store_sales.ipynb

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        "[texte du lien](https://)\n",
        "# Store Sales Predection :\n",
        "bigstore is a renowned retail corporation that operates a chain of hypermarkets. Here, bigstore has provided a data combining of 45 stores including store information and monthly sales. The data is provided on weekly basis. bigstore tries to find the impact of holidays on the sales of store. For which it has included four holidays’ weeks into the dataset which are Christmas, Thanksgiving, Super bowl, Labor Day. Here we are owing to Analyze the dataset given. Before doing that, let me point out the objective of this analysis.\n",
        "\n",
        "#  Objectives\n",
        "\n",
        "Our Main Objective is to predict sales of store in a week. As in dataset size and time related data are given as feature, so analyze if sales are impacted by time-based factors and space- based factor. Most importantly how inclusion of holidays in a week soars the sales in store?\n",
        "\n",
        "\n",
        "\n",
        "Dataset Info:\\\n",
        "This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Store_sales. Within this file you will find the following fields:\\\n",
        "* Store - the store number\n",
        "* Date - the week of sales\n",
        "* Weekly_Sales -  sales for the given store\n",
        "* Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week\n",
        "* Temperature - Temperature on the day of sale\n",
        "* Fuel_Price - Cost of fuel in the region\n",
        "* CPI – Prevailing consumer price index\n",
        "* Unemployment - Prevailing unemployment rate\n",
        "* Holiday Events\\\n",
        "Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13\\\n",
        "Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13\\\n",
        "Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13\\\n",
        "Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13\n",
        "\n",
        "### Acknowledgements\n",
        "The dataset is taken from Kaggle.\n",
        "\n",
        "### Objective:\n",
        "- Understand the Dataset & cleanup (if required).\n",
        "- Build Regression models to predict the sales  a single & multiple feature.\n",
        "- Also evaluate the models & compare their respective scores like R2, RMSE, etc.\n"
      ]
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        "# <center> Stractegic Plan of Action:\n",
        "**We aim to solve the problem statement by creating a plan of action, Here are some of the necessary steps:**\n",
        "1. Data Exploration\n",
        "2. Exploratory Data Analysis (EDA)\n",
        "3. Data Pre-processing\n",
        "4. Data Manipulation\n",
        "5. Feature Selection/Extraction\n",
        "6. Predictive Modelling\n",
        "7. Project Outcomes & Conclusion\n",
        "\n",
        "\n"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "# <center>1. Data Exploration"
      ]
    },
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      "cell_type": "markdown",
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      },
      "source": [
        "Importing the necessary libraries, like pandas, numpy and some plotting libraries such as matplotlib and seaborn......"
      ]
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    {
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      "source": [
        "\n",
        "import os\n",
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from IPython.display import display\n",
        "\n",
        "from statsmodels.formula import api\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
        "import matplotlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
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      "source": [
        "default size :"
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      "source": [
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['font.size'] = 16\n",
        "matplotlib.rcParams['figure.figsize'] = (10, 6)\n",
        "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-kU7FBwop04W"
      },
      "source": [
        "Reading of data as a pandas dataframe and named as **DATA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "ZDiNL73np5is"
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      "source": [
        "data = pd.read_csv('store.csv')"
      ]
    },
    {
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      "execution_count": 82,
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              "      Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
              "0         1  05-02-2010    1643690.90             0        42.31       2.572   \n",
              "1         1  12-02-2010    1641957.44             1        38.51       2.548   \n",
              "2         1  19-02-2010    1611968.17             0        39.93       2.514   \n",
              "3         1  26-02-2010    1409727.59             0        46.63       2.561   \n",
              "4         1  05-03-2010    1554806.68             0        46.50       2.625   \n",
              "...     ...         ...           ...           ...          ...         ...   \n",
              "6430     45  28-09-2012     713173.95             0        64.88       3.997   \n",
              "6431     45  05-10-2012     733455.07             0        64.89       3.985   \n",
              "6432     45  12-10-2012     734464.36             0        54.47       4.000   \n",
              "6433     45  19-10-2012     718125.53             0        56.47       3.969   \n",
              "6434     45  26-10-2012     760281.43             0        58.85       3.882   \n",
              "\n",
              "             CPI  Unemployment  \n",
              "0     211.096358         8.106  \n",
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              "4     211.350143         8.106  \n",
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              "6430  192.013558         8.684  \n",
              "6431  192.170412         8.667  \n",
              "6432  192.327265         8.667  \n",
              "6433  192.330854         8.667  \n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6435,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 45,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          40,\n          26,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 143,\n        \"samples\": [\n          \"04-05-2012\",\n          \"18-06-2010\",\n          \"02-09-2011\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564366.6220536975,\n        \"min\": 209986.25,\n        \"max\": 3818686.45,\n        \"num_unique_values\": 6435,\n        \"samples\": [\n          1138800.32,\n          1304850.67,\n          1769296.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Flag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.44493287581158,\n        \"min\": -2.06,\n        \"max\": 100.14,\n        \"num_unique_values\": 3528,\n        \"samples\": [\n          51.13,\n          98.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4590197071928525,\n        \"min\": 2.472,\n        \"max\": 4.468,\n        \"num_unique_values\": 892,\n        \"samples\": [\n          2.84,\n          3.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.35671229566418,\n        \"min\": 126.064,\n        \"max\": 227.2328068,\n        \"num_unique_values\": 2145,\n        \"samples\": [\n          184.613419,\n          214.1083654\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8758847818627977,\n        \"min\": 3.879,\n        \"max\": 14.313,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          8.185,\n          7.804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "_Shek1NGqdtc"
      },
      "outputs": [],
      "source": [
        "data['Date'] = pd.to_datetime(data['Date'], dayfirst=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kZrrJo6Jqmv-"
      },
      "source": [
        "Using the date column i create three seperate columns of weekday, month and year and added to the existing dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "ji2hOIaVqpL3"
      },
      "outputs": [],
      "source": [
        "data['weekday'] = data.Date.dt.weekday\n",
        "data['month'] = data.Date.dt.month\n",
        "data['year'] = data.Date.dt.year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZP-CMQMqyWE"
      },
      "source": [
        "Now I drop the date columns because of no use of it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "4HzsI40PqoTM"
