available on JULIE). 🤓"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGB-Vy4zuT8Z"
      },
      "source": [
        "## Deliverable 📬\n",
        "\n",
        "To complete this project, your team should: \n",
        "\n",
        "- Create some visualizations\n",
        "- Train at least one **linear regression model** on the dataset, that predicts the amount of weekly sales as a function of the other variables\n",
        "- Assess the performances of the model by using a metric that is relevant for regression problems\n",
        "- Interpret the coefficients of the model to identify what features are important for the prediction\n",
        "- Train at least one model with **regularization (Lasso or Ridge)** to reduce overfitting\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHlZMwQN8KOZ"
      },
      "source": [
        "# **Part 1 : Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hq1rMPUyeI6Q"
      },
      "source": [
        "Setting up environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9iqoaJDZ-Ku",
        "outputId": "454e6f86-0531-41c2-f3aa-08a69a828c88"
      },
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhzVdFPDaZ-J"
      },
      "source": [
        "os.chdir('/content/drive/MyDrive/08_28_walmart')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vhG5HCTpbw0t",
        "outputId": "7a241c75-a599-4776-f4c3-974325ad3472"
      },
      "source": [
        "# !pip install pandas-profiling==2.7.1\n",
        "!pip install matplotlib==3.4\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: matplotlib==3.4 in /usr/local/lib/python3.7/dist-packages (3.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.4) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.4) (0.10.0)\n",
            "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.4) (1.19.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.4) (1.3.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.4) (7.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.4) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib==3.4) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoF9LnMEucjQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83f4a359-19e4-4335-81a5-af271aba397d"
      },
      "source": [
        "!pip install missingpy\n",
        "!pip install xgboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: missingpy in /usr/local/lib/python3.7/dist-packages (0.2.0)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.7/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.4.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hR1azc7OaX8E"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") # to avoid deprecation warnings\n",
        "import datetime \n",
        "from datetime import datetime , date\n",
        "\n",
        "\n",
        "\n",
        "#Graph libraries\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import PercentFormatter\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "\n",
        "#Preprocessing libraries\n",
        "from missingpy import KNNImputer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import  OneHotEncoder, StandardScaler,LabelEncoder\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import  OneHotEncoder, StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "#Model Selection\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "from fbprophet import Prophet\n",
        "\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "#for classification only !\n",
        "# from sklearn.metrics import accuracy_score\n",
        "# from sklearn.metrics import classification_report\n",
        "# from sklearn.metrics import f1_score\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvaK7MKdX1r6"
      },
      "source": [
        "#Loading dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/08_28_walmart/Walmart_Store_sales.csv')\n",
        "\n",
        "# renaming columns\n",
        "new_cols = [c.lower() for c in df.columns]\n",
        "df = df.rename(columns=dict(zip(df.columns,new_cols)))\n",
        "\n",
        "# Converting Dates column \n",
        "df.date = pd.to_datetime(df['date'])\n",
        "df['year'] = df.date.dt.year"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fy2PRw8FVTOD"
      },
      "source": [
        "Weekly_Sales behavior"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "XLKqRG-gSmpM",
        "outputId": "de787ec7-5857-402b-d8f7-d2cc902b4c35"
      },
      "source": [
        "sns.relplot(\n",
        "        x=df['date'],\n",
        "        y=df['weekly_sales'],\n",
        "        hue= df['year'],\n",
        "        data = df,\n",
        "        kind='line',\n",
        "        height=5,\n",
        "        aspect=3,\n",
        "       \n",
        "        \n",
        "    )\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1150.88x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtItMQo5E3aP"
      },
      "source": [
        "# Temporal coverage of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tot762GrYaLl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fdf73d3-863f-465b-a943-553fd772d781"
      },
      "source": [
        "# Determination of the temporal coverage of the dataset : \n",
        "\n",
        "coverage = round(100- 100*df[df.date.isna()]['weekly_sales'].sum() / df['weekly_sales'].sum())\n",
        "\n",
        "print('We will therefore only be able to analyze {}% of the turnover'.format(coverage))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We will therefore only be able to analyze 87% of the turnover\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSoZ0sM0CeQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "28fa00a9-5e76-401b-de84-5ff8dda08fa0"
      },
      "source": [
        "\n",
        "df['year'] = df.year.fillna('unknown year').astype('str')\n",
        "df['year'] = [str(y).replace('.0','') for y in df.year]\n",
        "ca_per_year = df.groupby(['year'])[['weekly_sales']].sum()\n",
        "\n",
        "ca_per_year.rename({'weekly_sales' : 'annual_turn_over'}, axis=1, inplace=True)\n",
        "ca_per_year['(%)'] = [str(round(100 * x / ca_per_year.annual_turn_over.sum())) for x in ca_per_year.annual_turn_over]\n",
        "ca_per_year = ca_per_year.reset_index().astype('str')\n",
        "ca_per_year['(%)'] = ca_per_year.year + ' (' + ca_per_year['(%)'] + ' %)'\n",
        "\n",
        "#PLotting pie chart\n",
        "ax = plt.pie(ca_per_year.annual_turn_over,labels=ca_per_year['(%)']  )\n",
        "centre_circle = plt.Circle((0,0),0.65,fc='white')\n",
        "\n",
        "fig = plt.gcf()\n",
        "fig.gca().add_artist(centre_circle)\n",
        "plt.title('Turn-over repartition per year \\nPossible huge impact of missing values among years')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIeDXSL1FDux",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af524478-8e32-4419-bc15-97023a754f63"
      },
      "source": [
        "total_turn_over = round(df['weekly_sales'].sum() / 1000000)\n",
        "\n",
        "turn_over_year_unknown = round(df[df.year =='unknown year']['weekly_sales'].sum() / 1000000)\n",
        "known_turn_over = round((total_turn_over - turn_over_year_unknown) / 1000000)\n",
        "\n",
        "ratio_unknown = round(100* turn_over_year_unknown / total_turn_over,2)\n",
        "print('******************* Disclaimer *******************')\n",
        "print()\n",
        "print('The cumulated turnover is {} millions US$ over 3 years. \\nIt is not possible to analyze {} millions in turnover because we do not know which year to allocate, ie {}% of the total'.format(total_turn_over,turn_over_year_unknown,ratio_unknown))\n",
        "print()\n",
        "print('*************************************************')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "******************* Disclaimer *******************\n",
            "\n",
            "The cumulated turnover is 170 millions US$ over 3 years. \n",
            "It is not possible to analyze 22 millions in turnover because we do not know which year to allocate, ie 12.94% of the total\n",
            "\n",
            "*************************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6t3-vcM3KLlQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8310b9d-00f0-4df4-c4fb-faa03b76f0cc"
      },
      "source": [
        "\n",
        "# dropping lines with empty dates\n",
        "df.dropna(subset = ['date'], inplace=True)\n",
        "\n",
        "\n",
        "#Changing labels for holiday_flag:\n",
        "df['holiday_flag'] = df.holiday_flag.map({1.0 :'bank_holidays', 0.0 : 'regular'}).fillna('unknown')\n",
        "\n",
        "# Converting Dates column \n",
        "from datetime import datetime , date \n",
        "df.date = pd.to_datetime(df['date'])\n",
        "df['year'] = df.date.dt.year\n",
        "df['month'] = df.date.dt.month\n",
        "df['month_label'] = df.date.apply(lambda x: x.strftime(\"%B\")) \n",
        "df['day_of_week'] = df.date.apply(lambda x: x.strftime(\"%A\"))\n",
        "df['week_of_year'] = (df.date.dt.isocalendar().week)*1.0\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(132, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1f-DB9EeXsK"
      },
      "source": [
        "# df['date2'] = df.date.apply(lambda x: int(x.strftime(\"%s\")))\n",
        "# df[['date2']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Hba7ZVPkemA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "cd46cc86-3ef6-44cf-b4ba-4f2862cc97bc"
      },
      "source": [
        "df.sample(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>store</th>\n",
              "      <th>date</th>\n",
              "      <th>weekly_sales</th>\n",
              "      <th>holiday_flag</th>\n",
              "      <th>temperature</th>\n",
              "      <th>fuel_price</th>\n",
              "      <th>cpi</th>\n",
              "      <th>unemployment</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>month_label</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>week_of_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>18.0</td>\n",
              "      <td>2012-12-10</td>\n",
              "      <td>1074079.00</td>\n",
              "      <td>regular</td>\n",
              "      <td>50.97</td>\n",
              "      <td>4.029</td>\n",
              "      <td>138.911700</td>\n",
              "      <td>8.243</td>\n",
              "      <td>2012</td>\n",
              "      <td>12</td>\n",
              "      <td>December</td>\n",
              "      <td>Monday</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2010-09-24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>regular</td>\n",
              "      <td>80.94</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211.597225</td>\n",
              "      <td>7.787</td>\n",
              "      <td>2010</td>\n",
              "      <td>9</td>\n",
              "      <td>September</td>\n",
              "      <td>Friday</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>4.0</td>\n",
              "      <td>2011-07-22</td>\n",
              "      <td>2036231.39</td>\n",
              "      <td>regular</td>\n",
              "      <td>82.84</td>\n",
              "      <td>3.627</td>\n",
              "      <td>129.150774</td>\n",
              "      <td>5.644</td>\n",
              "      <td>2011</td>\n",
              "      <td>7</td>\n",
              "      <td>July</td>\n",
              "      <td>Friday</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     store       date  weekly_sales  ... month_label  day_of_week  week_of_year\n",
              "22    18.0 2012-12-10    1074079.00  ...    December       Monday          50.0\n",
              "133    1.0 2010-09-24           NaN  ...   September       Friday          38.0\n",
              "132    4.0 2011-07-22    2036231.39  ...        July       Friday          29.0\n",
              "\n",
              "[3 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgNqRf_6cgy5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "94bda25e-7da6-4625-da3d-2b82758b6939"
      },
      "source": [
        "ca_per_year = df.groupby(['year'])[['weekly_sales']].sum()\n",
        "\n",
        "ca_per_year.rename({'weekly_sales' : 'annual_turn_over'}, axis=1, inplace=True)\n",
        "ca_per_year['(%)'] = [str(round(100 * x / ca_per_year.annual_turn_over.sum())) for x in ca_per_year.annual_turn_over]\n",
        "ca_per_year = ca_per_year.reset_index().astype('str')\n",
        "ca_per_year['(%)'] = ca_per_year.year + ' (' + ca_per_year['(%)'] + ' %)'\n",
        "\n",
        "#Plotting pie chart\n",
        "ax = plt.pie(ca_per_year.annual_turn_over,labels=ca_per_year['(%)']  )\n",
        "centre_circle = plt.Circle((0,0),0.65,fc='white')\n",
        "\n",