      },
      "outputs": [],
      "source": [
        "data.drop(['Date'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "XQyTpqDLt4wL"
      },
      "outputs": [],
      "source": [
        "target = 'Weekly_Sales'\n",
        "features = [i for i in data.columns if i not in [target]]\n",
        "original_df = data.copy(deep=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5xWIlYUq4EO"
      },
      "source": [
        " the modified dataset is look like:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "HePK35rBq5FP",
        "outputId": "9512fc62-b5cf-4e70-db3b-34d2e8c7e45b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Store  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
              "0      1    1643690.90             0        42.31       2.572  211.096358   \n",
              "1      1    1641957.44             1        38.51       2.548  211.242170   \n",
              "2      1    1611968.17             0        39.93       2.514  211.289143   \n",
              "\n",
              "   Unemployment  weekday  month  year  \n",
              "0         8.106        4      2  2010  \n",
              "1         8.106        4      2  2010  \n",
              "2         8.106        4      2  2010  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>Holiday_Flag</th>\n",
              "      <th>Temperature</th>\n",
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              "      <td>42.31</td>\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6435,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 45,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          40,\n          26,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564366.6220536975,\n        \"min\": 209986.25,\n        \"max\": 3818686.45,\n        \"num_unique_values\": 6435,\n        \"samples\": [\n          1138800.32,\n          1304850.67,\n          1769296.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Flag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.44493287581158,\n        \"min\": -2.06,\n        \"max\": 100.14,\n        \"num_unique_values\": 3528,\n        \"samples\": [\n          51.13,\n          98.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4590197071928525,\n        \"min\": 2.472,\n        \"max\": 4.468,\n        \"num_unique_values\": 892,\n        \"samples\": [\n          2.84,\n          3.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.35671229566418,\n        \"min\": 126.064,\n        \"max\": 227.2328068,\n        \"num_unique_values\": 2145,\n        \"samples\": [\n          184.613419,\n          214.1083654\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8758847818627977,\n        \"min\": 3.879,\n        \"max\": 14.313,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          8.185,\n          7.804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekday\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJaZq-UAts__",
        "outputId": "c7d199cc-b603-4435-82f1-2ae3a9958227"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6435 entries, 0 to 6434\n",
            "Data columns (total 10 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Store         6435 non-null   int64  \n",
            " 1   Weekly_Sales  6435 non-null   float64\n",
            " 2   Holiday_Flag  6435 non-null   int64  \n",
            " 3   Temperature   6435 non-null   float64\n",
            " 4   Fuel_Price    6435 non-null   float64\n",
            " 5   CPI           6435 non-null   float64\n",
            " 6   Unemployment  6435 non-null   float64\n",
            " 7   weekday       6435 non-null   int32  \n",
            " 8   month         6435 non-null   int32  \n",
            " 9   year          6435 non-null   int32  \n",
            "dtypes: float64(5), int32(3), int64(2)\n",
            "memory usage: 427.4 KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWCc0rdqrB-4"
      },
      "source": [
        "Explored the unique values of the weekday, month and year columns as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYOdS8t9rDlb",
        "outputId": "591097dc-c164-4ea6-99b3-10f8650e0324"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "years unique value [2010 2011 2012]\n",
            "months unique value [ 2  3  4  5  6  7  8  9 10 11 12  1]\n",
            "weekday unique value [4]\n"
          ]
        }
      ],
      "source": [
        "print('years unique value', data.year.unique())\n",
        "print('months unique value', data.month.unique())\n",
        "print('weekday unique value', data.weekday.unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bveu-bdXrNyj"
      },
      "source": [
        "Months and weekday are as usual, but the data is taken from year 2010, 2011, 2012 only."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "C0aHcUborRVC",
        "outputId": "8cac2340-16aa-4cfa-9e38-1627e5789e26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Store  Weekly_Sales  Holiday_Flag  Temperature   Fuel_Price  \\\n",
              "count  6435.000000  6.435000e+03   6435.000000  6435.000000  6435.000000   \n",
              "mean     23.000000  1.046965e+06      0.069930    60.663782     3.358607   \n",
              "std      12.988182  5.643666e+05      0.255049    18.444933     0.459020   \n",
              "min       1.000000  2.099862e+05      0.000000    -2.060000     2.472000   \n",
              "25%      12.000000  5.533501e+05      0.000000    47.460000     2.933000   \n",
              "50%      23.000000  9.607460e+05      0.000000    62.670000     3.445000   \n",
              "75%      34.000000  1.420159e+06      0.000000    74.940000     3.735000   \n",
              "max      45.000000  3.818686e+06      1.000000   100.140000     4.468000   \n",
              "\n",
              "               CPI  Unemployment  weekday        month         year  \n",
              "count  6435.000000   6435.000000   6435.0  6435.000000  6435.000000  \n",
              "mean    171.578394      7.999151      4.0     6.447552  2010.965035  \n",
              "std      39.356712      1.875885      0.0     3.238308     0.797019  \n",
              "min     126.064000      3.879000      4.0     1.000000  2010.000000  \n",
              "25%     131.735000      6.891000      4.0     4.000000  2010.000000  \n",
              "50%     182.616521      7.874000      4.0     6.000000  2011.000000  \n",
              "75%     212.743293      8.622000      4.0     9.000000  2012.000000  \n",
              "max     227.232807     14.313000      4.0    12.000000  2012.000000  "
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</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>weekday</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6.435000e+03</td>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.0</td>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.046965e+06</td>\n",
              "      <td>0.069930</td>\n",
              "      <td>60.663782</td>\n",
              "      <td>3.358607</td>\n",
              "      <td>171.578394</td>\n",
              "      <td>7.999151</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.447552</td>\n",
              "      <td>2010.965035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.988182</td>\n",
              "      <td>5.643666e+05</td>\n",
              "      <td>0.255049</td>\n",
              "      <td>18.444933</td>\n",
              "      <td>0.459020</td>\n",
              "      <td>39.356712</td>\n",
              "      <td>1.875885</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.238308</td>\n",
              "      <td>0.797019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.099862e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.060000</td>\n",
              "      <td>2.472000</td>\n",
              "      <td>126.064000</td>\n",
              "      <td>3.879000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>5.533501e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>47.460000</td>\n",
              "      <td>2.933000</td>\n",
              "      <td>131.735000</td>\n",
              "      <td>6.891000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2010.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>9.607460e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>62.670000</td>\n",
              "      <td>3.445000</td>\n",
              "      <td>182.616521</td>\n",
              "      <td>7.874000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2011.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.420159e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>74.940000</td>\n",
              "      <td>3.735000</td>\n",
              "      <td>212.743293</td>\n",
              "      <td>8.622000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2012.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>3.818686e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100.140000</td>\n",
              "      <td>4.468000</td>\n",
              "      <td>227.232807</td>\n",
              "      <td>14.313000</td>\n",
              "      <td>4.0</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2012.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-03d46cd7-13ab-4d2c-bca6-82d6e5360bc6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45afe79a-3b26-407b-a171-ae17d3356c18 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2267.530836160574,\n        \"min\": 1.0,\n        \"max\": 6435.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6435.0,\n          23.0,\n          34.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1200219.5149406516,\n        \"min\": 6435.0,\n        \"max\": 3818686.45,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1046964.8775617715,\n          960746.04,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Flag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2275.0491726398463,\n        \"min\": 0.0,\n        \"max\": 6435.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.06993006993006994,\n          1.0,\n          0.25504894436982795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2257.0439834306026,\n        \"min\": -2.06,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          60.66378243978244,\n          62.67,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2274.0622491841236,\n        \"min\": 0.4590197071928525,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.358606837606838,\n          3.445,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2220.778606683361,\n        \"min\": 39.35671229566418,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          171.57839384878014,\n          182.6165205,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272.5201710166866,\n        \"min\": 1.8758847818627977,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.99915104895105,\n          7.874,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2273.9043162115936,\n        \"min\": 0.0,\n        \"max\": 6435.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6435.0,\n          4.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2273.0131854970714,\n        \"min\": 1.0,\n        \"max\": 6435.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.4475524475524475,\n          6.0,\n          6435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1808.09351519816,\n        \"min\": 0.7970193216334346,\n        \"max\": 6435.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6435.0,\n          2010.9650349650349,\n          2012.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnX-ic5kuGAN",
        "outputId": "e979580e-3ecf-46e4-d94d-663910134470"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weekday            1\n",
              "Holiday_Flag       2\n",
              "year               3\n",
              "month             12\n",
              "Store             45\n",
              "Unemployment     349\n",
              "Fuel_Price       892\n",
              "CPI             2145\n",
              "Temperature     3528\n",
              "Weekly_Sales    6435\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "data.nunique().sort_values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NcPvCwbas31m",
        "outputId": "defe2497-39b3-411f-8ad4-639d124af617"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Store           0\n",
              "Weekly_Sales    0\n",
              "Holiday_Flag    0\n",
              "Temperature     0\n",
              "Fuel_Price      0\n",
              "CPI             0\n",
              "Unemployment    0\n",
              "weekday         0\n",
              "month           0\n",
              "year            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CmU73OctHE2"
      },
      "source": [
        "[texte du lien](https://)Dataset doesn't have null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m54gPHg37CdZ",
        "outputId": "f2b107b8-baec-44f2-86e3-db4c55d023eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1mInference:\u001b[0m The Datset has 4 numerical & 5 categorical features.\n"
          ]
        }
      ],
      "source": [
        "#Checking number of unique rows in each feature\n",
        "\n",
        "nu = data[features].nunique().sort_values()\n",
        "nf = []; cf = []; nnf = 0; ncf = 0; #numerical & categorical features\n",
        "\n",
        "for i in range(data[features].shape[1]):\n",
        "    if nu.values[i]<=45:cf.append(nu.index[i])\n",
        "    else: nf.append(nu.index[i])\n",
        "\n",
        "print('\\n\\033[1mInference:\\033[0m The Datset has {} numerical & {} categorical features.'.format(len(nf),len(cf)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cClSimRKtKEN"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "S0tQx6EitLYQ",
        "outputId": "99d929b9-f3b3-4166-a769-7e5e6302c6d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Store  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
              "0      1    1643690.90             0        42.31       2.572  211.096358   \n",
              "1      1    1641957.44             1        38.51       2.548  211.242170   \n",
              "2      1    1611968.17             0        39.93       2.514  211.289143   \n",
              "\n",
              "   Unemployment  weekday  month  year  \n",
              "0         8.106        4      2  2010  \n",
              "1         8.106        4      2  2010  \n",
              "2         8.106        4      2  2010  "
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6435,\n  \"fields\": [\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 1,\n        \"max\": 45,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          40,\n          26,\n          27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weekly_Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 564366.6220536975,\n        \"min\": 209986.25,\n        \"max\": 3818686.45,\n        \"num_unique_values\": 6435,\n        \"samples\": [\n          1138800.32,\n          1304850.67,\n          1769296.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Holiday_Flag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.44493287581158,\n        \"min\": -2.06,\n        \"max\": 100.14,\n        \"num_unique_values\": 3528,\n        \"samples\": [\n          51.13,\n          98.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuel_Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4590197071928525,\n        \"min\": 2.472,\n        \"max\": 4.468,\n        \"num_unique_values\": 892,\n        \"samples\": [\n          2.84,\n          3.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CPI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.35671229566418,\n        \"min\": 126.064,\n        \"max\": 227.2328068,\n        \"num_unique_values\": 2145,\n        \"samples\": [\n          184.613419,\n          214.1083654\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unemployment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8758847818627977,\n        \"min\": 3.879,\n        \"max\": 14.313,\n        \"num_unique_values\": 349,\n        \"samples\": [\n          8.185,\n          7.804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekday\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yc-8TwKMtRUP"
      },
      "source": [
        "we have:\n",
        "\n",
        "**Numerical columns:** Weekly_sales, temperature, fuel_price, cpi, unemployment\n",
        "\n",
        "**Categorical columns:** Holiday_flag, Weekday, month, year\n",
        "\n",
        "Now plotted the count plot to get the distribution or frequency of the columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TN9IAbnvudH7"
      },
      "source": [
        "# <center> 2. Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "J-zBstHxufee",
        "outputId": "a3c4118a-fccd-4110-f445-7178385fb352"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Year Count plot')"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(2, 2, figsize=(16, 8))\n",
        "\n",
        "#axes[0,0].set_title('Holiday Count plot')\n",