        "fig = plt.gcf()\n",
        "fig.gca().add_artist(centre_circle)\n",
        "plt.title('Turn-over repartition per year \\nPossible huge impact of missing values among years')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dP1l6AO6Vrue"
      },
      "source": [
        "# Pareto chart of monthly sales over years"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDYoWaM9V5Qw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4fa0a0db-61a8-475b-ad28-e5675032a1f1"
      },
      "source": [
        "#custom function to build pareto graphes\n",
        "def pareto_graph(year):\n",
        "  df_year =  df[df.year == year].sort_values(by=['month']).groupby(['month'])['weekly_sales'].sum().to_frame()\n",
        "  df_year.weekly_sales =[round(x / 1000) for x in df_year.weekly_sales]\n",
        "  df_year[\"cum_percentage\"] = round(df_year[\"weekly_sales\"].cumsum()/df_year[\"weekly_sales\"].sum()*100,2)\n",
        "  \n",
        "  fig, ax = plt.subplots(figsize=(22,5))\n",
        "\n",
        "  # Plot bars (i.e. frequencies)\n",
        "  ax.bar(df_year.index, df_year[\"weekly_sales\"])\n",
        "  ax.set_title(\"Pareto Chart for monthly sales in K US $ for year {}\".format(year))\n",
        "  ax.set_xlabel(\"cumulative sales per month\")\n",
        "  ax.set_ylabel(\"turn_over in K US $\");\n",
        "\n",
        "  # Second y axis (i.e. cumulative percentage)\n",
        "  ax2 = ax.twinx()\n",
        "  ax2.plot(df_year.index, df_year[\"cum_percentage\"], color=\"red\", marker=\"D\", ms=7)\n",
        "  ax2.axhline(80, color=\"orange\", linestyle=\"dashed\")\n",
        "  ax2.yaxis.set_major_formatter(PercentFormatter())\n",
        "  ax2.set_ylabel(\"Cumulative Percentage\")\n",
        "\n",
        "  for c in ax.containers:\n",
        "    ax.bar_label(c, label_type='center', color=\"white\")\n",
        "  plt.show()\n",
        "\n",
        "for year in list(set(df.year)):\n",
        "  pareto_graph(year) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1584x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1584x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vUqVKrMsQERERERERERHZK6Snp7u7V9iBjxU26KxSpQqbN2+OdRkiIiIiIiIiIiJ7BTPbEusaSlOFTXBFRERERERERESk8lDQKSIiIiIiIiIiIuWegk4REREREREREREp9xR0ioiIiIiIiIiISLmnoFNERERERERERETKPQWdIiIiIiIiIiJSdkaOhObNg0eRElSqQaeZHWBmkyK+NpjZDWZW28xGmNns8LFWeL2Z2ZNmNsfMppjZoRH3ujS8fraZXVqadYuIiIiIiIiISCkYORJ69oQFC4JHhZ1Sgko16HT3me7ewd07AIcB6cCHQH/ga3dvDXwdPgfoDrQOv/oBzwGYWW1gAHAk0BEYkBeOioiIiIiIiIhIOZAXcqanB8/T0xV27mXM7BUzW2Fmv0W0FWvAYjjwcULY1ilsSzCzr8wstTTrL8up692Aue6+ADgDeD1sfx04Mzw+AxjkgbFATTNrCJwCjHD3Ne6+FhgBnFqGtYuIiIiIiIiIyO7KH3LmUdi5t3mNnTO3Yg1YBK4Ergd6ALeEbVcBb7p7vg9AySrLoPN84O3wuIG7Lw2PlwENwuPGwMKIPovCtsLaRURERERERERkb1ZYyJlHYedew92/A9bkay7ugMUsIDX8yjKzmkAvYFDpVg8Jpf0CAGaWBJwO3JH/nLu7mXkJvU4/ggSZpKSkkriliIiIiIiIiIgUR24uzJ8P06bB1Klw332Fh5x50tOhT5+gn5SmBDP7OeL5QHcfWESf4g5YfIYg1EwmGN35f8AD7p67p8UXpUyCToKhrBPdfXn4fLmZNXT3pWHSuyJsXww0jejXJGxbDJyQr31U/hcJfzADAdLS0kokPBWRvUPz/p/GuoRyZf5Dp8W6BBERERERqehyc+H337cHmnmP06fDli3br6tTJ3ieu4ucKzUVXn219GuWbHc/fHc7RzNg0d3/IMzxzGw/ghxvupm9ASQB/+fus3a3hl0pq6DzArZPWwf4BLgUeCh8/Dii/RozG0yw8dD6MAz9AnggYgOikylgdKiIiIiIiIiIiJSwnJyCA80ZM3YMNBs3hnbt4Morg8e2bYOvmjV3PX09NRWGDYMuXcrsLUmxFHfAYqT7gX8C1wEvAfOBB4ALS6PQUg86zSwNOIlgqGqeh4B3zawvsAA4N2wfTrBQ6RyCHdr7ALj7GjO7D/gpvO5ed8+/XoCIiIiIiIiIiOyunByYN6/gQDMjY/t1TZoEQeYJJ+wYaNaoUfi9u3QJwsz8YadCzvKgWAMW8zqZ2fHAEnefHe62nht+ldrO6+ZeMWd4p6Wl+ebNm2NdhoiUEE1dLx5NXRcRERERkULl5MDcuQUHmlu3br+uadPtQWZkoFm9+u6/duTIToWcZc7M0t09bRfn3yaYdl4XWA4MAD4C3gX2JRywGA5KNOBpgl3a04E+7v5zeB8DvgTOC689EHiLYNDlVe7+Q6m8PwWdIlIeKOgsHgWdIiIiIiJCdnYQaOaFmXmB5syZOwaazZrtGGa2awdt2uxZoLkrI0cGGw+9+qpCzjJWVNBZ3pXVGp0iIiIiIiIiIlIasrJ2DDTzHmfOhMzM7dc1bx4EmaecsmOgWa1a2dbbpYt2V5dSoaBTRERERERERKQ8yMqCOXMKDjSzsrZf16JFEGR2775joFm1auxqFykDCjpFRERERERERPYmmZkFB5qzZm0PNM22B5qnnbZjoJlWYWcmi+ySgk4RERERERERkVjIzAzCy8j1M/MCzezs4BozaNkyCDF79dq+jmabNsFmPiKyjYJOEREREREREZHStHXr9kAzcpTm7NnBDugQBJqtWgVB5hln7BhoVqkS2/pFygkFnSIiIiIiIiJSOZX07t9btwbrZeYPNOfM2R5oxsVtDzTPPnt7oHnAAQo0RfaQgk4RERERERERqXxGjoSePSE9PXgcNiz6sDMjo/BAMzc3uCYuDvbbLwgye/feMdBMSSm99yVSiSnoFBEREREREZHKJTLkhMLDzi1bCg40587dHmjGx0Pr1nDQQXDeeUGYmRdoJieX/XsTqcQUdIqIiIiIiIjEUklPn5Zdyx9y5klPh1NPDUZfbtoUhJrz5u0YaO6/P7RvDxdcsH2X89atFWiK7CUUdIqIiIiIiIjEyp5Mn66ssrODkZZ5X+np0T+fNQs++mj7jub5ZWbC//4HzZrBEUfAhRfuGGgmJZXpWxWR4lHQKSIiIiIiIhIL0U6f3tvl5gZrVu5u+FjU8/znsrJ2r864OHAPvqLx3nu79zoiEjMKOkVERERERETK2q6mT+9p2OkehIHFCQ/35HlGxu5/H6pU2fErNXX7cf36Oz7Pf764zxMTYdSogr/vkVJTg2UERKTcUdApIiIiIiIiUpYKCznzpKfDKafARRdBw4a7Fz7mrStZXImJO4aDkcc1a0KjRiUXPKakgNlufxt3S5cuQYhc2Pc/NbX8jagVkW0UdIqIiIiIiIiUpT59dj2iEIIRma++Gky3LiwsTE2FOnVKdtRjfHzZfA9iqbCwUyGnSLmnoFNERERERESkrCxeHARpr7++67Uiq1QJNs056aSyH/VYGeQPOxVyilQIcbEuQERERERERKRCy82FL7+Es88OdvN+7TU47DBITi74+tRU+PRTOPlkhZylKS/sbNZMIadIBaGgU0RERERERKQ0rFwJ//kPtG4drLk5ejTcfDPMmQM//QSffRaEmpE0srBsdekC8+fr+y1SQSjoFBERERERESkp7kGgeeGF0KQJ3H578Pi//8GiRfDvf0OrVsG1eSMK88JOhZwiIntEa3SKiIiIiIiI7Kl16+CNN+D552HaNKhRA668Mvhq167wfnlhZ58+weZDCjlFRHabgk4RERERERGR3fXzz0G4+fbbwaY2RxwBL78M550HaWnR3SNv+rSIiOwRBZ0iIiIiIiIixbF5cxBsPv88TJgQTDm/8MJg9OZhh8W6OhGRSktBp4iIiIiIiEg0fvstCDffeAM2bICDDoKnn4aLLgqmqouISEwp6BQREREREREpTEYGvP8+PPcc/PADJCXBuefC3/4GRx8NZrGuUEREQgo6RURERERERPKbPRsGDgw2CFq9GvbbDx5+GC67DOrWjXV1IiJSAAWdIiIiIiIiIgBZWTB0aDB686uvID4ezjwzGL3ZtSvExcW6QhER2QUFnSIiIiIiIlK5LVwIL74IL70ES5dC06Zw333w179Co0axrk5ERKKkoFNEREREREQqn5wc+PLLYPTmp5+CO3TvDi+8AD16BKM5RUSkXCn1cfdmVtPMhpjZDDObbmadzKy2mY0ws9nhY63wWjOzJ81sjplNMbNDI+5zaXj9bDO7tLTrFhERERERkQpo+XJ48MFgzc0ePWDcOOjfH+bNCwLPXr0UcoqIlFNlscDIE8Dn7t4GOASYDvQHvnb31sDX4XOA7kDr8Ksf8ByAmdUGBgBHAh2BAXnhqIiIiIiIiMguucPIkXDeedCkCdx5J7RoAe++G0xbv/9+aN481lWKiMgeKtWp62ZWAzgOuAzA3TOBTDM7AzghvOx1YBRwO3AGMMjdHRgbjgZtGF47wt3XhPcdAZwKvF2a9YuIiIiIiEg5tmYNvP56MB195kyoVQuuvRb69YM2bWJdnYiIlLDSXqOzBbASeNXMDgEmANcDDdx9aXjNMqBBeNwYWBjRf1HYVli7iIiIiIiIyHbuwXT055+Hd96BjAzo1CkIPP/8Z6hSJdYViohIKSntoDMBOBS41t3HmdkTbJ+mDoC7u5l5SbyYmfUjmPJOUlJSSdxSREREREREyoONG+Gtt4KAc/JkqFoVLrsM/vY3OOSQWFcnIiJloLTX6FwELHL3ceHzIQTB5/JwSjrh44rw/GKgaUT/JmFbYe07cPeB7n64ux+ekKAN5UVERERERCq8yZPhqqugUaPgEYKwc8mSYEd1hZwiIpVGqQad7r4MWGhmB4RN3YBpwCdA3s7plwIfh8efAJeEu68fBawPp7h/AZxsZrXCTYhODttERERERESkstmyJZiK3qkTdOgAr70G55wDY8fCL7/AlVdCtWqxrlJERMpYWQx7vBZ4y8ySgHlAH4KA9V0z6wssAM4Nrx0O9ADmAOnhtbj7GjO7D/gpvO7evI2JREREREREpJKYOTMYrfn667B2LRxwADz2GFxyCdSuHevqREQkxko96HT3ScDhBZzqVsC1DlxdyH1eAV4p0eJEdkPz/p/GuoRyY/5Dp8W6BBEREREp7zIz4aOPgoBz5EhITISzzw7W3jz+eDCLdYUiIrKX0EKWIiIiIiIisveZPx8GDoSXX4YVK6B5c3jwQejTBxo0iHV1IiKyF1LQKSIiIiIiInuHnBwYPjwYvfnZZ8FozZ49g9GbJ58M8fGxrlBERPZiCjpFREREREQktpYsCUZuvvgiLFwIDRvC//0fXH45NG0a6+pERKScUNApIiIiIiIiZS83F77+Ohi9+fHHwWjOk06Cxx+HXr2CtThFRESKQUGniIiIiIiIlJ1Vq+C11+CFF2DOHKhTB266Cfr1g/32i3V1IiJSjinoFBERERERkdLlDj/8EIzefO+9YCf1zp3hnnuCHdRTUmJdoYiIVAAKOkVERERERKR0rF8Pb74ZBJy//QbVqwcjN6+8Eg46KNbViYhIBaOgU0RERERERErWhAlBuPm//0F6Ohx2GLz0Epx/PqSlxbo6ERGpoBR0ioiIiIiIyJ7bvBneeScIOH/6CVJT4YIL4G9/g8MPj3V1IiJSCSjoFBERERERkd03dWqwsdCgQcFU9Xbt4Kmn4KKLoGbNWFcnIiKViIJOERERERERKZ6tW+H994PRm6NHQ1IS/PnPwejNY44Bs1hXKCIilVBcrAsQERERERGRvcDIkdC8efBYmLlz4fbboUkTuPBCWLIE/vMfWLQo2HSoc2eFnCIi5ZiZ3WhmU83sNzN728xSzKyFmY0zszlm9o6ZJYXXXhteNzyirbOZPRar+hV0ioiIiIiIVHYjR0LPnrBgQfAYGXZmZ8OHH8Ipp8B++8F//wvHHQdffgmzZsGtt0K9erGrXURESoSZNQauAw5394OAeOB84N/AY+6+H7AW6Bt2uRBoD/wInGJmBvwfcF9Z156nwk5db1YrA746YcfGfc+F/f8O2ekwqsfOnVpeFnxlrILve+98vvVV0Ow82LwQxly88/k2N0OTXrBhJoy/cufzB/0T9jkR1k6CCTfsfP6QB6De0bDyR5h8587nD3scanWAZV/Bb//a+XzHF6D6AbBoKMz4787nO70BaU1hwTsw+7mdz3ceAil1Yd5rwVd+JwyHhFSY9Sz88e7O508cFTxOfwQWD9vxXHwV6PJZcPzrfbD86x3PJ9eBY98PjifdAavG7Hg+tQkc/WZwPOGG4HsYqdr+cOTA4HhcP9g4a8fztToE3z+AHy+C9EU7nq/bCTo8GByPPge2rt7xfINucPD/AfBa8wGkxG3d4fTXGzry4qqzARjcsj/5DVt/LG+uPo0Uy+C1FnfvdH7I2hMZsvZEasWv57lmD+50/s3VPRi2/jgaJq7ksaY7/2xfXHkWX288kpbJi3ig8dM7nX9qxfn8sKkDbVPmcVejgTud/8+yS5mYfiCHpk7ntn1e3+n8vUv6MS2jJcdUncS19QfvdP7Oxdcwb2sTulUbxxX1Ptx+4quHg8eS+OwBF9X5lJ41Ru90+vx5DwFwRd0P6FZ9/A7nMnKTuWz+PQBcW/9tjqk6eYfza3Oqc9WC4M/bbfu8xqGpM3Y4vzSrLjcuvAWAuxoOpG2VeTucn7e1MXcuvhaABxo/RcvkxTucn7alJfcu7QfAY00foWHiqh3OT0xvw3+WXQbAc80eoFb8hh3O/7DpEJ5acQFQzM9e3vdev/f0ew/2+PceI7tDzpYdzzfuCQcGfzZ2+u8t6LOnz15wrM/ezuf12QuO9dnb+XwsP3vr1sE/Z0J6BrQDzkyHUSfC0jaQvhmWLoXnMiG+CTzyF2jzOySvBu6Hb+4P7qHPXnCsz97O5/V7LzjWZ2/n8/rsxe6zt2sJQBUzywJSgaVAV+Av4fnXgbuB5wADEsPrsoCLgM/cfU1xXrAkVdigU0RERGR3jJ23mnu/H820jMXhP/Cs3umaO0ePYt7WOXSr9hNX1Nv5/I3ffs3SrHr0rDGRi+rsfP6qkV+yNqcGvWtNpnetnc9f9vVnZHgKF9X5jZ41dj5/fv9PAbii7nS6Vd/xfEZuMpd9EZy/tv5Mjqm64/m1OVlc9Wlw/rZ95nBo6o7nl2YZN34SnL+r4e+0rbLj+Xlb/+DOD4PzDzT+g78cuFN5IlKerFsHv/4KGbk7tufkwrRpwXGd2vDsfdCzHyz7rOC/8IuISHmRYGY/Rzwf6O4DAdx9sZk9AvwBbAG+BCYA69w9O7x+EdA4PH4aGAtMBX4APgZOKf23UDhz91i+fqlJS0vzzZs3x7oMqYCah3+5lKLNf+i0EruXvu/FU5Lfe5HKRr9viqdC/74ZORL69IFXX4UuXWJdjVRUOTmQmRl8ZWXtfFxQW1Hno+2zZAmMGwe5uYXXl5ICw4frz4CISAVhZununlbIuVrA+8B5wDrgPWAIcHc4bR0za0owavOgfH3vAqYAucAlwELgZnffxX9kSp5GdIqIiIiI5Je3XmF6evA4bJiCnr1dbm4Q4O1JQFhSAWJxzpfmwJP4+GA39KQkSEzc8TEpKVhfc1chJ0BGRhD4z59fenWKiMje4kTgd3dfCWBmHwDHADXNLCEc1dkE2GHNNjNrBHR093vN7FuCqe7/BLoBI8ryDSjoFBERERGJFBlyQuULO923jzLcW8LAaPpkZxf93naX2c4hYUHBYd5xWtrObUX1KahtT/okJkJcEXvP5v+sFyQ1NRjVLCIilcEfwFFmlkowdb0b8DMwEugNDAYuJZiiHuk+4K7wuArgBCM7U8ug5h0o6BQRERERyVNY8LO7YWfeKMO9JQyMtk9pSkiIPtBLSoKqVUsuBNzd+8THB2FnRdOlS/CZLizsTE2tPAG/iIjg7uPMbAgwEcgGfgEGAp8Cg83sX2Hby3l9zOxPYd+JYdP/gF8Jpq7/p+yqDyjoFBEREZHKITsbNm6ETZuCx7yvvOcTJsCzzwaBX0HS0+HEE+HAA4MAKJqAMSen9N5PXFzxArtq1cp2RGFhowwrYmBYnhUWdirkFBGplNx9ADAgX/M8oGMh1/8C9I14/jjweCmVVyQFnSIiIiJ7uU4LpvDw8Me4tceNjGnWPtbllJ2srJ3DyGieF3YuI2PPa8rNhblzg/CnLILBokYZipSE/GGnQk4RESmnFHSKiIiI7MU6LZjCy0PuITV7Ky8PuYe+vQfsvWFnZuaeh5GRz7duje514+OD0YpVqwaPeV916+74PP/5/M8nTYKLL4YtWwp/LQVAUlHlhZ19+gRrcuozLiIi5ZCCThEREZG9VGTICZRs2OlOck4WqZlbSMvcQtXMLaRmZlA1M33786yM8DhoS8vMCB+DttTMDBh89fZwsrAp3/klJBQcNNavX7xgMu95SkrJTIdu1Qpq19Z6hVJ5demi3dVFRKRcU9ApIiIishfKH3LmSc3eyitD7ubuE/sxt07TiPAxPSKs3LKtLS0rg7StW7a1pWZtP07MjW79yMy4BDYnVWFzUkr4WIVNSamsqFqbNke1Ln4wmZy8967TqPUKRURERMotBZ0iIiIie5nCQs48VbIz+ffnTxfaf2t8AumJeYFk8LgxOZVl1ers0Bb5tSmpCumJKWxKSmVzctiemMLmpFQyExILfa35D522x+93r6P1CkVERETKJQWdIiIiInuZR4f9t9CQM9LK1BpcefY/g5AyIsDMii88mJQoab1CERERkTJnZqnAzcC+7n6FmbUGDnD3YdH0V9ApIiIispfYf+V8+o3/kLqb1+LAriZ3pyckc93ptzOx8YFlVV7lo/UKRURERMraq8AEoFP4fDHwHqCgU0RERGSv506nP36l3/j36TJvAumJybz1px5MbNSGhz5/qsCRnekJyXv37usiIiIiIrunlbufZ2YXALh7uln0i7uXetBpZvOBjUAOkO3uh5tZbeAdoDkwHzjX3deGhT8B9ADSgcvcfWJ4n0uBf4a3/Ze7v17atYuIiIiUlvjcHLrP/IF+4z+g/bI5rEqtwX87X8gbh57GuirVAVhZtfZOa3Uq5BQRERGRCizTzKoADmBmrYCi13QKldWIzi7uvirieX/ga3d/yMz6h89vB7oDrcOvI4HngCPDYHQAcDjBG51gZp+4+9oyql9ERESkRFTJzODcX0fQ96eP2Hf9cubVasSdp1zN++26sjUxeYdrxzRrT9/eA7aFnQo5RURERKSCGwB8DjQ1s7eAY4DLou0cq6nrZwAnhMevA6MIgs4zgEHu7sBYM6tpZg3Da0e4+xoAMxsBnAq8XbZli4iIiOyeOpvXccnEYVwy8VNqZWxkQqM23N+1LyP2O5LcuPhC++WFnQ8Pf4xbe9yokFNEREREKix3H2FmE4GjCJasvz7f4MldKoug04EvzcyBF9x9INDA3ZeG55cBDcLjxsDCiL6LwrbC2kVERET2as3XLOaKnz7knN++ISU7ky9bH8ULHc9mQpO2Ud9jTLP2dL7q1VKsUkREREQk9szs0PAwLzfc18xqAAvcPbuo/mURdHZ298VmVh8YYWYzIk+6u4ch6B4zs35AP4CkpKSSuKWIiIjIbjl08XT6jf+Ak2eNJSs+ng/adeWljmcxt07TWJcmIiIiIrK3ehY4FJhCMKLzIGAqUMPMrnL3L3fVudSDTndfHD6uMLMPgY7AcjNr6O5Lw6npK8LLFwOR//ffJGxbzPap7nntowp4rYHAQIC0tLQSCU9FREREomWey4lzxtNv3AccsXga61Kq8mynP/P6ob1YWbVWrMsTEREREdnbLQH6uvtUADNrC9wL3AZ8AMQu6DSzNCDO3TeGxyeHxX0CXAo8FD5+HHb5BLjGzAYTbEa0PgxDvwAeMLO8vyGcDNxRmrWLiIiIRCs5O5OzfvuGK376iFZrFrGoen3u6XYF77Q/mfSkKrEuT0RERESkvNg/L+QEcPdpZtbG3eeZWZGdix10mtm+QKq7zyjy4mDtzQ/DQhKA/7n752b2E/CumfUFFgDnhtcPB3oAc4B0oA+Au68xs/uAn8Lr7s3bmEhEREQkVmps2chFvwznsolDqbd5Hb81aMV1vW7l0zadydnFBkMiIiIiIlKgqWb2HDA4fH4eMM3MkoGsojoXGXSa2UMEO6FPM7NzgEeBdWY2zN3/sau+7j4POKSA9tVAtwLaHbi6kHu9ArxSVL0iIiIipa3J+uX0/ekjzp0ygrSsDL5tcSgvdDybH5sdAlH8S7OIiMRO8/6fxrqEcmX+Q6fFugQRqVwuA/4O3BA+/wG4hSDk7FJU52hGdJ7q7v3D4xsJpo3PASYCuww6RURERCqSdsvn0m/cB5w2YzRuxicHHseLHc9mRv0WsS5NRERERKTcc/ctwH/Dr/w2FdV/l0GnmQ0AGpjZXUAVoBXBkFEj2O3oLmCUu39X3MJFREREygV3jvt9Iv3Gf0DnBZPZmFSFVw4/g1cPP52l1evFujoRERERkQrDzFoDDwJtgZS8dndvGU3/XQad7n5PuLtRM6A6wRT2e80sCTjZ3e/d7cpFRERE9mIJOdn0mv4d/cZ/wIEr57Osam0ePOEy3j7kVDakVI11eSIiIiIiFdGrwADgMYKp6n2AuGg7RzN1/a/AJUAmMChs25cgXRURERGpUKpuTef8yZ/z158/odHGVcysuy+39LiBj9seT1Z8YqzLExERERGpyKq4+9dmZu6+ALjbzCYAd0XTucig0903A8/la5tDsE6niIiISMWwZAk88QQ/PvcM1bduZmzTg/jHKVczquVhuEX9j8giIiIiIrL7tppZHDDbzK4BFgNRT6eKZkSniIiISMU1dSo88gi89Rbk5PBd66MZeOTZTGm4f6wrExERERGpbK4HUoHrgPsIpq9fEm1nDU8QERGRyscdRo2C006Dgw6Cd96Bfv1g1iyuObO/Qk4RERERkdho7u6b3H2Ru/dx93MIltCMioJOERERqTyys+Hdd+HII6FLFxg/Hu65B/74A55+Glq1inWFIiIiIiKV2R1RthVol1PXzewKYJS7zzYzA14BzgHmA5e5+8RiFCoiIiISG+np8Oqr8OijMG8e7LcfPPccXHopVKkS6+pERERERCo1M+sO9AAam9mTEaeqA9nR3qeoNTqvB14Ljy8A2gMtgD8BTwDHRvtCIiIiImVu5cpgpOYzz8Dq1XDUUfDww3DGGRAfH+vqREREREQksAT4GTgdmBDRvhG4MdqbFBV0Zrt7VnjcExjk7quBr8zsP8UoVkRERKTszJkD//0vvPYaZGRAr15w221wzDFgFuvqREREREQkgrtPBiab2f8isshiKyrozDWzhsBaoBtwf8Q5zfMSERGRvcvYscGIzQ8/hMREuPhiuPlmOPDAWFcmIiIiIiJF62hmdwPNCHJLA9zdW0bTuaig8y6CYaPxwCfuPhXAzI4H5u1uxSIiIiIlJjcXhg0LAs7vv4eaNaF/f7j2WmjYMNbViYiIiIhI9F4mmKo+AcgpbuddBp3uPszMmgHV3H1txKmfgfOK+2IiIiIiJSYjA958M5iiPmMG7LsvPPYY9O0L1arFujoRERERESm+9e7+2e52LmrX9bMjjgEcWAVMcveNu/uiIiIiIrtt