        "sns.countplot(x='Holiday_Flag', data=data, ax= axes[0,0])\n",
        "axes[0, 0].set_title('Holiday Count plot')\n",
        "\n",
        "#axes[0,1].set_title('Weekday Count plot')\n",
        "sns.countplot(x='weekday', data=data, ax= axes[0,1]);\n",
        "axes[0, 1].set_title('Weekday Count plot')\n",
        "\n",
        "#axes[1,0].set_title('month Count plot')\n",
        "sns.countplot(x='month', data=data, ax= axes[1,0]);\n",
        "axes[1, 0].set_title('Month Count plot')\n",
        "\n",
        "#axes[1,1].set_title('year Count plot')\n",
        "sns.countplot(x='year', data=data, ax= axes[1,1]);\n",
        "axes[1, 1].set_title('Year Count plot')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8AGp1x6vndR"
      },
      "source": [
        "myyyyyy remarque :    \n",
        "*   Dans holiday flag, la plupart du temps, il n’y a pas de congé dans cette semaine.\n",
        "*   Dans les colonnes de la wekly, les observations sont principalement liées au jour 4\n",
        "*   La plupart des observations dans les données datent du mois d’avril.\n",
        "*   La majeure partie de l’observation dans les données est de l’année 2011"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6vdSWpcyC7M"
      },
      "source": [
        "To get the idea of how many observations are there in dataset crossponding to each store, I again plot a count plot.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "ONIZ1o8eyEId",
        "outputId": "e74fa66b-3e80-4dd1-c321-1e4cd9ffebc3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize= (18,8))\n",
        "sns.countplot(x= 'Store', data= data);\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlSkAN5D3WWr"
      },
      "source": [
        "Pour analyser la distribution des données, j’ai tracé l’histogramme et le diagramme en boîtes pour Température, Chômage, Fuel_Price, CPI.\n",
        "\n",
        "> Ajouter une citation\n",
        "\n",
        "> Ajouter une citation\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "IR4iH6x927pW",
        "outputId": "67c7231d-7994-4e36-f4bb-7bb74a3fd67c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1600 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, axes = plt.subplots(4, 2, figsize=(16, 16))\n",
        "# axes[0,0].set_title('Temperature')\n",
        "sns.histplot(x= 'Temperature', data= data, ax= axes[0,0])\n",
        "\n",
        "sns.boxplot(x= 'Temperature', data= data, ax= axes[0,1])\n",
        "\n",
        "# axes[1,0].set_title('Unemployment')\n",
        "sns.histplot(x= 'Unemployment', data= data, ax= axes[1,0])\n",
        "\n",
        "sns.boxplot(x= 'Unemployment', data= data, ax= axes[1,1])\n",
        "\n",
        "# axes[2,0].set_title('Fuel_Price')\n",
        "sns.histplot(x= 'Fuel_Price', data= data, ax= axes[2,0])\n",
        "\n",
        "sns.boxplot(x = 'Fuel_Price', data= data, ax= axes[2,1])\n",
        "\n",
        "# axes[3,0].set_title('CPI')\n",
        "sns.histplot(x= 'CPI', data= data, ax= axes[3,0])\n",
        "\n",
        "sns.boxplot(x= 'CPI', data= data, ax= axes[3,1]);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlLGCL4c5UWG"
      },
      "source": [
        "# <center> 3. Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dpd8jE4i5k-A"
      },
      "source": [
        "**remove** duplicate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "oDKCcP0a4wME"
      },
      "outputs": [],
      "source": [
        "data.drop_duplicates(inplace=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJQLhcpY51Kj"
      },
      "source": [
        "Check for empty elements\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54QVN3WA5xhw",
        "outputId": "0ff94bfd-2bd7-4801-980d-314cb6554234"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              Total Null Values  Percentage\n",
            "Store                         0         0.0\n",
            "Weekly_Sales                  0         0.0\n",
            "Holiday_Flag                  0         0.0\n",
            "Temperature                   0         0.0\n",
            "Fuel_Price                    0         0.0\n",
            "CPI                           0         0.0\n",
            "Unemployment                  0         0.0\n",
            "weekday                       0         0.0\n",
            "month                         0         0.0\n",
            "year                          0         0.0\n"
          ]
        }
      ],
      "source": [
        "\n",
        "nvc = pd.DataFrame(data.isnull().sum().sort_values(), columns=['Total Null Values'])\n",
        "nvc['Percentage'] = round(nvc['Total Null Values']/data.shape[0],3)*100\n",
        "print(nvc)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-et4kppqBJgm"
      },
      "source": [
        "Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03hVPGte9X_3",
        "outputId": "79b76452-a949-44c2-c19d-d6713460ea5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mOne-Hot Encoding on features:\u001b[0m\n",
            "Holiday_Flag\n",
            "\n",
            "\u001b[1mDummy Encoding on features:\u001b[0m\n",
            "year\n",
            "month\n",
            "Store\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6435, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "#Converting categorical Columns to Numeric\n",
        "\n",
        "df3 = data.copy()\n",
        "\n",
        "ecc = nvc[nvc['Percentage']!=0].index.values\n",
        "fcc = [i for i in cf if i not in ecc]\n",
        "#One-Hot Binay Encoding\n",
        "oh=True\n",
        "dm=True\n",
        "for i in fcc:\n",
        "    #print(i)\n",
        "    if df3[i].nunique()==2:\n",
        "        if oh==True: print(\"\\033[1mOne-Hot Encoding on features:\\033[0m\")\n",
        "        print(i);oh=False\n",
        "        df3[i]=pd.get_dummies(df3[i], drop_first=True, prefix=str(i))\n",
        "    if (df3[i].nunique()>2):\n",
        "        if dm==True: print(\"\\n\\033[1mDummy Encoding on features:\\033[0m\")\n",
        "        print(i);dm=False\n",
        "        df3 = pd.concat([df3.drop([i], axis=1), pd.DataFrame(pd.get_dummies(df3[i], drop_first=True, prefix=str(i)))],axis=1)\n",
        "\n",
        "df3.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylj8wB9A-b8t"
      },
      "source": [
        "One-Hot Encoding : Utilisé pour les colonnes avec exactement 2 valeurs uniques. Il crée une nouvelle colonne binaire.\n",
        "Dummy Encoding : Utilisé pour les colonnes avec plus de 2 valeurs uniques. Il crée plusieurs colonnes binaires, une pour chaque catégorie, sauf une pour éviter la multi-colinéarité.\n",
        "Avantages : Ces techniques permettent d'intégrer des variables catégorielles dans des modèles de machine learning qui ne peuvent travailler qu'avec des données numériques."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDbwwg_3AF1V",
        "outputId": "dcab10a8-745d-44a3-a2b1-a5bea4f28e1e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6435, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "df3.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "WMx0mtjcALZn",
        "outputId": "60df9c34-4d7e-4c5d-9dc8-a239f7ea719e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
              "0    1643690.90         False        42.31       2.572  211.096358   \n",
              "1    1641957.44          True        38.51       2.548  211.242170   \n",
              "2    1611968.17         False        39.93       2.514  211.289143   \n",
              "3    1409727.59         False        46.63       2.561  211.319643   \n",
              "4    1554806.68         False        46.50       2.625  211.350143   \n",
              "\n",
              "   Unemployment  weekday  year_2011  year_2012  month_2  ...  Store_36  \\\n",
              "0         8.106        4      False      False     True  ...     False   \n",
              "1         8.106        4      False      False     True  ...     False   \n",
              "2         8.106        4      False      False     True  ...     False   \n",
              "3         8.106        4      False      False     True  ...     False   \n",
              "4         8.106        4      False      False    False  ...     False   \n",
              "\n",
              "   Store_37  Store_38  Store_39  Store_40  Store_41  Store_42  Store_43  \\\n",
              "0     False     False     False     False     False     False     False   \n",
              "1     False     False     False     False     False     False     False   \n",