7Vp4/nl44glYvhw6dIC33oI//zmYri4iIiIiIuXVSDN7GPgA2JrX6O4To+lc1NT1XgW01Qbam1lfd/8m6jJFRERE9sSCBfD44/Dii7B5M5x8Mtx6K3Trpg2GREREREQqhiPDx8Mj2hzoGk3noqau9ymoPZzO/m7Ei4uIiIiUjkmTgvU333kneH7BBXDLLXDIITEtS0RERERESpa7d9mT/nG7+aILAM0NExERkdLhDl9+CSedBH/6E3zyCVx3HcybB2+8oZBTRERERKQCMrMGZvaymX0WPm9rZn2j7b9bQaeZHUDEPHkRERGREpGVFWww9Kc/wSmnwG+/wYMPwsKF8OijwYZDIiIiIiJSUb0GfAE0Cp/PAm6ItnNRmxENJZgHH6k20BC4KNoXEREREdmljRuDtTcffzwINQ88EF5+GS68EJKTY12diIiIiIiUjbru/q6Z3QHg7tlmlhNt56I2I3ok33MHVgOz3T2zeHWKiIiI5LN0KTz5JDz3HKxfD8cdB88+Cz16QNxuTTwREREREZHya7OZ1SEceGlmRwHro+1c1GZE3+5ZbSIiIiIFmD4dHnkkmKaenQ1nnx3soN6xY6wrExERERGR2LkJ+ARoZWY/APWA3tF2LmpEp4iIiEjJcIfRo4Md1IcNgypV4PLL4aaboFWrWFcnIiIiIiIx5u4Tzex44ADAgJnunhVtf80JExERkdKVkwNDhsBRR8Hxx8OYMTBgACxYAM88o5BTREREREQAMLOrgaruPtXdfwOqmtnfo+1f1GZEJ7n7iELO/dvdby9euVJSmvf/NNYllCvzHzot1iWIiFQ+6enw2mvBbulz5waB5jPPwGWXQWpqrKsTEREREZG9zxXu/kzeE3dfa2ZXAM9G07moEZ3PmNkOCZGZxZnZa8Ahxa1UREREKoGVK+Huu6FZM7j6aqhTB957D2bOhL//XSGniIiIiIgUJt7MLO+JmcUDSdF2LiroPAX4r5mdFd48hWBB0ESgV/FrFRERkXJr5Eho3jx4LMicOUGQ2awZ3HNPMFX9229h7Fjo3Rvi48u0XBERERERKXe+AN4xs25m1g14G/g82s5F7br+u5mdCHxhZg2Ai4Cf3P3GPalYREREypmRI6Fnz2A6es+ewWZCXboE58aPDzYYev99SEyEiy6Cm2+Gtm1jW7OIiIiIiJQ3twJXAleFz0cAL0Xbuag1Og8ND28HXg9v/kZeu7tPjOZFwmGmPwOL3b2nmbUABgN1gAnAxe6eaWbJwCDgMGA1cJ67zw/vcQfQF8gBrnP3L6J9kyIiIrIHIkNO2B52/uMf8MUX8N13UKMG3H47XHcdNGwY23pFRERERKTcCfPDqe7eBnh+d+6xy6AT+G/E8RSgQUSbA12jfJ3rgelA9fD5v4HH3H2wmT1PEGA+Fz6udff9zOz88LrzzKwtcD7QDmgEfGVm+7t7TpSvLyIiu0mbn0WvQm58lj/kzJOeHgSd9eoFmw1dfjlUqxabGkVEREREpNxz9xwzm2lm+7r7H7tzj6KmrnfZvdK2M7MmwGnA/cBN4YKiXYG/hJe8DtxNEHSeER4DDAGeDq8/Axjs7luB381sDtARGLOn9YmISPnTacEUHh7+GLf2uJExzdrHupzyJTc3CCk3b4ZNm3Z8zH/866/w5puQnV34/TZvhg4dFHKKiIiIiFQQZlaTYLr4QQQDHf8KzATeAZoD84Fzwx3RzwHuBdYAZ7r7ajNrBTzg7uftxsvXAqaa2Xhgc16ju58eTeeiRnSWhMeB24C8vwHVAda5e97fmhYBjcPjxsBCAHfPNrP14fWNgbER94zsIyIilUinBVN4ecg9pGZv5eUh99C394CKGXZmZhYdRO7O+fwjM/dUejr06QPz55fsfUVEREREJFaeAD53995mlgSkAncCX7v7Q2bWH+hPsNTltcARwNkEgxqfAv4F/HM3X/v/9qTwUg06zawnsMLdJ5jZCaX5WuHr9QP6ASQlRb3zvIiIlBORIScQ87DTPJcqWVtJy8ygSlYGTJmy50Fk3vGuRlHuVIhB1aqQlrb9MS0tWDezUaMd2yPPF3Qc2fbTT3DGGbsOR1NT4dVX9/ybKSIiIiIiMWdmNYDjgMsA3D0TyDSzM4ATwsteB0YRBJ25QDJBGJplZscCy9x99u68vrt/a2bNgNbu/pWZpQLx0fYv7RGdxwCnm1kPIIVgjc4ngJpmlhCO6mwCLA6vXww0BRaZWQJQg2BTorz2PJF9tnH3gcBAgLS0NC+VdyQiIjGRP+TMU2TY6U5STjZVsjJIzcogNTN4TMvaQpWsreHzLaRmbd3hfJWsDNLC49SsLaRmbg3awnNB+461BP8F2oXk5IJDxUaNig4dd9WWkhKEnSXtxBOD3dULWqMTgpAzcvd1ERERERHZ2yWY2c8RzweGeVqeFsBK4FUzO4RgE/HrgQbuvjS8ZhnBPj4ADwJfAUuAi4D3CPbZ2S1mdgXBIMbaQCuCGd3PA92i6R910GlmjYFmkX3c/btd9XH3O4A7wv4nALe4+4Vm9h7Qm2Dn9UuBj8Mun4TPx4Tnv3F3N7NPgP+Z2aMEmxG1BsZHW7uIiJRvhYWceVKzt/LmO/9kVp19yYmPD0PKILyskpVBYm70e9flYmxOSmFLYgrpiSmkJwWPG5NTWV6tdtCW71ze8aN9jik8lExLg4SyWDGmhHXpUnDYqZBTRERERKQ8ynb3w3dxPgE4FLjW3ceZ2RME09S3CbM6D49HACMAzOwSYDiwv5ndAqwFrnf34qyfdTXBvjzjwvvPNrP60XaO6m9cZvZv4DxgGpD3t0UHdhl07sLtwGAz+xfwC/By2P4y8Ea42dAawgTY3aea2bvh62cDV2vHdRGRyuOxYY8UGnLmifdcWqxdzA/NOxQaRKYnBgHm5sjjxBS2JAWP6YkpbE1I2u3RkY+eUwF3XYedw06FnCIiIiIiFdUiYJG7jwufDyEIOpebWUN3X2pmDYEVkZ3CKeaXAacAwwjW7OwNXAi8WIzX3+rumRb+nSyc8R31rO1oh5acCRwQ7nq+W9x9FMH8fdx9HkE6m/+aDODPhfS/n2DndhERqQSarltGr+nf0XPGaPbZtKbI69MTkivuxkR7g7yws0+fYE1OhZwiIiIiIhWOuy8zs4VmdoC7zySYMj4t/LoUeIgdZ2fnuRV40t2zzKwKQTiZS7B2Z3F8a2b/AKqY2UnA34Gh0XaONuicByQCux10ioiIFKXRhhWcNv17es4YzSHLgrWrf2l4APd1vZylVevwyPDHCxzZqZCzjHTpot3VRUREREQqvmuBt8Id1+cBfYA44F0z6wssAM7Nu9jMGgEd3f2esOkp4CdgHcHgyeLoD/QFfgWuJJgK/1K0naMNOtOBSWb2NRFhp7tfF32dIiIiO6u/cTWnzfyentNHc9iSGQBM2Wc/HjihD8PbdGZRjQbbrl2bWmOntToVcoqIiIiIiJQcd58EFLSOZ4EbArn7EuC0iOfvEWxKFLVwHc47gf0IQs6+7r6hOPeA6IPOT8IvERGRPVZ381pOnfkjvWaM5oiFU4nDmV6vOf857hI+bdOZBbUaFdhvTLP29O09YFvYqZBTRERERESkQhhEsMP7U0BP4EmCNT+LJaqg091fL+6NRUREItXcsoFTZ/5Izxmj6fTHr8R7LrPrNOWJYy5g2IHHMrdO06jukxd2Pjz8MW7tcaNCThERERERkfKvobv/Izz+wswm7s5Ndhl0mtm77n6umf1KATscubv+dikiIoWqnrGJk2ePpef00RyzYBKJuTn8Xqshzx71Z4YdeCwz6zbbrR3OxzRrT+erXi2FikVERERERCQWzKwWkPcXxPjI5+5e9A61FD2i8/rwseduVSgiIpVO1a3pnDhnHD2nf8dxv/9CUm42C2s04KUjzmJYm85MbdBqt8JNERERERERqbBqEExdj/zLYt6oTgdaRnOTXQad7r40fFywGwWKiEglUSUzg25zx9Nzxmi6zP2Z5JwsllSry+uH9WRYm2OZ3HB/hZsiIiIiIiJSIHdvXhL3iXYzIhERkR0kZ23lhHkT6DVjNF3njic1aysr0mrxvw6nMqzNsUxs3Aa3uFiXKSIiIiIiIpWEgk4REYlaUnYWx86fSM/pozlpzjiqZm5hdZXqfNCuK8MOPJbxTdqRGxcf6zJFRERERESkEioy6DSzeGCQu19YBvWIiMheJiEnm2MWTKbn9NGcMnsM1bduZl1KVYa1OZZhbY5lTLP25CjcFBERERERkRgrMuh09xwza2ZmSe6eWRZFiYjEUvWUBB46pz0HNKiGA7cNmcyWrBzuP/NgUpPjWbR2CzcMnsSmrdkAtNmnGg+cdTBVUxLIdeeMp3/ADJ698DCa1U4lx52vpy/n35/PjO0bK47sbBg1Ct59l/FvvkPtLRvYkJTKiP2PYmibY/mheQey4hNjXaWIiIiIiIhUMGbWGWjt7q+aWT2gqrv/Hk3faKeuzwN+MLNPgM15je7+aLGrFRHZyw3o1Y5vZ63k729NJDHeqJIYzxt9j+SB4dMZ9/sa/nx4E/od15JHR8wiPs547LwO3PTuJKYv3UjN1ESycnJJSojjxe/mMWbeahLjjbcuP4oT9q/HqFkrY/32CpebC6NHwzvvwPvvw4oVULUq37U4jGFtjuO7FoeSmaBwU0REREREREqHmQ0ADgcOAF4FEoE3gWOi6R/tLhFzgWHh9dUivkREKpRqyQl0bFGbd35aCEBWjrMhI5sW9dIY9/saAL6fvYruB+0DwLGt6zJj2UamL90IwLr0LHIdMrJyGTNv9bZ7TF2ynn1qpMTgHRUhNxd+/BGuvx6aNIETToDXXoPjj4chQ2DFCm7odStftT5SIaeIiIiIiIiUtrOA0wkHWrr7EoqRQUY1otPd7wEws1R3T9+NIkVEyoWmtVNZvTmTR/7cngMbVufXxeu555NpzF6+iZPbNuDLacvpcXBDGtasAkDLumm4O4P+2pHaaUkMnbyEF76bt8M9q6ck0K1NA175IaqR9qXPHX7+ORi5+e67sHAhJCdD9+5w3nnQsydUrRrrKkVERERERKTyyXR3NzMHMLO04nSOakSnmXUys2nAjPD5IWb2bLFLFRHZy8XHGQc1qs6bY//gtCe/Z0tmDled0IrbhkzmoqOaMfSazlRNTiArOze8Po4jmtfm+sG/0Pv5Hzml3T4c3arODvd78oI/8dqPv7NwzZZYva0g3Jw0Ce64A/bbDzp2hCefhPbtYdCgYJr6hx/C+ecr5BQREREREZFYedfMXgBqmtkVwFfAi9F2jnaNzseBU4BPANx9spkdV8xCRUT2esvWZ7BsQwaTFq4DYPivS7nqhP14dMQsLnllPAAt6qbRpU398PotjP99DWvTswAYOXMFBzWuwY9zg2nrD559ML+v2swrP8wv8/cCwNSpwcjNd96BWbMgPh66dYN//APOOgtq1YpNXSIiIiIiIiL5uPsjZnYSsIFgnc673H1EtP2jDTpx94VmFtmUE3WVIiLlxMpNW1myLoOWddOYt2ozx+xXl9nLN1InLYnVmzMxg2u67sdb4xYA8O3slVx5fCtSEuPIynGObFGHl78PpqjffPL+VEtJ4Pb3p5Tpe2ixZjHcd18Qbk6dCnFxwdqbN98chJv16pVpPSIiIiIiIiLRMLObgHeKE25GijboXGhmRwNuZonA9cD03XlBEZG93d2fTOXx8zuQGB/HwjXp3DJkMucc2oSLj2oGwBdTl/Hez4sA2LAlm5dG/84n13TGPRjROXLmCvapnsK1XVszZ8UmPr22MwCvj1mwbZOjktZ03TJ6zhhNz+mjabdiHphB587w9NNwzjmwzz6l8roiIiIiIiIiJaga8KWZrQHeAd5z9+XRdo426Pwb8ATQGFgMfAlcXcxCRUTKhWlLN3D60z/s0PbqD/N5tZDp5x9NWsxHkxbv0LZsQwbN+39aWiUC0GjDCk6b/j09Z4zmkGWzAZjY6ADu7XoFdw0aAI0bl+rri4iIiIiIiJSkcEP0e8ysPXAe8K2ZLXL3E6PpH23Qae5+4e4WKSIiJaP+xtWcNvN7ek4fzWFLZgAwZZ/9eOCEPnza5lgW1wjWDr1LIaeIiIiIiIiUXyuAZcBqoH60naINOn8ws/kEQ0bfd/d1xa1ORER2T53N6+g+8wd6zRjNEQunEoczrX4L/nPcJXzapjMLajWKdYkiIiIiIiIie8zM/g6cC9QD3gOucPdp0faPKuh09/3NrCNwPvAPM5sGDHb3N3ejZhERKULNLRs4deaP9Jwxmk5//Eq85zKrzr483vkvfNqmM3PrNI11iSIiIiIiIiIlrSlwg7tP2p3Oxdl1fTww3sweAB4FXgcUdIqIlJDqGZs4efZYek4fzTELJpGYm8O8Wo145qg/M+zAY5lVr3msSxQREREREREpcWZW3d03AA+Hz2tHnnf3NdHcJ6qg08yqA2cRjOhsBXwIdCxOwSIisrO0remcOGccPWeM5rjfJ5Kck83CGg146YizGHbgsUyt3zLYQV1ERERERESk4vof0BOYADgQ+RdhB1pGc5NoR3ROBj4C7nX3MdHXKCIi+VXJzKDb3PGcNuN7usz7mZTsTJZUq8ugQ3syrM2xTG64v8JNERERERERqTTcvWf42GJP7hNt0NnS3d3MqppZVXfftCcvKiJSEXRaMIWHhz/GrT1uZEyz9ru8NjlrKyfMm0CvGaPpOnc8qVlbWZFWi7cPOYVhbY5lYuM2uMWVUeUiIiIiIiIiex8z+9rduxXVVphog852ZvYGUDu4v60ELnX334pXrohIxdBpwRReHnIPqdlbeXnIPfTtPWCnsDMpO4tj50+k5/TRnDRnHFUzt7AqtQbvH9SNYW2O5acmbcmNi4/ROxARERERERHZO5hZCpAK1DWzWmyful4daBztfaINOgcCN7n7yPDFTwjbjo72hUREKorIkBPYIez8qUlbjlkwmZ7TR3PK7DFU37qZtSnVGNrmWIYdeBxj9z2YHIWbIiIiIiIiIpGuBG4AGhGs05kXdG4Ano72JtEGnWl5ISeAu48ys7SiOoVp7HdAcvhaQ9x9gJm1AAYDdcLiL3b3TDNLBgYBhwGrgfPcfX54rzuAvkAOcJ27fxFl7SIiJSZ/yJknNXsrb7zzT9ITU6iemc6G5DS+bH0Uw9ocy/fNO5AdH+2vWxEREREREZHKxd2fAJ4ws2vd/andvU+0f/OeZ2b/B7wRPr8ImBdFv61AV3ffZGaJwPdm9hlwE/CYuw82s+cJAsznwse17r6fmZ0P/Bs4z8zaEuz43o4g2f3KzPZ395wo6xcR2WOFhZx5EjyX1KwMHj72Yl7seDaZCYllXKGIiIiIiIhI+eXuT5nZQUBbICWifVA0/aMNOv8K3AN8QLCl++iwrajiHMjbuCgx/HKgK/CXsP114G6CoPOM8BhgCPC0mVnYPtjdtwK/m9kcoCOgHeClQvv+9i5s2ppNbq6Tneuc/vQPtG1YnfvPOojkhDiyc53/++g3Ji9aT7/jWnJmh0YAxMfFsV/9qhx63wgysnJ458pOJCfEER9nfPbrUh77anaM31n59PDwxwoNOfMkeC4XTP6cZ44+r4yqEhEREREREakYzGwAcAJB0Dkc6A58TzADvEhRBZ3uvha4bhdFPOXu1xZyLp5gevp+wDPAXGCdu2eHlyxi+6KijYGF4Wtmm9l6guntjYGxEbeN7CNSoV0wcCxr07O2Pe/fvQ1PfDWbUbNWcsIB9bijx4GcP3AsA7+bx8DvgoHW3Q6sT9/OLVi/Jej3lxfHkp6ZQ0KcMeRvnRg1cyW/LFwXi7dTrvU/9VpeGXIPSbmFDyZPT0jm1h43lmFVIiIiIiIiIhVGb+AQ4Bd372NmDYA3o+0cV0JFHFPYCXfPcfcOQBOCUZhtSug1d2Jm/czsZzP7OTs7u+gOIuVU1ZTg3yiqpySyfEPGTudPP6QRn0xasu15emYQzCXEGwnxcXjZlFlxuNN1znjuHfE8Sbk55FjBvzrTE5IL3H1dRERERERERKKyxd1zgWwzqw6sAJpG27nMdsdw93VmNhLoBNQ0s4RwVGcTYHF42WKC4heZWQJQg2BTorz2PJF9Il9jIMFu8KSlpSnLkXLPHd7oeyTu8L/xC3h7/ELuGTqNQX07cmePA4kz45znftyhT0piHMfvX4+7Pp66rS3OYNi1nWlWJ403xixgkkZzRq3V6oX839cvccLvE5hTuwmX/vkeMuMTd1qrUyGniIiIiIiIyB772cxqAi8SzBDfRDGWrizVoNPM6gFZYchZBTiJYIOhkQRDUQcDlwIfh10+CZ+PCc9/4+5uZp8A/zOzRwk2I2oNjC/N2kX2Br2f/5HlG7ZSJy2JNy8/krkrN9P9oH24b9g0Pv9tGacd3JB/n9Oei14et63PiQc24OcFa7dNWwfIdejx5PdUT0nghYsPZ/8GVZm1fFNBLymh6hmbuP6Ht7lk4jC2JKZwb9crGHToadt2T+/be8C2sFMhp4iIiIiIiMiec/e/h4fPm9nnQHV3nxJt/5IKOq2Q9obA6+E6nXHAu+4+zMymAYPN7F/AL8DL4fUvA2+Emw2tIdhpHXefambvAtOAbOBq7bgulcHyDcGIwdWbM/li6jIOaVKTcw5rwj1DpwHw6a9Leeicg3fo0yvftPVIGzKyGTNvFcfvX19BZyHicnM4b8oIbvluELW2bGTwIafwyHEXsya1xg7XjWnWnr69B/Dw8Me4tceNCjlFREREREREdpOZHbqrc+4+MZr7lFTQ+URBjWHi+qcC2ucRrNeZvz0D+HMh97ofuH/PyhQpP6okxhNnsDkzhyqJ8Rzbuh5Pfj2bFRu2clTL2oydt4ajW9Vh/qr0bX2qJSdwZIva3DB40ra22mlJZOfksiEjm+SEODrvV4/nv50bg3e09+u48DcGfDWQdivmMa5JO+458UqmNWhZ6PVjmrWn81WvlmGFIiIiIiIiIhXSf3dxzoGu0dwkqqDTzPYHbgWaRfZx967h42vR3EdEole3WhIDLz4cgPg44+NJS/h21ko2vz+FAb3akRBvbM3K4Y4Pt4/gPuWgfRg9exVbsrYPeK5fLZn/nnsIcWbEmfHpr0v4ZsaKMn8/e7NGG1Zw58hX6TljNIur1ePq02/n0zadwQobrC4iUjK+v70Lm7Zmk5vrZOc6pz/9w7Zzlx/bgn+e1pY/3fsla9OzqF4lgYd7H8K+tVPZmp3LbUMmM2v5JpIT4njnyk4kJ8QRH2d89utSHvtqdgzflYiIiIhI8bh7l5K4T7QjOt8DnidYCFRTxkXKwMI1W+j+xOid2n9esJZeT39fYJ8hExYxZMKiHdpmLNvIaU8WfH1ll5KVwd/Gvc/fxr2PYzza+UIGdjyLjMSUWJcmIpXIBQPHsjY9a4e2hjVSOK51PRat3T5q/+oT9mPakg1c+cYEWtVL494zDuLCl8axNTuXv7w4lvTMHBLijCF/68SomSv5RRvPiYiIiEg5Y2aXFNTu7oOi6R9t0Jnt7s9FXZWIyN7MnZ4zRnPHyFdpvHElQ9scy4Nd+rCkev1YVyYiAsD/9WzLg59N58VLDt/W1rpBNZ4bNQeAuSs306RWFepWTWLVpkzSM4N/h06INxLi4/CYVC0iIiIisseOiDhOAboBE4ESDTqHmtnfgQ+BrXmN7r4myv4iInuFdsvnctdXAzly0VR+a9CKG3rdzE9ND4p1WSJSSbnDG32PxB3+N34Bb49fyEltG7B8QwbTl27c4drpSzdw6kH78NP8tRzSpAaNa1ZhnxoprNqUSZzBsGs706xOGm+MWcAkjeYUERERkXLI3a+NfG5mNYHB0faPNui8NHy8NfK1gcJ36RAR2YvUTl/PLd8N4vzJX7K2SjX6n3IN77Y/idy4+FiXJiKVWO/nf2T5hq3USUvizcuPZO7KzVx9Qisufnn8Ttc+N2ouA3q1Zfh1nZmxbCNTl2wgNzc4l+vQ48nvqZ6SwAsXH87+Daoya/mmMn43IiIiIiIlbjPQItqLiww6zSwO6O/u7+xJVSIisZCYk8UlEz/l+h/epkpWBq8cfjpPHnMBG1Kqxro0ERGWbwgmyqzenMkXU5dxZIvaNKmdymc3HAvAPtVTGHbdsZz59A+s3LSVW4ds34Du+9u78Mea9B3utyEjmzHzVnH8/vUVdIqIiIhIuWNmQ2HbSkxxQFvg3Wj7Fxl0unuumd0KKOgUkXLl+HkTuOvrF2m1ZhGjWhzGfd0uZ26dprEuS0QEgCqJ8cQZbM7MoUpiPMe2rseTX8/m8H99te2a72/vQq+nvg92XU9JYEtWDlk5zvlHNGXc72vYtDWb2mlJZOfksiEjm+SEODrvV4/nv50bw3cmIiIiIrLbHok4zgYWuPuiwi7OL9qp61+Z2S0EYefmvEat0Skie6MWaxbzz29eotvcn5hXqxF9eg9gZKsjiu4oIlKG6lZLYuDFwWZD8XHGx5OW8O2slYVev1/9qjzy50NwYPbyTdz2/mQA6ldL5r/nHkKcGXFmfPrrEr6ZsaIs3oKIiIiISIly928BzKw6YW5pZrWjzSCjDTrPCx+vjnxttEaniOxFqm3dzDU/vkOfnz9ha0Ii/+ryV14/rBdZ8YmxLk1EZCcL12yh+xOjd3lN53+P3HY88Y91dP3vtztdM2PZRk578vsSr09EREREpKyZWT/gXiADyAWMYmSQUQWd7h71op8iImXNPJfev37Fbd8Ook76et47+EQePv4SVqXVinVpIiIiIiIiIhK9W4GD3H3V7nSOKug0s0sKanf3QbvzoiIiJeXQRdO5++sXaL9sDj83PpC/9h7Arw1bx7osERERERERESm+uUB6kVcVItqp65GL26UA3YCJgIJOEYmJfTasov+3r3LmtG9ZWrUO1/W6lU8OPA7MYl2aiIiIiMg2cQZDr+3MsvUZ9H39Z45uVYc7exy4bUO6W96bzILV6TSqkcJ/z+1A9SoJxJnx789nMGrmSs7o0Igrj9s+Y7PNPtXp+dT3TFu6IYbvSkSk1NwB/Ghm44CteY3ufl00naOdun5t5HMzqwkMjr5GEZGSkZy1lSt++pC/j32P+Nxcnjj6fJ4/sjdbklJiXZqIiIiIyE76HNOCOSs2UTU5+Ov3v848iCsGTWDuyk1cdFQzru26H7e8N4Vrurbm0ylLeHPcH+xXvyqv9TmCzv8eyceTlvDxpCUAHNCgGgMvOUwhp4iUKjOLB34GFrt7TzNrQZAD1gEmABe7e6aZXQtcCfwBnBm2dQbOcfcbd/PlXwC+AX4lWKOzWKId0ZnfZkDrdopI2XHn1Fk/8o+Rr9B0/XI+PeAYHuzyVxbVaBDrykRERERECrRP9RS6tqnP0yPncHnn4K/QDlRLCf4qXj0lgeUb8gYsOVULbN/u9A6NGDp5aVmULiKV2/XAdKB6+PzfwGPuPtjMngf6As8BFwLtgTuBU8xsGPB/wAV78NqJ7n7T7naOdo3OoQS/jwHigLbAe7v7oiIixTJlCm8PvpNOf/zK9HrNueD8BxjTrH2sqxIRERER2aW7erXlwc+mbxvNCdD//Sm8etkRZGTnsCkjm7Oe/RGAx76azRt9O3Lp0c1JTUrgwpfG7nS/nu0bcsWgn8usfhGpfMysCXAacD9wk5kZ0BX4S3jJ68DdBEGnAYlAKpAFXAR85u5r9qCEz8Kd14ey49T1qO4Z7YjORyKOs4EF7r4o6hJFRHbHqlVw113wwgsckFyVf5z8dwYfcgo5cfGxrkxEREREZJe6tqnP6k2Z/LZ4A0e1rL2tvW/nlvR57ScmLVxHv+Na8s+eB9L//V85/ZBGDJmwiJdG/86h+9bksXM7cPLj3+HhkKMOTWuyJSuHWcs3xegdiUgl8ThwG1AtfF4HWOfu2eHzRUDj8PhpYCwwFfgB+Bg4ZQ9fP2806B0RbQ60LODanUQbdPZw99sjG8zs3/nbRERKRFYWPPccDBgAGzfCNdfQhU6sr1Kt6L4iIiIiInuBw5vV4sS29enSpgvJCXFUTU7klcuOoFW9NCYtXAfAsMlLeP2vHQE474imXPrKeAAm/rGO5IR4aqcmsXpzJgC9DmnIJ+FanSIieyDBzCKHhg9094EAZtYTWOHuE8zshKJu5O5vAG+Efe8CngS6m9klwELgZncv1jqb7r5HS2XGRXndSQW0dd+TFxYRKdCIEdChA1x/PRx+OEyeDE88oZBTRERERMqV/3wxk04PfkPnf4/k2rd/4ce5q7hi0M9US0mkRd00ADq3rseclcEIzSXrtnDMfnUBaFWvKsmJcdtCTjM47eBGDJ2ioFNE9li2ux8e8TUw4twxwOlmNp9g86GuwBNATTPLGyzZBFgceUMzawR0dPePgJuB84B1QLfiFmdmlxT0FW3/XY7oNLOrgL8DLc1sSsSpagRDUkVESsbcuXDzzfDxx9CqVfDYq1fwf3UiIiIiIhVATq5zxwdTeO6iQ3GH9VuyuHXIZAD+9el0Hjr7YPp2boG7c8t7k7f1O7JFbZau38LCNVtiVbqIVALufgfhlPFwROct7n6hmb0H9CYIPy8lmKIe6T7grvC4CsFU81yCtTuL64iI4xSCsHQiMCiazkVNXf8f8BnwINA/on3jHi4sKiIS2LgRHngAHn0UkpLgoYfghhsgOTnWlYmIiIiIlIix89Ywdl7wV+gvpi7ni6nLd7pmzopN9H5+TKH98zYtEhGJgduBwWb2L+AX4OW8E2b2JwB3nxg2/Q/4lWDq+n+K+0Lufm3kczOrSRCwRmWXQae7rwfWs2fbwouI7Cw3F958E/r3h6VL4dJLg8CzUaNYVyYiIiIiIiJSqbn7KGBUeDwP6FjIdb8AfSOeP06woVFJ2QxEvW5ntJsRiYiUnHHj4LrrYPx4OPJI+