              "2     False     False     False     False     False     False     False   \n",
              "3     False     False     False     False     False     False     False   \n",
              "4     False     False     False     False     False     False     False   \n",
              "\n",
              "   Store_44  Store_45  \n",
              "0     False     False  \n",
              "1     False     False  \n",
              "2     False     False  \n",
              "3     False     False  \n",
              "4     False     False  \n",
              "\n",
              "[5 rows x 64 columns]"
            ],
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              "  <div id=\"df-8696255c-d850-4a1d-9d93-f9f898d66ccc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>Holiday_Flag</th>\n",
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              "      <th>year_2011</th>\n",
              "      <th>year_2012</th>\n",
              "      <th>month_2</th>\n",
              "      <th>...</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1643690.90</td>\n",
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              "      <td>42.31</td>\n",
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              "      <th>1</th>\n",
              "      <td>1641957.44</td>\n",
              "      <td>True</td>\n",
              "      <td>38.51</td>\n",
              "      <td>2.548</td>\n",
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              "      <td>8.106</td>\n",
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              "      <td>1611968.17</td>\n",
              "      <td>False</td>\n",
              "      <td>39.93</td>\n",
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              "      <th>3</th>\n",
              "      <td>1409727.59</td>\n",
              "      <td>False</td>\n",
              "      <td>46.63</td>\n",
              "      <td>2.561</td>\n",
              "      <td>211.319643</td>\n",
              "      <td>8.106</td>\n",
              "      <td>4</td>\n",
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              "      <th>4</th>\n",
              "      <td>1554806.68</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 64 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8696255c-d850-4a1d-9d93-f9f898d66ccc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8696255c-d850-4a1d-9d93-f9f898d66ccc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8696255c-d850-4a1d-9d93-f9f898d66ccc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-81015647-265d-4a2f-ab0c-c20fc1915307 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df3"
            }
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "df3.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyJajY_m6MDX"
      },
      "source": [
        "3lach 7ayadna les valeur abberrantes 🇰\n",
        "\n",
        "\n",
        "*   Les valeurs aberrantes peuvent fausser les résultats des analyses statistiques,\n",
        "*   es valeurs aberrantes peuvent perturber les algorithmes et conduire à des modèles moins précis.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "qXt5NgH96chY",
        "outputId": "6c300ab8-c125-4db2-f0b4-ea14146f468c"
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            "text/plain": [
              "   Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n",
              "0    1643690.90         False        42.31       2.572  211.096358   \n",
              "1    1641957.44          True        38.51       2.548  211.242170   \n",
              "2    1611968.17         False        39.93       2.514  211.289143   \n",
              "3    1409727.59         False        46.63       2.561  211.319643   \n",
              "4    1554806.68         False        46.50       2.625  211.350143   \n",
              "\n",
              "   Unemployment  weekday  year_2011  year_2012  month_2  ...  Store_36  \\\n",
              "0         8.106        4      False      False     True  ...     False   \n",
              "1         8.106        4      False      False     True  ...     False   \n",
              "2         8.106        4      False      False     True  ...     False   \n",
              "3         8.106        4      False      False     True  ...     False   \n",
              "4         8.106        4      False      False    False  ...     False   \n",
              "\n",
              "   Store_37  Store_38  Store_39  Store_40  Store_41  Store_42  Store_43  \\\n",
              "0     False     False     False     False     False     False     False   \n",
              "1     False     False     False     False     False     False     False   \n",
              "2     False     False     False     False     False     False     False   \n",
              "3     False     False     False     False     False     False     False   \n",
              "4     False     False     False     False     False     False     False   \n",
              "\n",
              "   Store_44  Store_45  \n",
              "0     False     False  \n",
              "1     False     False  \n",
              "2     False     False  \n",
              "3     False     False  \n",
              "4     False     False  \n",
              "\n",
              "[5 rows x 64 columns]"
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              "      <th>0</th>\n",
              "      <td>1643690.90</td>\n",
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              "      <th>1</th>\n",
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              "      <td>38.51</td>\n",
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              "      <th>2</th>\n",
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              "      <td>False</td>\n",
              "      <td>39.93</td>\n",
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              "      <td>2.561</td>\n",
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              "<p>5 rows × 64 columns</p>\n",
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              "                                                    [key], {});\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
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          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1mInference:\u001b[0m\n",
            "Before removal of outliers, The dataset had 6435 samples.\n",
            "After removal of outliers, The dataset now has 5953 samples.\n"
          ]
        }
      ],
      "source": [
        "#Removal of outlier:\n",
        "\n",
        "data1 = df3.copy()\n",
        "\n",
        "#features1 = [i for i in features if i not in ['CHAS','RAD']]\n",
        "features1 = nf\n",
        "\n",
        "for i in features1:\n",
        "    Q1 = data1[i].quantile(0.25)\n",
        "    Q3 = data1[i].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    data1 = data1[data1[i] <= (Q3+(1.5*IQR))]\n",
        "    data1 = data1[data1[i] >= (Q1-(1.5*IQR))]\n",
        "    data1 = data1.reset_index(drop=True)\n",
        "display(data1.head())\n",
        "print('\\n\\033[1mInference:\\033[0m\\nBefore removal of outliers, The dataset had {} samples.'.format(data.shape[0]))\n",
        "print('After removal of outliers, The dataset now has {} samples.'.format(data1.shape[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "n5TaqRj-7h0U",
        "outputId": "3dd5cac3-88bb-4406-d6be-c283bb59185a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1mInference:\u001b[0m After the cleanup process, 482 samples were dropped, while retaining 7.49% of the data6.\n"
          ]
        }
      ],
      "source": [
        "#Final data6set size after performing Preprocessing\n",
        "\n",
        "data6 = data1.copy()\n",
        "data6.columns=[i.replace('-','_') for i in data6.columns]\n",
        "\n",
        "plt.title('Final data6set')\n",
        "plt.pie([data6.shape[0], original_df.shape[0]-data6.shape[0]], radius = 1, labels=['Retained','Dropped'], counterclock=False,\n",
        "        autopct='%1.1f%%', pctdistance=0.9, explode=[0,0], shadow=True)\n",
        "plt.pie([data6.shape[0]], labels=['100%'], labeldistance=-0, radius=0.78)\n",
        "plt.show()\n",
        "\n",
        "print(f'\\n\\033[1mInference:\\033[0m After the cleanup process, {original_df.shape[0]-data6.shape[0]} samples were dropped, \\\n",
        "while retaining {round(100 - (data6.shape[0]*100/(original_df.shape[0])),2)}% of the data6.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52lgct8f8CcS"
      },
      "source": [
        "# <center> 4. Data Manipulation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96ImPEhTCHSL"
      },
      "source": [
        "**Standardization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "PxWfHmo0BpG9"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "scaler.fit(data6)\n",
        "scaled_df = scaler.transform(data6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "ScnCx1aqFToA"
      },
      "outputs": [],
      "source": [
        "scaled_df = pd.DataFrame(data = scaled_df, columns = data6.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "ds4epDAbB5N6",
        "outputId": "5e4e4954-13d3-4506-ea88-45895a0e3bc9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price       CPI  \\\n",
              "0      0.397291           0.0     0.388689    0.050100  0.840500   \n",
              "1      0.396811           1.0     0.348520    0.038076  0.841941   \n",
              "2      0.388501           0.0     0.363531    0.021042  0.842405   \n",
              "\n",
              "   Unemployment  weekday  year_2011  year_2012  month_2  ...  Store_36  \\\n",
              "0      0.573889      0.0        0.0        0.0      1.0  ...       0.0   \n",
              "1      0.573889      0.0        0.0        0.0      1.0  ...       0.0   \n",
              "2      0.573889      0.0        0.0        0.0      1.0  ...       0.0   \n",
              "\n",
              "   Store_37  Store_38  Store_39  Store_40  Store_41  Store_42  Store_43  \\\n",
              "0       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "1       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "2       0.0       0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "\n",
              "   Store_44  Store_45  \n",
              "0       0.0       0.0  \n",
              "1       0.0       0.0  \n",
              "2       0.0       0.0  \n",
              "\n",
              "[3 rows x 64 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0743c845-6e7a-41ed-a334-8901506254af\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>weekday</th>\n",
              "      <th>year_2011</th>\n",
              "      <th>year_2012</th>\n",
              "      <th>month_2</th>\n",
              "      <th>...</th>\n",
              "      <th>Store_36</th>\n",
              "      <th>Store_37</th>\n",
              "      <th>Store_38</th>\n",
              "      <th>Store_39</th>\n",
              "      <th>Store_40</th>\n",
              "      <th>Store_41</th>\n",
              "      <th>Store_42</th>\n",
              "      <th>Store_43</th>\n",
              "      <th>Store_44</th>\n",
              "      <th>Store_45</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.397291</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.388689</td>\n",
              "      <td>0.050100</td>\n",
              "      <td>0.840500</td>\n",
              "      <td>0.573889</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.396811</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.348520</td>\n",
              "      <td>0.038076</td>\n",
              "      <td>0.841941</td>\n",
              "      <td>0.573889</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.388501</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.363531</td>\n",
              "      <td>0.021042</td>\n",
              "      <td>0.842405</td>\n",
              "      <td>0.573889</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 64 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0743c845-6e7a-41ed-a334-8901506254af')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0743c845-6e7a-41ed-a334-8901506254af button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0743c845-6e7a-41ed-a334-8901506254af');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e76a77ec-6b82-467c-aca1-f74f471a804a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e76a77ec-6b82-467c-aca1-f74f471a804a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e76a77ec-6b82-467c-aca1-f74f471a804a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "scaled_df"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "scaled_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "7sXV_3_bFbvo"
      },
      "outputs": [],
      "source": [
        "scaled_df.to_csv('scaled_df.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2dUu_yLCGCon"
      },
      "source": [
        "**Train-Test-Split**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0r7dx83HTtz"
      },
      "source": [
        "Split the dataset into the two part:\n",
        "1. Training dataset\n",
        "2. Testing dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zQo_2NeHisK"
      },
      "source": [
        "mhm hnaya m7inah flawal bach nkhdmoh ka target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "h__9E6-JHgRw"
      },
      "outputs": [],
      "source": [
        "X = scaled_df.drop(target, axis=1)\n",
        "y = scaled_df[target]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRb89qC7Hvy9"
      },
      "source": [
        " divising the dataset into the train and the test parts and each part has input feature and target features\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "C9I0nRGFHtxo"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)\n",
        "X_train.reset_index(drop=True,inplace=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwO47ijlH29w",
        "outputId": "08bc8afc-7bf4-45ac-f20c-3aa4e7f6672d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4762, 63), (1191, 63), (4762,), (1191,))"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edgVhkOAItdZ"
      },
      "source": [
        "# <center> 5. Feature Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwZ6_0NuJZ8c"
      },
      "source": [
        "checking the correlation\n",
        "**[texte en gras](https://)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "kk2OHwebJYjU",
        "outputId": "b9578ebc-9577-43b1-e335-145e3865322d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                       \u001b[1mCorrelation Matrix                                       \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "print('\\033[1mCorrelation Matrix'.center(100))\n",
        "plt.figure(figsize=[25,20])\n",
        "sns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, center=0) #cmap='BuGn'\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **RFE**"
      ],
      "metadata": {
        "id": "kouR_fSzdKMu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Out of all the 78 features all are not important and we have to choose\n",
        "\n",
        "the important feature out of all the features\n",
        "\n",
        "\n",
        "usin RFE"
      ],
      "metadata": {
        "id": "oUPdmxb8dXdS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "Trr=[]; Tss=[]; n=3\n",
        "order=['ord-'+str(i) for i in range(2,n)]\n",
        "Trd = pd.DataFrame(np.zeros((10,n-2)), columns=order)\n",
        "Tsd = pd.DataFrame(np.zeros((10,n-2)), columns=order)\n",
        "\n",
        "m=data.shape[1]-2\n",
        "for i in range(m):\n",
        "    lm = LinearRegression()\n",
        "    rfe = RFE(lm, n_features_to_select=X_train.shape[1]-i)\n",
        "    rfe = rfe.fit(X_train, y_train)\n",
        "    LR = LinearRegression()\n",
        "    LR.fit(X_train.loc[:,rfe.support_], y_train)\n",
        "    pred1 = LR.predict(X_train.loc[:,rfe.support_])\n",
        "    pred2 = LR.predict(X_test.loc[:,rfe.support_])\n",
        "    Trr.append(np.sqrt(mean_squared_error(y_train, pred1)))\n",
        "    Tss.append(np.sqrt(mean_squared_error(y_test, pred2)))"
      ],
      "metadata": {
        "id": "d7tDzFMwbr63"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(Trr, label= 'Train RMSE')\n",
        "plt.plot(Tss, label= 'Test RMSE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "FXhpV2xirdCG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "outputId": "a3dc2022-ac07-4c2b-ef4a-c94bd67c3ba7"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "lm = LinearRegression()\n",
        "rfe = RFE(lm,n_features_to_select=X_train.shape[1]-9)\n",
        "rfe = rfe.fit(X_train, y_train)\n",
        "LR = LinearRegression()\n",
        "LR.fit(X_train.loc[:,rfe.support_], y_train)\n",
        "pred1 = LR.predict(X_train.loc[:,rfe.support_])\n",
        "pred2 = LR.predict(X_test.loc[:,rfe.support_])\n",
        "print(\"MSE train\",np.sqrt(mean_squared_error(y_train, pred1)))\n",