Ogj6Fjg70wRERERERERqSTMbCjB1HcI9hZqC7wbbX8FnSJSdpYsCUZwvvEGNGwIgwbBhRdCXLT7oomIiIiIiIhIBfZIxHE2sMDdF0XbWUGniJS+jAx47DG4/37IyoI77gi+qmkndREREREREZHKzsz2Axq4+7f52o8xs2R3nxvNfRR0SlTiDIZe25ll6zPo+/rPPH5eBw5uUoPsHGfyonXc+cGvZOc6reql8XDvQ2jXuDqPfDGLF0fP2+V9pIJzD3ZPv/lmmDcPzjwTHnkk2FVdRERERERERCTwOOGO7/lsCM/1iuYmmi8qUelzTAvmrNi07flHkxbT7b/fcsrj35GSEMf5RzQFYF16FncPncqL3/0e1X2k4mq9cgGcdBKcdRZUqQIjRsCHHyrkFBEREREREZH8Grj7r/kbw7bm0d5EIzqlSPtUT6Frm/o8PXIOl3cONroaNXPltvOTF61nnxpVAFi9OZPVmzPp2qZ+VPeRiqfGlo3c+P1bXPTLcKhZA556Cv72N0jQrxspWv5R301qVeHpC/5EzdQkflu8nhvfnURWjvN/PQ+kU8s6AKQkxlO3ajLt7/kSgP6ntqFL+DvoqW9mM2zK0pi9HxEREREREYlKzV2cqxLtTZQ8SJHu6tWWBz+bTtXknT8uCXHGWX9qzD1Dp+7RfaT8i8/N4YJJn3Pz6DepvnUzb3XoziUjXoc6dWJdmpQjeaO+835P9O/ehpe//52hU5Zy/5kHcd7hTXlz3B/cN2z6tj6XHt2cdo2qA9DlgPq0a1ydHk+OJik+jsH9jmLUzJVs2podk/cjIiIiIiIiUfnZzK5w9xcjG83scmBCtDfR1HXZpa5t6rN6Uya/Ld5Q4Pn7zjyI8b+v4af5a/foPlK+dVowhWGvXc+/RjzH9Pot6NHnSe46+SqFnFIseaO+B/+0cFvb0a3qMvy3ZQC8P3ERJ7fbZ6d+px/SiE8mLQGgdYOqjP99DTm5zpasHGYs28jx+9crmzcgIiIiIiIiu+sGoI+ZjTKz/4Zf3wJ9geujvUmpDq0zs6bAIKAB4MBAd3/CzGoD7xDMsZ8PnOvua83MgCeAHkA6cJm7TwzvdSnwz/DW/3L310uzdgkc3qwWJ7atT5c2XUhOiKNqciKPndeBG9+ZxPXdWlMnLYkrPyw6WN/VfaT8arJuGf8Y+QrdZ/3IwhoNuPLMO/li/05gFuvSpBzKP+q7VmoiG7ZkkZPrACxdn0GD6ik79GlcswpNa1Xhx7mrAJi+dAPXd2vNi6PnUSUxnk4t6zB7xcayfSMiIiIiIiJSLO6+HDjazLoAB4XNn7r7N8W5T2nPIc4Gbnb3iWZWDZhgZiOAy4Cv3f0hM+sP9AduB7oDrcOvI4HngCPDYHQAcDhBYDrBzD5x910PI5Q99p8vZvKfL2YCcFTL2lxxbEtufGcS5x3RlOP2r8dfXhyL++7fR8qnKpkZ/H3se/Qb/wE5cXE8fOzFvHTEmWxNTI51aVJORY76Pqpl7aj79TqkIcN/W0aYhTJ69iraN6nJB1cdzerNmUz8Yy25uVH8khIREREREZGYc/eRwMjd7V+qQae7LwWWhscbzWw60Bg4AzghvOx1YBRB0HkGMMjdHRhrZjXNrGF47Qh3XwMQhqWnAm+XZv1SuPvPPIjF67bw4d+PBuDzqct48us51KuazCfXHkPV5ATc4a+dm3PSo99pfbyKwp0zpo2i/6jXaLhpNR+2PYGHTriM5dXqxroyKecKGvU9oFc7qldJJD7OyMl1GtZIYfmGjB369TqkEf/30Y5rBD8zcg7PjJwDwBPnd2Deqs1l9j5EREREREQkdspsVxgzaw78CRhHsGV83ja4ywimtkMQgi6M6LYobCusPf9r9AP6ASQlJZVg9QIwdt4axs5bA8B+//iswGtWbtpKpwd3Pao48j5Sfhy8dDZ3f/UChy2ZweR9WnP1Gf2Z2OTAWJclFURBo75veGcSz/zlUHoctA9DpyzlnEOb8OW05dv6tKqXRo0qiUz8Y/vg/jiD6lUSWZeeRZt9qtFmn+qMnj25zN+PiIiIiIiIlL0yCTrNrCrwPnCDu2+wiPX73N3NrETmFbr7QGAgQFpamuYqipSAepvWcut3r3Pur1+xMq0mt3a/niEHd8NNe5lJ6Xvo8+k8dcGh3HzyAUxdsoF3IzYq6nVII4ZOXrLD9Ynxcbx3ZScANm3N5sZ3Jm1b41NEREREpDJq3v/TWJdQrsx/6LRYlyB7oNSDTjNLJAg533L3D8Lm5WbW0N2XhlPTV4Tti4GmEd2bhG2L2T7VPa99VGnWLVLZJWVncdmET7j2x8EkZ2fxfMezefro89mUnBrr0qSCixz1vXDNFs585ocCr3v8q9k7tW3NzuWkx74r1fpERERERERk71Tau64b8DIw3d0fjTj1CXAp8FD4+HFE+zVmNphgM6L1YRj6BfCAmdUKrzsZuKM0axeptNzpNnc8//zmJVqsXcpXrY7gX10vZ37tnVaLEBERERERERHZa5T2iM5jgIuBX81sUth2J0HA+a6Z9QUWAOeG54YDPYA5QDrQB8Dd15jZfcBP4XX35m1MJCIlp9Wqhdz1zYsc//tE5tRuwqV/vodvWx4W67JERERERERERIpU2ruufw9YIae7FXC9A1cXcq9XgFdKrjoRyVM9YxPX//A2l0wcxpbEFO7tegWDDj2N7Pgy269MRERERERERGSPKMUQqcTicnM4b8oIbvluELW2bGTwIafwyHEXsya1RqxLExEREREREREpFgWdIpVUx4W/MeCrgbRbMY9xTdpxz4lXMq1By1iXJSIiIiIiIiKyWxR0ilQyjdev4I5Rr9JzxmgWV6vH1affzqdtOoMVtsqEiIiIiIiIiMjeT0GnSCWRkpXBVWPf58rx7+MYj3a+kIEdzyIjMSXWpYmIiIiIiIiI7DEFnSIVnTu9pn9H/1Gv0XjjSoa2OZYHu/RhSfX6sa5MRERERERERKTEKOgUKec6LZjCw8Mf49YeNzKmWfsdzrVbNocBXw+k46Jp/NagFTf0upmfmh4Uo0pFREREREREREqPgk6RcqzTgim8POQeUrO38vKQe+jbewBjmrWnzuZ13PLdIM6bMoK1VarR/5RreLf9SeTGxce6ZBERERERERGRUqGgU6Scigw5AVKzt/LKkHt476CunDntW6pkb+WVw0/nyWMuYENK1RhXKyIiIiIiIiJSuhR0ipRD+UPOPFWyt3LJpM+YtM/+3NzzRubWaRqjCkVEREREREREylZcrAsQkeIpLOSMtP+qBdTftLYMqxIRERERERERiS0FnSLlzMPDH9tlyAnBNPaHhz9WRhWJiIiIiIiIiMSegk6RcmZgx7PJsV3/0U1PSObWHjeWUUUiIiIiIiIiIrGnNTpFyokj//iVa38cTOcFk1mflEpq9lYSc3N2ui49IXnb7usiIiIiIiIiIpWFgk6RvZk7xyyYzHU/DubIhb+xMq0m/+ryV97q0IMOS2fttFanQk4RERERERERqawUdIrsjdw5Yd7PXPfjYA5dMpOlVesw4MQrGdz+ZLYmJgMwpll7+vYesC3sVMgpIiIiIiIiIpWZgk6RvYh5LifNHsc1Y96h/bI5LKpen3+c/HfeO/gkMhMSd7o+L+x8ePhj3NrjRoWcIiIiIiIiIlJpKegU2QuY59J95o9c++NgDlw5n/k1G3Jr9+v4sF1XsuN3/cd0TLP2dL7q1TKqVERERERERERk76SgUySG4nNz6Dn9O64Z8y6tVy9kbu0m3NDzZoYeeBw5cfGxLk9EREREREREpNxQ0CkSAwk52Zw1dSR/H/suLdYuZUbdZlxz+m0MP+AYchVwioiIiIiIiIgUm4JOkTKUlJ3FOb99zd/HvkfT9cv5rUErrjzrTr5sfRRucbEuT0RERERERESk3FLQKVIGkrMzOW/yF/xt3Ps02riKSQ33Z8CJV/JNqyPALNbliYiIiIiIiIiUewo6RUpRSlYGF076nCvHvU/9zWsZ36Qtt3e/jtHN/6SAU0RERERERESkBCnoFCkFaVvTufiX4Vz+04fUTV/Pj/u257rTb2Vs04MVcIqIiIiIiIiIlAIFnSIlqHrGJi6dMJS//vwJtTI28m2LQ3ny6POZ0KRtrEsTEREREREREanQFHSKlICaWzbw158+5rIJQ6memc6I/TrydKfzmNzogFiXJiIiIiIiIiJSKSjoFNkDdTav4/KfPuLiXz6lauYWhu9/NE8ffT7TGrSMdWkiIiIiIiIiIpWKgk6R3VBv0xquHPc+F076nOTsTIYdeCxPdzqXWfWax7o0EREREREREZFKSUGnSHEsXMg9I57j/MlfEp+bw8ftTuCZo85lXp0msa5MREREREREJKb+07s9XdvUZ/WmTE55/DsADmxYjfvPPJjU5HgWrd3CDYMnsWlrNonxxgNnHczBTWrgDvcMncrYeWt2uN+LlxzOvrVTt91LpChxpXlzM3vFzFaY2W8RbbXNbISZzQ4fa4XtZmZPmtkcM5tiZodG9Lk0vH62mV1amjWLFOj33+HKK6FVK/4y6XM+bNeFrle8wM2n3aSQU0RERERERAQYMmERl74yfoe2h85uz78/n8Gpj4/mi6nL6HdcsNTb+UfsC8Cpj4/mopfG8Y8ebTHb3u+UdvuQnpldZrVLxVCqQSfwGnBqvrb+wNfu3hr4OnwO0B1oHX71A56DIBgFBgBHAh2BAXnhqEipmz0b+vSB1q3htdfg8ss5od+L9O9+HX/Uahjr6kRERERERET2GuN/X8P6LVk7tLWol8a434ORmt/PXkX3g/YBoHWDqvw4dzUAqzdnsiEji/aNawCQmhTP5ce24Klv5pRh9VIRlGrQ6e7fAWvyNZ8BvB4evw6cGdE+yANjgZpm1hA4BRjh7mvcfS0wgp3DU5GSNX06XHQRtGkDgwfDNdfAvHnw7LMsrlE/1tWJiIiIiIiIlAuzl2/i5LYNAOhxcEMa1qwCwPSlGzjxwAbExxlNalXh4MY1tp27+eT9eWn0PDKycmJWd2VlZk3NbKSZTTOzqWZ2fdhe2Aztc8LrRptZnbCtlZm9E4v6S3tEZ0EauPvS8HgZ0CA8bgwsjLhuUdhWWLtIyZsyBc49F9q1g48+gptuCqatP/44NNbHTkRERERERKQ4bhsymYuOasbQazpTNTmBrOxcAN79eRHLNmxh6DXHMKBXWyYsWEturtO2YXX2rZ3GF1OXx7jySisbuNnd2wJHAVebWVsKn6F9LXAE8ALwl7DtX8A/y7TqUEw3I3J3NzMvqfuZWT+Cae8kJSWV1G2lMpgwAe67Dz7+GKpVgzvugBtvhLp1Y12ZiIiIiIiISLk1d+VmLgnX7WxRN40ubYJZkjm5zn3Dpm+77v2rjmbeqs0c1aI27ZvU4PvbuxAfZ9RJS2Zwv6M4f+DYmNRf2YSDE5eGxxvNbDrBgMMzgBPCy14HRgG3A7lAMpAKZJnZscAyd59dtpUHYhF0Ljezhu6+NJyaviJsXww0jbiuSdi2mO3fyLz2UQXd2N0HAgMB0tLSSixAlQps7Ngg4Bw+HGrWhLvvhuuug1paBlZERERERERkT9VJS2L15kzM4Jqu+/HWuAUApCTGYRhbsnLovF9dsnNzmbNiE3NWbOLNcX8A0KRWFV6+9AiFnDFiZs2BPwHjKHyG9oPAV8AS4CLgPeD8sq10u1gEnZ8AlwIPhY8fR7RfY2aDCTYeWh+GoV8AD0RsQHQycEcZ1ywVzXffBQHnV19BnTpw//3BOpzVq8e6MhEREREREZFy6cnzO3BUyzrUSktizB1deWzEbNKS47n4qGYAfDF1Ge/9vAiAulWTef2vHXGHZeszuOmdybEsvTJJMLOfI54PDAcO7sDMqgLvAze4+wYz23Yucoa2u48g2E8HM7sEGA7sb2a3AGuB6909vdTeTT6lGnSa2dsEozHrmtkigt3THwLeNbO+wALg3PDy4UAPYA6QDvQBcPc1ZnYf8FN43b3unn+DI5GiucM338C99wZBZ4MG8PDD8Le/QdWqsa5OREREREREpFy7bvCkAttf/WH+Tm2L1m6h23+/3eX9Fq3dwimPf1cClUmEbHc/fFcXmFkiQcj5lrt/EDYXNkM7r08qcBnBpuLDgLOB3sCFwIsl+xYKV6pBp7tfUMipbgVc68DVhdznFeCVEixNKhN3+OyzYATnmDHQqBE88QRccQVUqRLr6kRERERERERE9goWDN18GZju7o9GnCpshnaeW4En3T3LzKoATrB+Z2rpV71dTDcjEilV7pw0ZxzX/PgO/Gc27LsvPPss9OkDKSmxrk5EREREREREZG9zDHAx8KuZTQrb7qTwGdqYWSOgo7vfEzY9RTAzex1wZplUHVLQKRWOeS7dZ/7ItT8O5sCV81lQcx946SW4+GJISop1eSIiIiIiIiIieyV3/x6wQk7vNEM77LMEOC3i+XsEmxKVOQWdUmHE5ebQc8ZorvnxXfZf/QdzazfhxtNu4pO2xzO37+mxLk9EREREREREREqRgk4p9xJysjlz2ij+PuZdWq5dwsy6+3LN6bcx/IBjyI2Lj3V5IiIiIiIiIiJSBhR0SrmVmJNF71+/5qqx77Hv+uVMrd+SK8+8ky/3Pwq3uFiXJyIiIiIiIiIiZUhBp5Q7ydmZnDvlS/429n0ab1zJpIatufvEK/mm1RFghS0jISIiIiIiIiIiFZmCTik3UrIyuHDS5/Qb/wENNq3hp8ZtuePUa/iuxaEKOEVEREREREREKjkFnbLXS9uazkWThnPF+A+pm76eH/dtzw09b2HMvgcr4BQREREREREREUBBp+zFqm3dzKUThtL3p4+plbGR75r/iSePOZ+fm7SLdWkiIiIiIiIiIrKXUdApe50aWzby158/ps+EoVTfupmvWh3BU0efz+RGB8S6NBERERERERER2Usp6JS9Ru309Vz+04dcMvFTqmZu4bP9j+bpo89jaoNWsS5NRERESlnz/p/GuoRyZf5Dp8W6BBEREZG9joJOibl6m9bQb/wHXDjpM1KyMvm0TWeePvo8ZtZrHuvSRERERERERESknFDQKTGzz4ZV/G3cEC6Y/AXxuTl83PZ4nu10LnPrNI11aSIiMafRbcWj0W0iUh7pd3309HteRESioaBTylyT9cu5aux79P71K+Lcef+gbjx71J/5o1bDWJcmIiIiIiIiIiLllIJOKTGdFkzh4eGPcWuPGxnTrP1O55utXcLVY97lrKkjyTXjvYNP4rmj/sziGvVjUK2IiIiIiIiIiFQkCjqlRHRaMIWXh9xDavZWXh5yD317D9gWdrZatZBrxrzD6dO/Iys+gTf/1IMXOp7Dsup1Y1y1iIiIiIiIiIhUFAo6ZY9FhpzAtrDz7pOu5Ljff6HHjO/JSEzipSPO5KUjzmJl1VoxrlhERERku+9v78Kmrdnk5jrZuc7pT//AgQ2rcf+ZB5OaHM+itVu4YfAkNm3NJiHO+Pc57WnXuDoJcXF8MHERz46aG+u3ICJSLmhd2uhpXVqR3aOgU/ZI/pAzT2r2Vv7z2ZOkJyTxbKc/8/LhZ7A2tUaMqhQRkeIoKPS56aT9OaltA9ydVZsyueW9yazYuJWT2jbgppP2xz249t6h0/h5wdpYvwWRYrtg4FjWpmdte/7Q2e15YPh0xv2+hj8f3oR+x7Xk0RGz6HFwQ5IS4jj18dGkJMbx1U3H88nkJSxauyWG1YsUT0G/5+/o3oYTD2xAZk4uf6xJ59b3JrMhI5szOjTiyuNabuvbZp/q9Hzqe6Yt3RDDdyAiIlIwBZ2y2woLOSMZ8EOzDgo5RUTKmfyhz8Dv5vHoiFkAXHZ0c67v1pp/fPQbP8xZxYhpywFos081nvnLoXR79NuY1CxSklrUS2Pc72sA+H72Kgb9teO2PwNVkuKJjzNSEuPJzM5lY0Z2LEsV2S35f89/P2cV//liJjm5Tv9T2/D3E/bjoc9n8PGkJXw8aQkABzSoxsBLDlPIKSIiey0FnbLbHh7+2C5DToAq2Zk8PPwxOl/1ahlVJSIipWHT1u1BTmpSPB4ep2fmFNguUp64wxt9j8Qd/jd+AW+PX8js5Zs4uW0Dvpy2nB4HN6RhzSoADP91KSe1bcD4O7tRJSme+4ZNY/2WrCJeQWTvN3r2qm3HvyxcS/eDGu50zekdGjF08tKyLEukxMQZDL22M8vWZ9D39Z/59zntad8kGJDz+6rN3PLeZNIzc+jbuQXnH9GU7FxnzeZMbhsyhcXrNGpfpLxQ0Cm77dYeNxY5ojM9IZlbe9xYhlWJiMieKij0Abjl5AM4+9DGbMzI5oIXx267/pR2DbjtlDbUqZrEX1/7KVZli+y23s//yPINW6mTlsSblx/J3JWbuW3IZAb0ase1XVvz1fTlZGXnAnBI05rk5DpHPvA1Naok8u7fOvH9nFUsXKO/BEv5Udjv+Tx/PrwpwwoINHu2b8gVg34uqzJFSlSfY1owZ8UmqiYHMch9w6Zt+4fcf552IJd2as5z385l2pIN9Hr6ezKycrnoyH25o3sbrnn7l1iWLiLFEBfrAqT8GtOsPX17DyA9IbnA8+kJyTvsvi4iIuVD7+d/pOdT33PZq+O5pFNzOraoDcAjX87k6Ie+4eNJi7m0U7Nt138xdTndHv2Wfm9M4KaTDohV2SK7bfmG4B9tV2/O5IupyzikSU3mrtzMJa+Mp9fT3/PJ5CUsWJMOwBkdGvHtrJVk5zqrN2cyYcFa2jeuGcPqRYqvsN/zAFd32Y+cXOejSYt36NOhaU22ZOUwa/mmsi5XZI/tUz2Frm3qM/in7aF+5GyVlMR4PJyXMmbeajKygn/c+mXhOvapkVK2xYrIHlHQKXuksLBTIaeISPlVUOgT6aNfFnNqAVMax/++hn1rp1IrNbEsyhQpEVUS40lLit92fGzresxavpE6aUkAmME1XffjrXELAFiybgtHt6qz7fo/Na3J3JUKfqR8Kez3fO/DmtCtTX2uH7zz6LVehzTkk3CtTpHy5q5ebXnws+m477jIzsO92/PTP06kVb2qvPbj/J36nXt4U0bNWllGVYpISVDQKXssf9ipkFNEpPwqLPRpXid12zUntdtnW7DTLKK9XaPqJCXE7bC5hcjerm61JIZcdTSfXX8sH19zDN/MWMG3s1ZyeodGfHPz8Xx90/Gs2JDBez8vAmDQmAWkJiXw5Y3H8ck1x/DehEXMWLYxxu9CJHqF/Z4/fv96XHlcSy4f9PO20Wx5zOC0gxsxdIqCTil/urapz+pNmfy2eOdNtG4dMoUjH/iKOSs20at9ox3OndmhMe2b1GDgt/PKqlQRKQFao1NKRF7Y+fDwx7i1x40KOUVEyqm61ZIYePHhAMTHGR9PWsK3s1by3EWH0rJuVXLdWbxuC//48FcAuh+0D2cf2oTsnFwysnK55n8TY1m+SLEtXLOF7k+M3qn91R/m8+oP83dqT8/M4Wp9zqUcK+z3/KhbTiApIY43+3YE4Jc/1vGPj34D4MgWtVm6fovWopVy6fBmtTixbX26tOlCckIcVZMTeey8Dtz4ziQAch2GTlnClce15L0JwT9qHbNfHa7puh/nvTCGzJzcXdxdRPY2CjqlxIxp1l67q4uIlHOFhT5XvVlwsPP8t/N4XiMdRETKjcJ+z5/wyKhC+4ydt4aznv2xFKsSKT3/+WIm//liJgBHtazNFce25MZ3JtGsTioLVgfrL594YAPmrtwMBDNUHjjrYC59ZTyrN2fGrG4R2T0KOkVERERERESk0jCD//75EKqmJGAY05du4J/hCOY7uh9IalICz154KACL12VwxaCfY1muiBSDgk4RERERERERqfDGzlvD2HlrAOj9/JgCr7no5XFlWZKIlLBytRmRmZ1qZjPNbI6Z9Y91PSIiIiIiIiIiIrJ3KDdBp5nFA88A3YG2wAVm1ja2VYmIiIiIiIiIiMjeoNwEnUBHYI67z3P3TGAwcEaMaxIREREREREREZG9QHkKOhsDCyOeLwrbREREREREREREpJIzd491DVExs97Aqe5+efj8YuBId78m4pp+QL/w6aHAljIvVCQ2EoDsWBchUsb0uZfKRp95qWz0mZfKRp95qWz0mY+NKu5engY+Fkt52nV9MdA04nmTsG0bdx8IDCzLokT2Bmb2s7sfHus6RMqSPvdS2egzL5WNPvNS2egzL5WNPvNSGspTgvsT0NrMWphZEnA+8EmMaxIREREREREREZG9QLkZ0enu2WZ2DfAFEA+84u5TY1yWiIiIiIiIiIiI7AXKTdAJ4O7DgeGxrkNkL6QlG6Qy0udeKht95qWy0WdeKht95qWy0WdeSly52YxIREREREREREREpDDlaY1OERERERERERERkQIp6BQpx8ysqZmNNLNpZjbVzK6PdU0iZcHM4s3sFzMbFutaREqbmdU0syFmNsPMpptZp1jXJFKazOzG8P9rfjOzt80sJdY1iZQ0M3vFzFaY2W8RbbXNbISZzQ4fa8WyRpGSVMhn/uHw/2+mmNmHZlYzhiVKBaGgU6R8ywZudve2wFHA1WbWNsY1iZSF64HpsS5CpIw8AXzu7m2AQ9BnXyowM2sMXAcc7u4HEWxCen5sqxIpFa8Bp+Zr6w987e6tga/D5yIVxWvs/JkfARzk7u2BWcAdZV2UVDwKOkXKMXdf6u4Tw+ONBH/5bRzbqkRKl5k1AU4DXop1LSKlzcxqAMcBLwO4e6a7r4tpUSKlLwGoYmYJQCqwJMb1iJQ4d/8OWJOv+Qzg9fD4deDMsqxJpDQV9Jl39y/dPTt8OhZoUuaFSYWjoFOkgjCz5sCfgHExLkWktD0O3AbkxrgOkbLQAlgJvBou1/CSmaXFuiiR0uLui4FHgD+ApcB6d/8ytlWJlJkG7r40PF4GNIhlMSJl7K/AZ7EuQso/BZ0iFYCZVQXeB25w9w2xrkektJhZT2CFu0+IdS0iZSQBOBR4zt3/BGxGUxmlAgvXJDyDIORvBKSZ2UWxrUqk7Lm7Ax7rOkTKgpn9g2BZtrdiXYuUfwo6Rco5M0skCDnfcvcPYl2PSCk7BjjdzOYDg4GuZvZmbEsSKVWLgEXunjdafwhB8ClSUZ0I/O7uK909C/gAODrGNYmUleVm1hAgfFwR43pESp2ZXQb0BC4MA36RPaKgU6QcMzMjWLdturs/Gut6REqbu9/h7k3cvTnB5hTfuLtG+kiF5e7LgIVmdkDY1A2YFsOSRErbH8BRZpYa/n9ON7QBl1QenwCXhseXAh/HsBaRUmdmpxIsSXW6u6fHuh6pGBR0ipRvxwAXE4xqmxR+9Yh1USIiUqKuBd4ysylAB+CB2JYjUnrC0ctDgInAr//f3r3G2FWVcRh//lNAGkQIisag0BSoBSpUrJWLxqJWP2iIRMQLiASjYpRqE00QjZ80aTTRSEnDLaEgl4AWLKJCESypptoClt4QGwuoCcFADRENtbavH/Ya5mQ600512uE0zy85mX32We9aa68zH868866z6f5euWZCJyXtBUluBVYCb0ry1ySfBhYAc5NsoqtuXjCRc5TG0yi/81cChwL3tb9lr5rQSWq/ECuDJUmSJEmSJPU7KzolSZIkSZIk9T0TnZIkSZIkSZL6nolOSZIkSZIkSX3PRKckSZIkSZKkvmeiU5IkSZIkSVLfM9EpSZLUp5JMSbJ+DG0+0fN8VpIr9v7sdprHk0les6/HnQhJLu853u17JEmSpPFholOSJGn/NgV4KdFZVQ9V1byJm87ESHLAPhzu8t03kSRJ0ngz0SlJkjSOklyYZG2SR5P8sJ1bnOTcnjYvtJ9zkjyYZGmSzUkWJDk/yaok65Icu6v4YeNOSbIiySPtcUZ7aQHwziRrksxvY96dZKBVWR7e08emJK9LcmSSJUlWt8eZI4x3Upvnmna9x7fzP0nycJINST47yhpd0BN7dZJJ7bE4yfp27fNHiFuc5KokDyX5Y5IPtvOTkny3zXVtks/1rO+KJHcBG0fo74UWtyHJL5PMTrK8vRdntzYHJ7m+zen3Sc5q5y9KckeSe9q6faedXwBMbtd2cxtqUpJr2zjLkkweaV0kSZL0/9mX/9mWJEnaryU5CfgGcEZVPZvkiDGEnQKcAGwBNgPXVdXsJF8CLgW+PMbh/wbMraoXW9LxVmAWcBnwlaoaTArOAaiqHUmWAucA1yd5O/BUVT2T5Bbg+1X16yRHA/e2Ofa6BPhBVd2c5CBgUjt/cVVtacm81UmWVNVzPWt0AvBR4Myq2pZkEXA+sAE4qqpmtHaHj3KdU4DZwLHAr5IcB1wIPF9Vb0vyCuA3SZa19qcCM6rqiRH6OgR4oKq+muRO4FvAXOBE4AbgLuAL3XLVm5NMB5YlmdbiZwJvAbYCjydZWFWXJfliVc1s1zEFOB74eFV9JsntwIeBm0a5PkmSJP2PTHRKkiSNn3cDP6qqZwGqassYYlZX1dMASf4EDCbo1gFn7cHYBwJXJpkJbAem7bo5ALcB3wSuBz7WngO8FzgxyWC7VyV5ZVX1VpKuBL6e5A3AHVW1qZ2fl+ScdvxGuiTfcz1x7wHeSpcEBZhMl6T9KTA1yULgZwytw3C3V9UOYFOSzcB04H3AyT1Vr4e1cf8NrBolyUl7/Z52vA7Y2pKv6+gSqgDvABYCVNUfkjzF0NreX1XPAyTZCBwD/GWEcZ6oqjXt+OGeviVJkjSOTHRKkiTtff+hfWVQkgHgoJ7XtvYc7+h5voOhz2q7ih80H3iGrkJ0AHhxDPNaCRyX5EjgQ3QVjbT406pq1D6q6pYkvwM+APy8bRffQZckPb2q/pVkOXDwsNAAN1TV14b3meQU4P101aLnARePNPQIzwNcWlX3DutvDvDP0a4B2FZVg/29tPat2nUsn5N737vtjP7Zeng7t65LkiTtBX5HpyRJ0vh5APhIklcD9Gxdf5KuihHgbLrqyz0xlvjDgKdbteMnGdpK/g/g0JE6bUm+O4HvAY/1bDFfRrdtnnYdM4fHJpkKbK6qK4ClwMltDn9vSc7pwGkjDHs/cG6S17Z+jkhyTLo7sg9U1RK67f+njjRnuvUdSPf9pVOBx+m21n8+yYGtz2lJDhklfk+toNtaT9uyfnQbc1e2Dc5FkiRJ+46JTkmSpHFSVRuAbwMPJnmULoEIcC3wrnbudHZdZTiSscQvAj7V2kzvabMW2J7u5kg73eCHbrv6BQxtWweYB8xqN/bZSFdhOdx5wPoka4AZwI1028APSPIY3U2Qfjs8qKo20iUylyVZC9wHvB44Clje+rsJ2Knis/kzsAr4BXBJqzq9ju5mQ48kWQ9czfjtXFoEDLTt7LcBF1XV1t3EXAOs7bkZkSRJkvaBDO3WkSRJkl6+kiwG7q6qH0/0XCRJkvTyY0WnJEmSJEmSpL5nRackSZIkSZKkvmdFpyRJkiRJkqS+Z6JTkiRJkiRJUt8z0SlJkiRJkiSp75nolCRJkiRJktT3THRKkiRJkiRJ6nsmOiVJkiRJkiT1vf8ClmjYdfyXoCIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1584x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZKkyq54XR0G"