        "print(\"MSE test\",np.sqrt(mean_squared_error(y_test, pred2)))\n",
        "print(\"r2_score train - {}\".format(r2_score(y_train, pred1)))\n",
        "print(\"r2_score test - {}\".format(r2_score(y_test, pred2)))"
      ],
      "metadata": {
        "id": "bFCgZu5ac2eB",
        "outputId": "84be9b20-67bd-4f79-ba9e-3855b91aaeb8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE train 0.038902035275263445\n",
            "MSE test 0.04325112297647686\n",
            "r2_score train - 0.9389186865616108\n",
            "r2_score test - 0.929629683567451\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now** Removing the 10 features and create the New training and test dataset"
      ],
      "metadata": {
        "id": "9DfckPq4dAX8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.loc[:,rfe.support_]\n",
        "X_test = X_test.loc[:,rfe.support_]"
      ],
      "metadata": {
        "id": "kKtbIIvSdCdE"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now onwards I am going to use various models\n"
      ],
      "metadata": {
        "id": "fhCXKb5UdFkF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <center> 6. Predictive Modelling"
      ],
      "metadata": {
        "id": "SVz57Us8dg49"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Objective:\n"
      ],
      "metadata": {
        "id": "hwCnnyocd3-2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# REGRESSION LINEAR\n",
        "\n",
        "\n",
        "> Ajouter une citation\n",
        "\n"
      ],
      "metadata": {
        "id": "xwOlVieGyshS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr =LinearRegression()\n",
        "lr.fit(X_train, y_train)\n",
        "pred1 = lr.predict(X_train)\n",
        "pred2 = lr.predict(X_test)\n",
        "\n",
        "print(\"Root Mean Squared Error train {}\".format(np.mean(mean_squared_error(y_train, pred1))))\n",
        "print(\"Root Mean Squared Error test {}\".format(np.sqrt(mean_squared_error(y_test, pred2))))\n",
        "print(\"r2_score train {}\".format(r2_score(y_train, pred1)))\n",
        "print(\"r2_score test {}\".format(r2_score(y_test, pred2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b8w3sBgnIc0",
        "outputId": "527abccf-428a-4c58-a978-e0c5c4537ea2"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error train 0.0015133683485578412\n",
            "Root Mean Squared Error test 0.04325112297647686\n",
            "r2_score train 0.9389186865616108\n",
            "r2_score test 0.929629683567451\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Définition de la fonction Evaluate\n",
        "Model_Evaluation_Comparison_Matrix = pd.DataFrame(np.zeros([1,8]), columns=['Train-R2','Test-R2','Train-RSS','Test-RSS',\n",
        "                                                                            'Train-MSE','Test-MSE','Train-RMSE','Test-RMSE'])\n",
        "rc = np.random.choice(X_train.loc[:, X_train.nunique() >= 50].columns.values, 2, replace=False)\n",
        "\n",
        "print('\\n\\n{}Training Set Metrics{}'.format('-'*20, '-'*20))\n",
        "print('\\nR2-Score on Training set --->', round(r2_score(y_train, pred1), 20))\n",
        "print('Residual Sum of Squares (RSS) on Training set  --->', round(np.sum(np.square(y_train - pred1)), 20))\n",
        "print('Mean Squared Error (MSE) on Training set       --->', round(mean_squared_error(y_train, pred1), 20))\n",
        "print('Root Mean Squared Error (RMSE) on Training set --->', round(np.sqrt(mean_squared_error(y_train, pred1)), 20))\n",
        "\n",
        "print('\\n{}Testing Set Metrics{}'.format('-'*20, '-'*20))\n",
        "print('\\nR2-Score on Testing set --->', round(r2_score(y_test, pred2), 20))\n",
        "print('Residual Sum of Squares (RSS) on Training set  --->', round(np.sum(np.square(y_test - pred2)), 20))\n",
        "print('Mean Squared Error (MSE) on Training set       --->', round(mean_squared_error(y_test, pred2), 20))\n",
        "print('Root Mean Squared Error (RMSE) on Training set --->', round(np.sqrt(mean_squared_error(y_test, pred2)), 20))\n",
        "print('\\n{}Residual Plots{}'.format('-'*20, '-'*20))\n",
        "\n",
        "plt.figure(figsize=[15, 4])\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.distplot((y_train - pred1))\n",
        "plt.title('Error Terms')\n",
        "plt.xlabel('Errors')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.scatter(y_train, pred1)\n",
        "plt.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'r--')\n",
        "plt.title('Test vs Prediction')\n",
        "plt.xlabel('y_test')\n",
        "plt.ylabel('y_pred')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "on2CcAuandPP",
        "outputId": "9c2b72fe-412a-409f-bb63-12655550a2c5"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "--------------------Training Set Metrics--------------------\n",
            "\n",
            "R2-Score on Training set ---> 0.9380889849901288\n",
            "Residual Sum of Squares (RSS) on Training set  ---> 7.304552161864375\n",
            "Mean Squared Error (MSE) on Training set       ---> 0.001533925275486009\n",
            "Root Mean Squared Error (RMSE) on Training set ---> 0.03916535810491217\n",
            "\n",
            "--------------------Testing Set Metrics--------------------\n",
            "\n",
            "R2-Score on Testing set ---> 0.928441811042915\n",
            "Residual Sum of Squares (RSS) on Training set  ---> 2.2655642040850843\n",
            "Mean Squared Error (MSE) on Training set       ---> 0.001902236947174714\n",
            "Root Mean Squared Error (RMSE) on Training set ---> 0.04361464143122942\n",
            "\n",
            "--------------------Residual Plots--------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "3l1m9UjhnEst"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RIDGE REGRESSION**"
      ],
      "metadata": {
        "id": "85wzuFGZ5zCf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a Ridge Regression model\n",
        "\n",
        "RLR = Ridge().fit(X_train,y_train)\n",
        "pred1 = RLR.predict(X_train)\n",
        "pred2 = RLR.predict(X_test)\n",
        "plt.figure(figsize=[15, 4])\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.distplot((y_train - pred1))\n",
        "plt.title('Error Terms')\n",
        "plt.xlabel('Errors')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.scatter(y_train, pred1)\n",
        "plt.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'r--')\n",
        "plt.title('Test vs Prediction')\n",
        "plt.xlabel('y_test')\n",
        "plt.ylabel('y_pred')\n",
        "plt.show()\n",
        "print('{}{}\\033[1m Evaluating Ridge Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
        "#print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
        "print('The Intercept of the Regresion Model was found to be ',lr.intercept_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "ToRBM51Ox1nC",
        "outputId": "c99d1ea1-11e0-4818-deb8-8cfcad41d978"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<<<-----------------------------------\u001b[1m Evaluating Ridge Regression Model \u001b[0m----------------------------------->>>\n",
            "\n",
            "The Intercept of the Regresion Model was found to be  0.2852704505143167\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LASSO REGRESSION **"
      ],
      "metadata": {
        "id": "0_r2eJpb7J0-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "lr1 = Lasso()\n",
        "lr1.fit(X_train, y_train)\n",
        "predlr1 = lr1.predict(X_train)\n",
        "predlr2 = lr1.predict(X_test)\n",
        "\n",
        "print('{}{}\\033[1m Evaluating Lasso Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
        "#print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
        "print('The Intercept of the Regresion Model was found to be ',lr.intercept_)\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.distplot((y_train - pred1))\n",
        "plt.title('Error Terms')\n",
        "plt.xlabel('Errors')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.scatter(y_train, pred1)\n",
        "plt.plot([y_train.min(), y_train.max()], [y_train.min(), y_train.max()], 'r--')\n",
        "plt.title('Test vs Prediction')\n",
        "plt.xlabel('y_test')\n",
        "plt.ylabel('y_pred')\n",
        "plt.show()\n",
        "print('{}{}\\033[1m Evaluating Ridge Regression Model \\033[0m{}{}\\n'.format('<'*3,'-'*35 ,'-'*35,'>'*3))\n",
        "#print('The Coeffecient of the Regresion Model was found to be ',MLR.coef_)\n",