      },
      "source": [
        "Is the data truely representative of the reality ?\n",
        "These graphs show that the data should be treated with caution: in july and august 2010 and 2012 the turnover looks very low compared to the 2011 data. Idem for december 2012 where it appears to be 4 / 6 times lower than in 2011 /2010."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7I-uYioVx_M"
      },
      "source": [
        "# Let's take a closer look at the data store by store"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NwCGyv9HTjz"
      },
      "source": [
        "# We temporarily assign a  integer data type to the stores to disply their metrics in the right order\n",
        "\n",
        "df['store'] = [int(x) for x in df.store]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4UJ5At7TmbU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "outputId": "0812d4ae-9924-4009-f3c0-ffbbbf3e0b13"
      },
      "source": [
        "df = df.sort_values('store')\n",
        "s = df.groupby([df.store, df.year])['weekly_sales'].agg('sum').unstack().sort_index()\n",
        "s['cumul'] = s.sum(axis=1)\n",
        "s = s.apply(lambda x: round(100 * x / s.cumul,2))\n",
        "s.drop(columns=['cumul'], inplace=True)\n",
        "ax = s.plot(kind='barh',stacked=True, figsize=(16,12));\n",
        "for c in ax.containers:\n",
        "    ax.bar_label(c, label_type='center', color = 'white' )\n",
        "plt.legend(loc='best')\n",
        "plt.title('Sales per shop and per year in %')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooJbG19MUjO_"
      },
      "source": [
        "It appears that the turnover is not equally spread over the 3 years. One may wonder if the missing years are due to a store shut-down or to a data collection problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgZQlb7XWWVO"
      },
      "source": [
        "\n",
        "Do the stores have the same level of sales?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8QGDNbwWT-4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        },
        "outputId": "7e9c501a-0c90-4bfd-b7e5-3f04fe6e6482"
      },
      "source": [
        "#Plotting turn-over by year and by store\n",
        "\n",
        "ax = df.groupby([df.store, df.year])['weekly_sales'].agg('sum').mul(0.001).unstack().sort_index().apply(lambda x : round(x)).plot(kind='bar',stacked=True, figsize=(16,12))\n",
        "for c in ax.containers:\n",
        "    ax.bar_label(c, label_type='center', color = 'white' )\n",
        "plt.legend(loc='best')\n",
        "plt.title('Sales per shop and per year (in KUS$)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWKkBFW-W786"
      },
      "source": [
        "It will be interesting to investigate why some businesses have 3 times or more  turnover than others. \n",
        "\n",
        "2012 does not appear very preponderant in the analysis of the turnover (no notable progression compared to the previous year)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kb_cwx6QTwPg"
      },
      "source": [
        "We can question the missing years for some stores: closure? unavailable data? A temporal analysis at this stage appears therefore quite problematic."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvs2EpKbPS9u",
        "outputId": "fd31c30c-3482-4bd0-8860-edfa6cb05cdf"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['store', 'date', 'weekly_sales', 'holiday_flag', 'temperature',\n",
              "       'fuel_price', 'cpi', 'unemployment', 'year', 'month', 'month_label',\n",
              "       'day_of_week', 'week_of_year'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN4VT0h1UTr8"
      },
      "source": [
        "# Distribution plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bii99sytYXJQ"
      },
      "source": [
        "#Let's modify the type for the store column in the dataframe\n",
        "df['store'] = ['store_' + str(s) for s in df.store]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IFqSolJUROX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "812bf59d-24f2-4e86-bd57-4816f5ba4e0c"
      },
      "source": [
        "\n",
        "plt.rcParams['figure.figsize'] = [6, 2]\n",
        "%matplotlib inline\n",
        "\n",
        "# sample time series data\n",
        "df2 = df.copy()\n",
        "df2.drop(columns = ['store','holiday_flag','month_label','day_of_week'], inplace=True) #categorical columns\n",
        "\n",
        "# create distplots\n",
        "for c in df2.columns:\n",
        "    plt.figure()             # <==================== here!\n",
        "    sns.distplot(df2[c])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYsf408FfNtz"
      },
      "source": [
        "# Conclusion : It should make sense to drop outliers in the unemployment column.\n",
        "#Scaling the data will be necessary"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udaHCdHQfvdS"
      },
      "source": [
        "# Plot pairwise relationships in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxDSZeIz8tGC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acb08dc0-af50-407a-b671-12ddcf4c3e5c"
      },
      "source": [
        "\n",
        "sns.pairplot(df[['weekly_sales', 'temperature',\n",
        "       'fuel_price', 'cpi', 'unemployment']],corner=True, diag_kind=\"kde\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x900 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQeIKGfz7ZCm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb81232b-0246-478d-f4c7-bcc46f750fdb"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['store', 'date', 'weekly_sales', 'holiday_flag', 'temperature',\n",
              "       'fuel_price', 'cpi', 'unemployment', 'year', 'month', 'month_label',\n",
              "       'day_of_week', 'week_of_year'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RloVmj6lEwP-"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q86xDBQCEgZh"
      },
      "source": [
        "# **Part II Preprocessing & model selection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLuuMe1Vqt50"
      },
      "source": [
        "# Detection of highly correlated features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3kOmMQUpQq8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "outputId": "5019006f-2989-4f84-9039-baf207d070bb"
      },
      "source": [
        "# Plotting a matrix correlation of features\n",
        "corr = df.corr()\n",
        "plt.figure(figsize=(16,12))\n",
        "sns.heatmap(corr, annot=True, cmap='YlGnBu');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpzjNP-VlZAx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "69e7e194-d320-4e99-a2a0-2022b8cdc980"
      },
      "source": [
        "#Let's identify most correlated features :\n",
        "correlations = corr.unstack().reset_index()\n",
        "correlations = correlations.rename(columns=dict(zip(correlations.columns, ['feature1', 'feature2', 'coeff'])))\n",
        "correlations['coeff'] = correlations['coeff'].apply(lambda x : abs(x))\n",
        "\n",
        "#Filtering features with a high correlation : \n",
        "correlations[(correlations.coeff !=1) &  (correlations.coeff > 0.8)].drop_duplicates(subset=['coeff'])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature1</th>\n",
              "      <th>feature2</th>\n",
              "      <th>coeff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>fuel_price</td>\n",
              "      <td>year</td>\n",
              "      <td>0.810191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>month</td>\n",
              "      <td>week_of_year</td>\n",
              "      <td>0.996446</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      feature1      feature2     coeff\n",
              "21  fuel_price          year  0.810191\n",
              "55       month  week_of_year  0.996446"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p08RuduephrM"
      },
      "source": [
        "#Conclusion : let's drop the month which appear to be to much correlated with the week of year !\n",
        "df.drop('month', axis=1, inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Je-WkOQNU1j"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSd3_RirhAqf"
      },
      "source": [
        "# Outliers detection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mT7S7fCIhk0V"
      },
      "source": [
        "We drop lines containing invalid values or outliers : In this project, will be considered as outliers all the numeric features that don't fall within the range :  [X¯−3σ,X¯+3σ] . This concerns the columns : Temperature, Fuel_price, CPI and Unemployment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWfkEcUeg_lp"
      },
      "source": [
        "for i in ['temperature', 'fuel_price', 'cpi', 'unemployment']:\n",
        "  df = df[~(np.abs(df[i]-df[i].mean()) > (3*df[i].std()))]\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCjhHHwBkJCH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "152cb4d6-1d6c-461a-e6fb-aa03176532e1"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fAsxR2YzNzg"
      },
      "source": [
        "# Pipelines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGVf_qpVzeZt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "259ae19d-a2ad-44b4-946d-61ecaff9bb2c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 127 entries, 44 to 71\n",
            "Data columns (total 13 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   store         127 non-null    object        \n",
            " 1   date          127 non-null    datetime64[ns]\n",
            " 2   weekly_sales  113 non-null    float64       \n",
            " 3   holiday_flag  127 non-null    object        \n",
            " 4   temperature   114 non-null    float64       \n",
            " 5   fuel_price    114 non-null    float64       \n",
            " 6   cpi           117 non-null    float64       \n",
            " 7   unemployment  115 non-null    float64       \n",
            " 8   year          127 non-null    int64         \n",
            " 9   month         127 non-null    int64         \n",
            " 10  month_label   127 non-null    object        \n",
            " 11  day_of_week   127 non-null    object        \n",
            " 12  week_of_year  127 non-null    float64       \n",
            "dtypes: datetime64[ns](1), float64(6), int64(2), object(4)\n",
            "memory usage: 13.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZj92w27tMj_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2acb3276-1960-4b0e-a2f3-650bd27a03d7"
      },
      "source": [
        "# Missing values\n",
        "100* df.isnull().sum().sort_values(ascending=False) / df.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weekly_sales    11.023622\n",
              "fuel_price      10.236220\n",
              "temperature     10.236220\n",
              "unemployment     9.448819\n",
              "cpi              7.874016\n",
              "week_of_year     0.000000\n",
              "day_of_week      0.000000\n",
              "month_label      0.000000\n",
              "month            0.000000\n",
              "year             0.000000\n",
              "holiday_flag     0.000000\n",
              "date             0.000000\n",
              "store            0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZUyqYwtaSYp"
      },
      "source": [
        "Droping lines where target values are missing :\n",
        "\n",
        "The target variable (Y) corresponds to the column Weekly_Sales. One can see above that there are some missing values in this column.\n",
        "We never use imputation techniques on the target : it might create some bias in the predictions !\n",
        "Then, we just drop the lines in the dataset for which the value in Weekly_Sales is missing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDsRmD2IZ6e3"
      },
      "source": [
        "df.dropna(subset=['weekly_sales'], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVXMFdFObT8E"
      },
      "source": [
        "# Converting date to unix\n",
        "df['date'] = df.date.apply(lambda x: int(x.strftime(\"%s\")))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTd1w2tVhPKs"
      },
      "source": [
        "Splitting features & target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SwWtiKa0bxt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "563f7f88-70a7-4830-a686-54179585ae70"
      },
      "source": [
        "target = 'weekly_sales'\n",
        "\n",
        "X = df.loc[:, df.columns != target]\n",
        "Y = df.loc[:,target] \n",
        "\n",
        "Y = Y.apply(lambda x: float(x[1:]) if type(x)==str else x)\n",
        "\n",
        "#Split the data into a train set and test set \n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)\n",
        "\n",
        "\n",
        "# Convert pandas DataFrames to numpy arrays before using scikit-learn\n",
        "print(\"Convert pandas DataFrames to numpy arrays...\")\n",
        "X_train = X_train.values\n",
        "X_test = X_test.values\n",
        "Y_train = Y_train.tolist()\n",
        "Y_test = Y_test.tolist()\n",
        "print(\"...Numpy conversion to arrays Done !\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Convert pandas DataFrames to numpy arrays...\n",
            "...Numpy conversion to arrays Done !\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MIfyOCrzv8v"
      },
      "source": [
        "Automatically detect positions of numeric/categorical features : "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "so8L6cbizt_x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fe236b0-b18e-4496-d631-2eadcb2b5ae0"
      },
      "source": [
        "idx = 0\n",
        "numeric_features = []\n",
        "numeric_indices = []\n",
        "categorical_features = []\n",
        "categorical_indices = []\n",
        "for i,t in X.dtypes.iteritems():\n",
        "    if ('float' in str(t)) or ('int' in str(t)) :\n",
        "        numeric_features.append(i)\n",
        "        numeric_indices.append(idx)\n",
        "    else :\n",
        "        categorical_features.append(i)\n",
        "        categorical_indices.append(idx)\n",
        "\n",
        "    idx = idx + 1\n",
        "\n",
        "print('Found numeric features ', numeric_features,' at positions ', numeric_indices)\n",
        "print('Found categorical features ', categorical_features,' at positions ', categorical_indices)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found numeric features  ['date', 'temperature', 'fuel_price', 'cpi', 'unemployment', 'year', 'month', 'week_of_year']  at positions  [1, 3, 4, 5, 6, 7, 8, 11]\n",
            "Found categorical features  ['store', 'holiday_flag', 'month_label', 'day_of_week']  at positions  [0, 2, 9, 10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8jYDuCj0Pzb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1649c24c-9046-48a7-9d1d-240faf26ddbd"
      },
      "source": [
        "from missingpy import KNNImputer\n",
        "imputer = KNNImputer()\n",
        "\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer',KNNImputer()),\n",
        "    ('scaler',StandardScaler()) \n",
        "])\n",
        "#StandardScaler()\n",
        "# Create pipeline for categorical features\n",
        "\n",
        "categorical_transformer = Pipeline(\n",
        "    steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')), # missing values will be replaced by most frequent value\n",
        "    ('encoder', OneHotEncoder(drop='first')) # first column will be dropped to avoid creating correlations between features\n",
        "    ])\n",
        "\n",
        "# Use ColumnTransformer to make a preprocessor object that describes all the treatments to be done\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_indices),\n",
        "        ('cat', categorical_transformer, categorical_indices)\n",
        "    ])\n",
        "\n",
        "# Preprocessings on train set\n",
        "print(\"Performing preprocessings on train set...\")\n",
        "X_train = preprocessor.fit_transform(X_train)\n",
        "print('preprocessings on train set...Done.')\n",
        "\n",
        "\n",
        "# Preprocessings on test set\n",
        "print(\"Performing preprocessings on test set...\")\n",
        "X_test = preprocessor.transform(X_test) # Don't fit again !! The test set is used for validating decisions\n",
        "# we made based on the training set, therefore we can only apply transformations that were parametered using the training set.\n",
        "# Otherwise this creates what is called a leak from the test set which will introduce a bias in all your results.\n",
        "print('preprocessings on test set...Done.')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing preprocessings on train set...\n",
            "preprocessings on train set...Done.\n",
            "Performing preprocessings on test set...\n",
            "preprocessings on test set...Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2pBzeur0qQp"
      },
      "source": [
        "\n",
        "# **# PART II : Models fitting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-dCGF_y2Cs0"
      },
      "source": [
        "#Instanciate models\n",
        "lin = LinearRegression()\n",
        "lasso1000 = Lasso(alpha = 1000)\n",
        "lasso2000= Lasso(alpha = 2000)\n",
        "lasso500 = Lasso(alpha =500)\n",
        "ridge_regressor_small_alpha = Ridge(alpha = 0)\n",
        "ridge_regressor_big_alpha = Ridge(alpha = 1)\n",
        "regressor_ada = AdaBoostRegressor(lin)\n",
        "regressor_xgb = XGBRegressor()\n",
        "regressor_random_forrest = RandomForestRegressor(max_depth=4, random_state=42)\n",
        "tree_regressor = DecisionTreeRegressor()\n",
        "bagging_regressor = BaggingRegressor(n_estimators=40)\n",
        "gbr = GradientBoostingRegressor(learning_rate=0.5,n_estimators=40)\n",
        "\n",
        "# m = Prophet()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izWUnZJj_cN3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b7d014b-5146-4319-f947-7c269b8d6e39"
      },
      "source": [
        "\n",
        "#from sklearn.metrics import classification_report #pas utilisé\n",
        "classifier = []\n",
        "for model in [lin,lasso1000, lasso2000, lasso500, ridge_regressor_small_alpha, ridge_regressor_big_alpha, regressor_ada, regressor_xgb, regressor_random_forrest,tree_regressor, bagging_regressor, gbr ]:\n",
        "  model.fit(X_train, Y_train)\n",
        "  Y_train_pred    = model.predict(X_train)\n",
        "  Y_test_pred     = model.predict(X_test)\n",
        "  score           = model.score(X_test, Y_test)\n",
        "\n",
        "  r2_score_train  = r2_score(Y_train, Y_train_pred)\n",
        "  r2_score_test   = r2_score(Y_test, Y_test_pred)\n",
        "  \n",
        "  mae_train       = round(mean_absolute_error(Y_train, Y_train_pred) /1000)\n",
        "  mean_squared_error_train = round(np.sqrt(mean_squared_error(Y_train, Y_train_pred)))\n",
        "  mean_squared_error_test = round(np.sqrt(mean_squared_error(Y_test, Y_test_pred)))\n",
        "  mae_test= round(mean_absolute_error(Y_test, Y_test_pred) / 1000) #pour lisibilité\n",
        "\n",
        "#for classification only\n",
        "\n",
        "  # accuracy_train  = accuracy_score(Y_train, Y_train_pred)\n",
        "  # accuracy_test = accuracy_score(Y_test, Y_test_pred)\n",
        "  # f1_score_train =f1_score(Y_train, Y_train_pred)\n",
        "  # f1_score_test = f1_score(Y_test, Y_test_pred)\n",
        "\n",
        "  \n",
        "\n",
        "  classifier.append({'classifier'     : model,\n",
        "                    'score'           : score,\n",
        "                    'r2_score_train'  : r2_score_train,\n",
        "                     'r2_score_test'  : r2_score_test,\n",
        "                     'mae_train / 1000'      : mae_train,\n",
        "                     'mae_test / 1000'       : mae_test,\n",
        "                     'rmse_train' : mean_squared_error_train,\n",
        "                     'rmse_test' : mean_squared_error_test\n",
        "                    # 'accuracy_train' : accuracy_train,\n",
        "                    # 'accuracy_test' : accuracy_test,\n",
        "                    # 'f1_score_train' : f1_score_train,\n",
        "                    # 'f1_score_test' : f1_score_test\n",
        "                     }\n",
        "                    )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[17:47:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "95XP2HeejM0O",
        "outputId": "c53f3d0b-339d-40fa-be98-53d9c0d8dbaa"
      },
      "source": [
        "all_scores = pd.DataFrame.from_dict(classifier)\n",
        "all_scores.sort_values(by='r2_score_test', ascending=False).reset_index().drop(columns=['index'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>classifier</th>\n",
              "      <th>score</th>\n",
              "      <th>r2_score_train</th>\n",
              "      <th>r2_score_test</th>\n",
              "      <th>mae_train / 1000</th>\n",
              "      <th>mae_test / 1000</th>\n",
              "      <th>rmse_train</th>\n",
              "      <th>rmse_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Lasso(alpha=1000, copy_X=True, fit_intercept=T...</td>\n",
              "      <td>0.932723</td>\n",
              "      <td>0.971956</td>\n",
              "      <td>0.932723</td>\n",
              "      <td>80</td>\n",
              "      <td>118</td>\n",
              "      <td>111020</td>\n",
              "      <td>171915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lasso(alpha=500, copy_X=True, fit_intercept=Tr...</td>\n",
              "      <td>0.929031</td>\n",
              "      <td>0.974808</td>\n",
              "      <td>0.929031</td>\n",
              "      <td>80</td>\n",
              "      <td>121</td>\n",
              "      <td>105223</td>\n",
              "      <td>176568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lasso(alpha=2000, copy_X=True, fit_intercept=T...</td>\n",
              "      <td>0.925772</td>\n",
              "      <td>0.964681</td>\n",
              "      <td>0.925772</td>\n",
              "      <td>85</td>\n",
              "      <td>123</td>\n",
              "      <td>124590</td>\n",
              "      <td>180577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ridge(alpha=0, copy_X=True, fit_intercept=True...</td>\n",
              "      <td>0.913762</td>\n",
              "      <td>0.975918</td>\n",
              "      <td>0.913762</td>\n",
              "      <td>82</td>\n",
              "      <td>131</td>\n",
              "      <td>102879</td>\n",
              "      <td>194637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LinearRegression(copy_X=True, fit_intercept=Tr...</td>\n",
              "      <td>0.913317</td>\n",
              "      <td>0.975925</td>\n",
              "      <td>0.913317</td>\n",
              "      <td>81</td>\n",
              "      <td>131</td>\n",
              "      <td>102864</td>\n",
              "      <td>195140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(LinearRegression(copy_X=True, fit_intercept=T...</td>\n",
              "      <td>0.893136</td>\n",
              "      <td>0.996001</td>\n",
              "      <td>0.893136</td>\n",
              "      <td>31</td>\n",
              "      <td>143</td>\n",
              "      <td>41922</td>\n",
              "      <td>216668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ridge(alpha=1, copy_X=True, fit_intercept=True...</td>\n",
              "      <td>0.888069</td>\n",
              "      <td>0.930408</td>\n",
              "      <td>0.888069</td>\n",
              "      <td>137</td>\n",
              "      <td>192</td>\n",
              "      <td>174889</td>\n",
              "      <td>221745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>([DecisionTreeRegressor(ccp_alpha=0.0, criteri...</td>\n",
              "      <td>0.767963</td>\n",
              "      <td>0.999659</td>\n",
              "      <td>0.767963</td>\n",
              "      <td>10</td>\n",
              "      <td>243</td>\n",
              "      <td>12248</td>\n",
              "      <td>319269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>XGBRegressor(base_score=0.5, booster='gbtree',...</td>\n",
              "      <td>0.672850</td>\n",
              "      <td>0.977313</td>\n",
              "      <td>0.672850</td>\n",
              "      <td>76</td>\n",
              "      <td>299</td>\n",
              "      <td>99856</td>\n",
              "      <td>379098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(DecisionTreeRegressor(ccp_alpha=0.0, criterio...</td>\n",
              "      <td>0.513430</td>\n",
              "      <td>0.945046</td>\n",