        "print('The Intercept of the Regresion Model was found to be ',lr.intercept_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "DruKj4YR7Mml",
        "outputId": "0cd3447e-146f-452f-a6d0-18f871e7371a"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<<<-----------------------------------\u001b[1m Evaluating Lasso Regression Model \u001b[0m----------------------------------->>>\n",
            "\n",
            "The Intercept of the Regresion Model was found to be  0.2852704505143167\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<<<-----------------------------------\u001b[1m Evaluating Ridge Regression Model \u001b[0m----------------------------------->>>\n",
            "\n",
            "The Intercept of the Regresion Model was found to be  0.2852704505143167\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Polynomial Regression**"
      ],
      "metadata": {
        "id": "ZK70hlWr6MXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Trr = []\n",
        "Tss = []\n",
        "for i in range(2,4):\n",
        "  poly_reg = PolynomialFeatures(degree = i)\n",
        "  pl_X_train = poly_reg.fit_transform(X_train)\n",
        "  pl_X_test = poly_reg.fit_transform(X_test)\n",
        "  lr = LinearRegression()\n",
        "  lr.fit(pl_X_train, y_train)\n",
        "  pred_poly_train = lr.predict(pl_X_train)\n",
        "  Trr.append(np.sqrt(mean_squared_error(y_train, pred_poly_train)))\n",
        "  pred_poly_test = lr.predict(pl_X_test)\n",
        "  Tss.append(np.sqrt(mean_squared_error(y_test, pred_poly_test)))"
      ],
      "metadata": {
        "id": "WhPq9yDY6LX0"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[15,6])\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(range(2,4), Trr, label= 'Training')\n",
        "plt.plot(range(2,4), Tss, label= 'Testing')\n",
        "plt.title('Polynomial Feature on training data')\n",
        "plt.xlabel('Degree')\n",
        "plt.ylabel('RMSE')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "vqsqqQJI6zk3",
        "outputId": "18cca18b-768d-42e0-9298-06f9d80b5a12"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7c0f23d88e50>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poly_reg = PolynomialFeatures(degree = 2)\n",
        "pl_X_train = poly_reg.fit_transform(X_train)\n",
        "pl_X_test = poly_reg.fit_transform(X_test)\n",
        "lr = LinearRegression()\n",
        "lr.fit(pl_X_train, y_train)\n",
        "pred_poly_train = lr.predict(pl_X_train)\n",
        "print(\"r2_score train {}\".format(r2_score(pred_poly_train, y_train)))\n",
        "pred_poly_test = lr.predict(pl_X_test)\n",
        "print(\"r2_score test {}\".format(r2_score(pred_poly_test, y_test)))\n",
        "print(\"Root Mean Squared Error train {}\".format(np.mean(mean_squared_error(y_train, pred_poly_train))))\n",
        "print(\"Root Mean Squared Error test {}\".format(np.sqrt(mean_squared_error(y_test, pred_poly_test))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctqKFOy266tH",
        "outputId": "eaee6e01-345a-4f7a-c2b6-9cceb6619894"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2_score train 0.9549340538522609\n",
            "r2_score test 0.9371797967825095\n",
            "Root Mean Squared Error train 0.0010683654035495735\n",
            "Root Mean Squared Error test 0.03911319889002968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabel = {\n",
        "        'Train R2': [0.9389186865616108, 0.9380889849901288, 0.9380889849901288  ,0.9549340538522609],\n",
        "        'Test R2' : [0.929629683567451, 0.928441811042915, 0.928441811042915, 0.9371797967825095],\n",
        "        'Train RMSE' : [0.038902035275263445, 0.03916535810491217, 0.03916535810491217, 0.0010683654035495735 ],\n",
        "        'Test RMSE' : [0.04325112297647686, 0.04361464143122942, 0.04361464143122942,  0.03911319889002968 ]\n",
        "        }"
      ],
      "metadata": {
        "id": "l8Ugn2ud7CQq"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = pd.DataFrame(tabel)"
      ],
      "metadata": {
        "id": "ybW1yQqL7SVP"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new.index = ['Linear Regression', 'Ridge Regression', 'Lasso Regression',  'Polynomial Regression']"
      ],
      "metadata": {
        "id": "Y8UKlLOg82zg"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "DtSLLTNw9K5z",
        "outputId": "3e58b663-ece3-4c3f-b90e-945b1e21df7d"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Train R2   Test R2  Train RMSE  Test RMSE\n",
              "Linear Regression      0.938919  0.929630    0.038902   0.043251\n",
              "Ridge Regression       0.938089  0.928442    0.039165   0.043615\n",
              "Lasso Regression       0.938089  0.928442    0.039165   0.043615\n",
              "Polynomial Regression  0.954934  0.937180    0.001068   0.039113"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Train R2</th>\n",
              "      <th>Test R2</th>\n",
              "      <th>Train RMSE</th>\n",
              "      <th>Test RMSE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Linear Regression</th>\n",
              "      <td>0.938919</td>\n",
              "      <td>0.929630</td>\n",
              "      <td>0.038902</td>\n",
              "      <td>0.043251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge Regression</th>\n",
              "      <td>0.938089</td>\n",
              "      <td>0.928442</td>\n",
              "      <td>0.039165</td>\n",
              "      <td>0.043615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso Regression</th>\n",
              "      <td>0.938089</td>\n",
              "      <td>0.928442</td>\n",
              "      <td>0.039165</td>\n",
              "      <td>0.043615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Polynomial Regression</th>\n",
              "      <td>0.954934</td>\n",
              "      <td>0.937180</td>\n",
              "      <td>0.001068</td>\n",
              "      <td>0.039113</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_new",
              "summary": "{\n  \"name\": \"df_new\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Train R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00829347880762254,\n        \"min\": 0.9380889849901288,\n        \"max\": 0.9549340538522609,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9389186865616108,\n          0.9380889849901288,\n          0.9549340538522609\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00420843479811685,\n        \"min\": 0.928441811042915,\n        \"max\": 0.9371797967825095,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.929629683567451,\n          0.928441811042915,\n          0.9371797967825095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Train RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01900501460045569,\n        \"min\": 0.0010683654035495735,\n        \"max\": 0.03916535810491217,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.038902035275263445,\n          0.03916535810491217,\n          0.0010683654035495735\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002196828717768274,\n        \"min\": 0.03911319889002968,\n        \"max\": 0.04361464143122942,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.04325112297647686,\n          0.04361464143122942,\n          0.03911319889002968\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "UJav2OqSIs6x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# R2-Scores Comparison for different Regression Models\n",
        "\n",
        "R2 = round(df_new['Test R2'].sort_values(ascending=True),4)\n",
        "plt.hlines(y=R2.index, xmin=0, xmax=R2.values)\n",
        "plt.plot(R2.values, R2.index,'o')\n",
        "plt.title('R2-Scores Comparison for various Regression Models')\n",
        "plt.xlabel('R2-Score')\n",
        "#plt.ylabel('Regression Models')\n",
        "for i, v in enumerate(R2):\n",
        "    plt.text(v+0.02, i-0.05, str(v*100), color='blue')\n",
        "plt.xlim([0,1.1])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "NfPZzdlyItea",
        "outputId": "95bc917a-3c30-4037-addc-b9c7b42f8745"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is clear that plynomial Regression is the Best Model in the dataset, with test accuracy of 93%(approx).\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "SXvao-6D9_1B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "R\n",
        "2\n",
        "  Score :valeur plus élevé indique un meilleur ajustement du modèle aux données.\n",
        "\n"
      ],
      "metadata": {
        "id": "BICMw1KwJxEe"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}