              "      <td>0.513430</td>\n",
              "      <td>118</td>\n",
              "      <td>369</td>\n",
              "      <td>155411</td>\n",
              "      <td>462329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(DecisionTreeRegressor(ccp_alpha=0.0, criterio...</td>\n",
              "      <td>0.356383</td>\n",
              "      <td>0.809777</td>\n",
              "      <td>0.356383</td>\n",
              "      <td>237</td>\n",
              "      <td>460</td>\n",
              "      <td>289143</td>\n",
              "      <td>531731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>DecisionTreeRegressor(ccp_alpha=0.0, criterion...</td>\n",
              "      <td>-0.085442</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.085442</td>\n",
              "      <td>0</td>\n",
              "      <td>431</td>\n",
              "      <td>0</td>\n",
              "      <td>690528</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           classifier  ...  rmse_test\n",
              "0   Lasso(alpha=1000, copy_X=True, fit_intercept=T...  ...     171915\n",
              "1   Lasso(alpha=500, copy_X=True, fit_intercept=Tr...  ...     176568\n",
              "2   Lasso(alpha=2000, copy_X=True, fit_intercept=T...  ...     180577\n",
              "3   Ridge(alpha=0, copy_X=True, fit_intercept=True...  ...     194637\n",
              "4   LinearRegression(copy_X=True, fit_intercept=Tr...  ...     195140\n",
              "5   (LinearRegression(copy_X=True, fit_intercept=T...  ...     216668\n",
              "6   Ridge(alpha=1, copy_X=True, fit_intercept=True...  ...     221745\n",
              "7   ([DecisionTreeRegressor(ccp_alpha=0.0, criteri...  ...     319269\n",
              "8   XGBRegressor(base_score=0.5, booster='gbtree',...  ...     379098\n",
              "9   (DecisionTreeRegressor(ccp_alpha=0.0, criterio...  ...     462329\n",
              "10  (DecisionTreeRegressor(ccp_alpha=0.0, criterio...  ...     531731\n",
              "11  DecisionTreeRegressor(ccp_alpha=0.0, criterion...  ...     690528\n",
              "\n",
              "[12 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcCng5qjiT7P"
      },
      "source": [
        "# **# Part III : Let's try to minimize the overfitting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyPQ8odTlELz",
        "outputId": "9c664f26-f525-4b9e-bc50-643d76f7babb"
      },
      "source": [
        "gridsearch = GridSearchCV(\n",
        "    Lasso(), {'alpha': [910, 920, 930, 940, 950, 960, 970, 980, 990, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, 1080, 1090]}, cv=3)\n",
        "gridsearch.fit(X_train, Y_train)\n",
        "print(\"...Done.\")\n",
        "print(\"Best hyperparameters : \", gridsearch.best_params_)\n",
        "print(\"Best R2 score : \", gridsearch.best_score_)\n",
        "\n",
        "Y_test_pred = gridsearch.predict(X_test)\n",
        "print(\"R2 score on training set : \", r2_score(Y_train, Y_train_pred))\n",
        "print(\"R2 score on test set : \", r2_score(Y_test, Y_test_pred))\n",
        "\n",
        "mae_test= round(mean_absolute_error(Y_test, Y_test_pred) / 1000) #pour lisibilité\n",
        "print(\"MAE Test score / 1000: \",mae_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "...Done.\n",
            "Best hyperparameters :  {'alpha': 940}\n",
            "Best R2 score :  0.8943602711375244\n",
            "R2 score on training set :  0.9996586934416916\n",
            "R2 score on test set :  0.9320191822165182\n",
            "MAE Test score / 1000:  119\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dppXibQ1q1Wy",
        "outputId": "201e5787-32d9-44ee-e2f9-b195e4ccdc64"
      },
      "source": [
        "gridsearch.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=940, copy_X=True, fit_intercept=True, max_iter=1000,\n",
              "      normalize=False, positive=False, precompute=False, random_state=None,\n",
              "      selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVuuwiXqrLma",
        "outputId": "c37c2ac1-d227-48a4-d14b-fde6bed8c4b3"
      },
      "source": [
        "model = gridsearch.best_estimator_\n",
        "model.fit(X_train, Y_train)\n",
        "model.coef_\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([    6161.58605406,   -25318.1112102 ,   -28614.56812816,\n",
              "          58387.80030929,   -29960.82764335,        0.        ,\n",
              "          40486.87858548,   446735.85387265,   176049.16685804,\n",
              "         515680.68812398,   583746.44217609,  -721425.02980735,\n",
              "       -1019535.90212676,  -614236.75831628,  -269505.20024421,\n",
              "             -0.        ,   222234.09392831,   368427.13161769,\n",
              "       -1148304.83557663,   668262.29321825, -1258854.77508833,\n",
              "              0.        ,  -959472.3410871 ,  -730744.38373227,\n",
              "       -1065717.11222204,   121165.1071701 ,    64591.99051384,\n",
              "             -0.        ,    66744.37547257,    91157.24669754,\n",
              "             -0.        ,   -51590.64869862,    19261.83442646,\n",
              "          30167.43160442,        0.        ,       -0.        ,\n",
              "          25148.47592879,   -73220.08723249,   -63950.86119761,\n",
              "         -26382.86794359,    85609.16764295,        0.        ,\n",
              "         155312.53244184])"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBrrW4MjI6x-",
        "outputId": "78571c20-b171-4f1c-c4b1-b5130476d368"
      },
      "source": [
        "preprocessed_features = numeric_features\n",
        "cat_features_preprocessed = preprocessor.transformers_[1][1][1].get_feature_names()\n",
        "\n",
        "preprocessed_features +=cat_features_preprocessed.tolist()\n",
        "print(preprocessed_features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['date', 'temperature', 'fuel_price', 'cpi', 'unemployment', 'year', 'week_of_year', 'x0_store_10', 'x0_store_11', 'x0_store_13', 'x0_store_14', 'x0_store_15', 'x0_store_16', 'x0_store_17', 'x0_store_18', 'x0_store_19', 'x0_store_2', 'x0_store_20', 'x0_store_3', 'x0_store_4', 'x0_store_5', 'x0_store_6', 'x0_store_7', 'x0_store_8', 'x0_store_9', 'x1_regular', 'x1_unknown', 'x2_August', 'x2_December', 'x2_February', 'x2_January', 'x2_July', 'x2_June', 'x2_March', 'x2_May', 'x2_November', 'x2_October', 'x2_September', 'x3_Monday', 'x3_Saturday', 'x3_Sunday', 'x3_Thursday', 'x3_Tuesday']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tztm2r6qKQb8",
        "outputId": "e78f2169-6bc4-44e7-8107-6428aea40606"
      },
      "source": [
        "removed_features = np.array(preprocessed_features)[(model.coef_ == 0)]\n",
        "removed_features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['year', 'x0_store_19', 'x0_store_6', 'x2_August', 'x2_January',\n",
              "       'x2_May', 'x2_November', 'x3_Thursday'], dtype='<U12')"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zh4-etlK2TtF"
      },
      "source": [
        "One can wonder about the relevance of removing some stores, some months or a day (...)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "SGI6ETDx3CKL",
        "outputId": "c03f8238-ab5b-4d43-f3bd-987d329f828c"
      },
      "source": [
        "all_coef_importance = pd.DataFrame()\n",
        "all_coef_importance['features'] = preprocessed_features\n",
        "all_coef_importance['importance'] = model.coef_\n",
        "all_coef_importance.sort_values(by='importance', ascending=False).plot(x = 'features', y='importance',kind = 'barh', figsize=(10, 10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='features'>"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKWno-XY4z9M"
      },
      "source": [
        "# **A gridsearchcv does not significantly improve the results since the R2 score on test set: goes from 0.932014 to 0.932019**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYvpFviipeNd"
      },
      "source": [
        "# Cross-validated score for a Ridge model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "itcy_n27Ob3y",
        "outputId": "2465ac28-d234-4d27-968f-f9a5410344c5"
      },
      "source": [
        "# Perform 3-fold cross-validation to evaluate the generalized R2 score obtained with a Ridge model\n",
        "print(\"3-fold cross-validation...\")\n",
        "regressor = Ridge()\n",
        "params = {\n",
        "    'alpha': [ 0.05,0.06,0.07,0.08,0.09,0.1] # 0 corresponds to no regularization\n",
        "}\n",
        "gridsearch = GridSearchCV(regressor, param_grid = params, cv = 3) # cv : the number of folds to be used for CV\n",
        "gridsearch.fit(X_train, Y_train)\n",
        "scores = cross_val_score(regressor, X_train, Y_train, cv=3)\n",
        "\n",
        "print(\"Best hyperparameters : \", gridsearch.best_params_)\n",
        "print(\"Best R2 score : \", gridsearch.best_score_)\n",
        "\n",
        "Y_test_pred = gridsearch.predict(X_test)\n",
        "print(\"R2 score on training set : \", r2_score(Y_train, Y_train_pred))\n",
        "print(\"R2 score on test set : \", r2_score(Y_test, Y_test_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3-fold cross-validation...\n",
            "Best hyperparameters :  {'alpha': 0.07}\n",
            "Best R2 score :  0.8768902593732183\n",
            "R2 score on training set :  0.9996586934416916\n",
            "R2 score on test set :  0.9174204989431481\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "KaQ8p8y_7eg0",
        "outputId": "f2823414-15bf-4118-c15a-40930ebfad93"
      },
      "source": [
        "model = gridsearch.best_estimator_\n",
        "model.fit(X_train, Y_train)\n",
        "model.coef_\n",
        "preprocessed_features = numeric_features\n",
        "cat_features_preprocessed = preprocessor.transformers_[1][1][1].get_feature_names()\n",
        "\n",
        "preprocessed_features +=cat_features_preprocessed.tolist()\n",
        "\n",
        "all_coef_importance = pd.DataFrame()\n",
        "all_coef_importance['features'] = preprocessed_features\n",
        "all_coef_importance['importance'] = model.coef_\n",
        "all_coef_importance.sort_values(by='importance', ascending=False).plot(x = 'features', y='importance',kind = 'barh', figsize=(10, 10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Removed features :  []\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='features'>"
            ]
          },
          "metadata": {},
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DipQPvtu63PV"
      },
      "source": [
        "**A gridsearchcv with ridge model does neither significantly improve the results**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3K00WXQteZR"
      },
      "source": [
        "## Last but not least : let's do a test to see if inflation, temperature, and unemployement data drive accurate results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5D3vxUkoN-d",
        "outputId": "7cac57ed-f89b-477f-9e0b-228bd72c55f5"
      },
      "source": [
        "X = df.loc[:, df.columns.isin(['holiday_flag', 'temperature',\n",
        "       'fuel_price', 'cpi', 'unemployment','week_of_year'])]\n",
        "\n",
        "Y = df.loc[:,target] \n",
        "\n",
        "Y = Y.apply(lambda x: float(x[1:]) if type(x)==str else x)\n",
        "\n",
        "#Split the data into a train set and test set \n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)\n",
        "\n",
        "\n",
        "# Convert pandas DataFrames to numpy arrays before using scikit-learn\n",
        "print(\"Convert pandas DataFrames to numpy arrays...\")\n",
        "X_train = X_train.values\n",
        "X_test = X_test.values\n",
        "Y_train = Y_train.tolist()\n",
        "Y_test = Y_test.tolist()\n",
        "print(\"...Numpy conversion to arrays Done !\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Convert pandas DataFrames to numpy arrays...\n",
            "...Numpy conversion to arrays Done !\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBmnAAsPpY0r",
        "outputId": "c20accf4-f544-4b0c-c312-2ac9e7161a4d"
      },
      "source": [
        "idx = 0\n",
        "numeric_features = []\n",
        "numeric_indices = []\n",
        "categorical_features = []\n",
        "categorical_indices = []\n",
        "for i,t in X.dtypes.iteritems():\n",
        "    if ('float' in str(t)) or ('int' in str(t)) :\n",
        "        numeric_features.append(i)\n",
        "        numeric_indices.append(idx)\n",
        "    else :\n",
        "        categorical_features.append(i)\n",
        "        categorical_indices.append(idx)\n",
        "\n",
        "    idx = idx + 1\n",
        "\n",
        "print('Found numeric features ', numeric_features,' at positions ', numeric_indices)\n",
        "print('Found categorical features ', categorical_features,' at positions ', categorical_indices)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found numeric features  ['temperature', 'fuel_price', 'cpi', 'unemployment', 'week_of_year']  at positions  [1, 2, 3, 4, 5]\n",
            "Found categorical features  ['holiday_flag']  at positions  [0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9KLelsxpcoB",
        "outputId": "fe61160e-49a6-4b68-80cb-eb7af0cb5bb2"
      },
      "source": [
        "from missingpy import KNNImputer\n",
        "imputer = KNNImputer()\n",
        "\n",
        "numeric_transformer = Pipeline(steps=[\n",
        "    ('imputer',KNNImputer()),\n",
        "    ('scaler',StandardScaler()) \n",
        "])\n",
        "#StandardScaler()\n",
        "# Create pipeline for categorical features\n",
        "\n",
        "categorical_transformer = Pipeline(\n",
        "    steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')), # missing values will be replaced by most frequent value\n",
        "    ('encoder', OneHotEncoder(drop='first')) # first column will be dropped to avoid creating correlations between features\n",
        "    ])\n",
        "\n",
        "# Use ColumnTransformer to make a preprocessor object that describes all the treatments to be done\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', numeric_transformer, numeric_indices),\n",
        "        ('cat', categorical_transformer, categorical_indices)\n",
        "    ])\n",
        "\n",
        "# Preprocessings on train set\n",
        "print(\"Performing preprocessings on train set...\")\n",
        "X_train = preprocessor.fit_transform(X_train)\n",
        "print('preprocessings on train set...Done.')\n",
        "\n",
        "\n",
        "# Preprocessings on test set\n",
        "print(\"Performing preprocessings on test set...\")\n",
        "X_test = preprocessor.transform(X_test) # Don't fit again !! The test set is used for validating decisions\n",
        "# we made based on the training set, therefore we can only apply transformations that were parametered using the training set.\n",
        "# Otherwise this creates what is called a leak from the test set which will introduce a bias in all your results.\n",
        "print('preprocessings on test set...Done.')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Performing preprocessings on train set...\n",
            "preprocessings on train set...Done.\n",
            "Performing preprocessings on test set...\n",
            "preprocessings on test set...Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PRoA2hQpiPl"
      },
      "source": [
        "#Instanciate models\n",
        "lin = LinearRegression()\n",
        "lasso1000 = Lasso(alpha = 1000)\n",
        "lasso2000= Lasso(alpha = 2000)\n",
        "lasso500 = Lasso(alpha =500)\n",
        "ridge_regressor_small_alpha = Ridge(alpha = 0)\n",
        "ridge_regressor_big_alpha = Ridge(alpha = 1)\n",
        "regressor_ada = AdaBoostRegressor(lin)\n",
        "regressor_xgb = XGBRegressor()\n",
        "regressor_random_forrest = RandomForestRegressor(max_depth=4, random_state=42)\n",
        "tree_regressor = DecisionTreeRegressor()\n",
        "bagging_regressor = BaggingRegressor(n_estimators=40)\n",
        "gbr = GradientBoostingRegressor(learning_rate=0.5,n_estimators=40)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qv6_OUGapoJR",
        "outputId": "ee085f6c-4901-44ca-d5e9-15832a206bbe"
      },
      "source": [
        "\n",
        "#from sklearn.metrics import classification_report #pas utilisé\n",
        "classifier = []\n",
        "for model in [lin,lasso1000, lasso2000, lasso500, ridge_regressor_small_alpha, ridge_regressor_big_alpha, regressor_ada, regressor_xgb, regressor_random_forrest,tree_regressor, bagging_regressor, gbr ]:\n",
        "  model.fit(X_train, Y_train)\n",
        "  Y_train_pred    = model.predict(X_train)\n",
        "  Y_test_pred     = model.predict(X_test)\n",
        "  score           = model.score(X_test, Y_test)\n",
        "\n",
        "  r2_score_train  = r2_score(Y_train, Y_train_pred)\n",
        "  r2_score_test   = r2_score(Y_test, Y_test_pred)\n",
        "  \n",
        "  mae_train       = round(mean_absolute_error(Y_train, Y_train_pred) /1000)\n",
        "  mean_squared_error_train = round(np.sqrt(mean_squared_error(Y_train, Y_train_pred)))\n",
        "  mean_squared_error_test = round(np.sqrt(mean_squared_error(Y_test, Y_test_pred)))\n",
        "  mae_test= round(mean_absolute_error(Y_test, Y_test_pred) / 1000) #pour lisibilité\n",
        "\n",
        "#for classification only\n",
        "\n",
        "  # accuracy_train  = accuracy_score(Y_train, Y_train_pred)\n",
        "  # accuracy_test = accuracy_score(Y_test, Y_test_pred)\n",
        "  # f1_score_train =f1_score(Y_train, Y_train_pred)\n",
        "  # f1_score_test = f1_score(Y_test, Y_test_pred)\n",
        "\n",
        "  \n",
        "\n",
        "  classifier.append({'classifier'     : model,\n",
        "                    'score'           : score,\n",
        "                    'r2_score_train'  : r2_score_train,\n",
        "                     'r2_score_test'  : r2_score_test,\n",
        "                     'mae_train / 1000'      : mae_train,\n",
        "                     'mae_test / 1000'       : mae_test,\n",
        "                     'rmse_train' : mean_squared_error_train,\n",
        "                     'rmse_test' : mean_squared_error_test\n",
        "                    # 'accuracy_train' : accuracy_train,\n",
        "                    # 'accuracy_test' : accuracy_test,\n",
        "                    # 'f1_score_train' : f1_score_train,\n",
        "                    # 'f1_score_test' : f1_score_test\n",
        "                     }\n",
        "                    )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[18:21:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "fuNmcg6zptiF",
        "outputId": "1d897535-cafa-49fc-ccf1-7853ea699038"
      },
      "source": [
        "all_scores = pd.DataFrame.from_dict(classifier)\n",
        "all_scores.sort_values(by='r2_score_test', ascending=False).reset_index().drop(columns=['index'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>classifier</th>\n",
              "      <th>score</th>\n",
              "      <th>r2_score_train</th>\n",
              "      <th>r2_score_test</th>\n",
              "      <th>mae_train / 1000</th>\n",
              "      <th>mae_test / 1000</th>\n",
              "      <th>rmse_train</th>\n",
              "      <th>rmse_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(DecisionTreeRegressor(ccp_alpha=0.0, criterio...</td>\n",
              "      <td>-0.027173</td>\n",
              "      <td>0.724454</td>\n",
              "      <td>-0.027173</td>\n",
              "      <td>280</td>\n",
              "      <td>599</td>\n",
              "      <td>347999</td>\n",
              "      <td>671738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XGBRegressor(base_score=0.5, booster='gbtree',...</td>\n",
              "      <td>-0.063291</td>\n",
              "      <td>0.961699</td>\n",
              "      <td>-0.063291</td>\n",
              "      <td>96</td>\n",
              "      <td>547</td>\n",
              "      <td>129743</td>\n",
              "      <td>683446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lasso(alpha=2000, copy_X=True, fit_intercept=T...</td>\n",
              "      <td>-0.075286</td>\n",
              "      <td>0.155536</td>\n",
              "      <td>-0.075286</td>\n",
              "      <td>544</td>\n",
              "      <td>633</td>\n",
              "      <td>609217</td>\n",
              "      <td>687290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ridge(alpha=1, copy_X=True, fit_intercept=True...</td>\n",
              "      <td>-0.076034</td>\n",
              "      <td>0.155635</td>\n",
              "      <td>-0.076034</td>\n",
              "      <td>544</td>\n",
              "      <td>634</td>\n",
              "      <td>609181</td>\n",
              "      <td>687529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Lasso(alpha=1000, copy_X=True, fit_intercept=T...</td>\n",
              "      <td>-0.076791</td>\n",
              "      <td>0.155671</td>\n",
              "      <td>-0.076791</td>\n",
              "      <td>543</td>\n",
              "      <td>633</td>\n",
              "      <td>609168</td>\n",
              "      <td>687771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Lasso(alpha=500, copy_X=True, fit_intercept=Tr...</td>\n",
              "      <td>-0.077560</td>\n",
              "      <td>0.155705</td>\n",
              "      <td>-0.077560</td>\n",
              "      <td>543</td>\n",
              "      <td>633</td>\n",
              "      <td>609155</td>\n",
              "      <td>688017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ridge(alpha=0, copy_X=True, fit_intercept=True...</td>\n",
              "      <td>-0.078339</td>\n",
              "      <td>0.155716</td>\n",
              "      <td>-0.078339</td>\n",
              "      <td>543</td>\n",
              "      <td>634</td>\n",
              "      <td>609151</td>\n",
              "      <td>688265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LinearRegression(copy_X=True, fit_intercept=Tr...</td>\n",
              "      <td>-0.078339</td>\n",
              "      <td>0.155716</td>\n",
              "      <td>-0.078339</td>\n",
              "      <td>543</td>\n",
              "      <td>634</td>\n",
              "      <td>609151</td>\n",
              "      <td>688265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(DecisionTreeRegressor(ccp_alpha=0.0, criterio...</td>\n",
              "      <td>-0.091277</td>\n",
              "      <td>0.904299</td>\n",
              "      <td>-0.091277</td>\n",
              "      <td>167</td>\n",
              "      <td>599</td>\n",
              "      <td>205087</td>\n",
              "      <td>692382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(LinearRegression(copy_X=True, fit_intercept=T...</td>\n",
              "      <td>-0.109345</td>\n",
              "      <td>0.138841</td>\n",
              "      <td>-0.109345</td>\n",
              "      <td>545</td>\n",
              "      <td>634</td>\n",
              "      <td>615209</td>\n",
              "      <td>698090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>([DecisionTreeRegressor(ccp_alpha=0.0, criteri...</td>\n",
              "      <td>-0.177130</td>\n",
              "      <td>0.998555</td>\n",
              "      <td>-0.177130</td>\n",
              "      <td>20</td>\n",
              "      <td>625</td>\n",
              "      <td>25198</td>\n",
              "      <td>719102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>DecisionTreeRegressor(ccp_alpha=0.0, criterion...</td>\n",
              "      <td>-0.897370</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.897370</td>\n",
              "      <td>0</td>\n",
              "      <td>675</td>\n",
              "      <td>0</td>\n",
              "      <td>912965</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           classifier  ...  rmse_test\n",
              "0   (DecisionTreeRegressor(ccp_alpha=0.0, criterio...  ...     671738\n",
              "1   XGBRegressor(base_score=0.5, booster='gbtree',...  ...     683446\n",
              "2   Lasso(alpha=2000, copy_X=True, fit_intercept=T...  ...     687290\n",
              "3   Ridge(alpha=1, copy_X=True, fit_intercept=True...  ...     687529\n",
              "4   Lasso(alpha=1000, copy_X=True, fit_intercept=T...  ...     687771\n",
              "5   Lasso(alpha=500, copy_X=True, fit_intercept=Tr...  ...     688017\n",
              "6   Ridge(alpha=0, copy_X=True, fit_intercept=True...  ...     688265\n",
              "7   LinearRegression(copy_X=True, fit_intercept=Tr...  ...     688265\n",
              "8   (DecisionTreeRegressor(ccp_alpha=0.0, criterio...  ...     692382\n",
              "9   (LinearRegression(copy_X=True, fit_intercept=T...  ...     698090\n",
              "10  ([DecisionTreeRegressor(ccp_alpha=0.0, criteri...  ...     719102\n",
              "11  DecisionTreeRegressor(ccp_alpha=0.0, criterion...  ...     912965\n",
              "\n",
              "[12 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Al4bAyDLsfFD"
      },
      "source": [
        "Conclusion : the test scores are extremely bad which proves that it was a very bad idea to restrict the analysis to 'holiday_flag', 'temperature', 'fuel_price', 'cpi', 'unemployment', 'week_of_year' features"
      ]
    }
  ]
}