MLProject(BigMart).ipynb

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    "colab": {
      "name": "MLProject(BigMart).ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/istiaksaif/CSE445_BigMart_Sales_Prediction/blob/main/MLProject(BigMart).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW1YCSiLpQLm"
      },
      "source": [
        "Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyznJxywlUjY"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from google.colab import drive"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqpu_IX6UyTp",
        "outputId": "652fa177-5365-454a-8649-d8c3ca32beaa"
      },
      "source": [
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovqLw3lXqkAl"
      },
      "source": [
        "Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJdLNvJaqfJe"
      },
      "source": [
        "#loading dataset from drive (csvfile)\n",
        "bigmart_data = pd.read_csv('/content/drive/MyDrive/CSE445/BigMart Dataset/Train.csv')\n",
        "bigmart_test_data = pd.read_csv('/content/drive/MyDrive/CSE445/BigMart Dataset/Test.csv')\n",
        "submission = pd.read_csv('/content/drive/MyDrive/CSE445/BigMart Dataset/Submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCrOhigorNdx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "a45403be-5c6e-4a47-f36e-9b414c3c6831"
      },
      "source": [
        "bigmart_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\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>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight  ...        Outlet_Type  Item_Outlet_Sales\n",
              "0           FDA15         9.30  ...  Supermarket Type1          3735.1380\n",
              "1           DRC01         5.92  ...  Supermarket Type2           443.4228\n",
              "2           FDN15        17.50  ...  Supermarket Type1          2097.2700\n",
              "3           FDX07        19.20  ...      Grocery Store           732.3800\n",
              "4           NCD19         8.93  ...  Supermarket Type1           994.7052\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPaWk6ycsGPO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eab229c-d4f0-4146-8c92-082c551439c0"
      },
      "source": [
        "#number of Features and points\n",
        "bigmart_data.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8523, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyT1vNSstLWj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb39a322-460f-4c44-8fae-63717a934b3a"
      },
      "source": [
        "#info about datatype\n",
        "bigmart_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8523 entries, 0 to 8522\n",
            "Data columns (total 12 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Item_Identifier            8523 non-null   object \n",
            " 1   Item_Weight                7060 non-null   float64\n",
            " 2   Item_Fat_Content           8523 non-null   object \n",
            " 3   Item_Visibility            8523 non-null   float64\n",
            " 4   Item_Type                  8523 non-null   object \n",
            " 5   Item_MRP                   8523 non-null   float64\n",
            " 6   Outlet_Identifier          8523 non-null   object \n",
            " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
            " 8   Outlet_Size                6113 non-null   object \n",
            " 9   Outlet_Location_Type       8523 non-null   object \n",
            " 10  Outlet_Type                8523 non-null   object \n",
            " 11  Item_Outlet_Sales          8523 non-null   float64\n",
            "dtypes: float64(4), int64(1), object(7)\n",
            "memory usage: 799.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r14b6I4hIzwo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f525d3f-7ae2-4694-8395-2b738f417c1f"
      },
      "source": [
        "#check unique values\n",
        "bigmart_data.apply(lambda x: len(x.unique()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier              1559\n",
              "Item_Weight                   416\n",
              "Item_Fat_Content                5\n",
              "Item_Visibility              7880\n",
              "Item_Type                      16\n",
              "Item_MRP                     5938\n",
              "Outlet_Identifier              10\n",
              "Outlet_Establishment_Year       9\n",
              "Outlet_Size                     4\n",
              "Outlet_Location_Type            3\n",
              "Outlet_Type                     4\n",
              "Item_Outlet_Sales            3493\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4VhbrSJY-js",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "15e91056-afb4-4495-eb13-aeec95c1162e"
      },
      "source": [
        "#static info\n",
        "bigmart_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7060.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "      <td>8523.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.857645</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>140.992782</td>\n",
              "      <td>1997.831867</td>\n",
              "      <td>2181.288914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.643456</td>\n",
              "      <td>0.051598</td>\n",
              "      <td>62.275067</td>\n",
              "      <td>8.371760</td>\n",
              "      <td>1706.499616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.555000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.290000</td>\n",
              "      <td>1985.000000</td>\n",
              "      <td>33.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.773750</td>\n",
              "      <td>0.026989</td>\n",
              "      <td>93.826500</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>834.247400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.053931</td>\n",
              "      <td>143.012800</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>1794.331000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.850000</td>\n",
              "      <td>0.094585</td>\n",
              "      <td>185.643700</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>3101.296400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.350000</td>\n",
              "      <td>0.328391</td>\n",
              "      <td>266.888400</td>\n",
              "      <td>2009.000000</td>\n",
              "      <td>13086.964800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Item_Weight  ...  Item_Outlet_Sales\n",
              "count  7060.000000  ...        8523.000000\n",
              "mean     12.857645  ...        2181.288914\n",
              "std       4.643456  ...        1706.499616\n",
              "min       4.555000  ...          33.290000\n",
              "25%       8.773750  ...         834.247400\n",
              "50%      12.600000  ...        1794.331000\n",
              "75%      16.850000  ...        3101.296400\n",
              "max      21.350000  ...       13086.964800\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJxtH36UuCTa"
      },
      "source": [
        "\n",
        "\n",
        "**Categorical Features**\n",
        "\n",
        "*   Item_Identifier\n",
        "*   Item_Weight\n",
        "*   Item_Fat_Content\n",
        "*   Item_Visibility\n",
        "*   Item_Type\n",
        "*   Item_MRP\n",
        "*   Outlet_Identifier\n",
        "*   Outlet_Establishment_Year\n",
        "*   Outlet_Size\n",
        "*   Outlet_Location_Type\n",
        "*   Outlet_Type\n",
        "*   Item_Outlet_Sales"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRuaUzdsUvWz"
      },
      "source": [
        "#Preprocessing Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGxaz6kMoKmX",
        "outputId": "4e10f32c-322e-4c0e-9b16-fd9f4325635c"
      },
      "source": [
        "bigmart_test_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                   976\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  1606\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSPP2aHPt81V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd88a3f9-86a9-44df-f025-86ae18dc11ac"
      },
      "source": [
        "#missing values\n",
        "bigmart_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                  1463\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2410\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2eg_goEQn0b"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pq-seUJSK1JF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "044920d2-ba72-4544-f3dd-14fd4a3af739"
      },
      "source": [
        "#check categorical attributes\n",
        "cat_column =[]\n",
        "for x in bigmart_data.dtypes.index:\n",
        "  if bigmart_data.dtypes[x] == 'object':\n",
        "    cat_column.append(x)\n",
        "cat_column"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Item_Identifier',\n",
              " 'Item_Fat_Content',\n",
              " 'Item_Type',\n",
              " 'Outlet_Identifier',\n",
              " 'Outlet_Size',\n",
              " 'Outlet_Location_Type',\n",
              " 'Outlet_Type']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_d2NaZVgKYhp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c018c8a9-e31c-4811-8082-36a6a483bc21"
      },
      "source": [
        "cat_column.remove('Item_Identifier')\n",
        "cat_column.remove('Outlet_Identifier')\n",
        "cat_column"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Item_Fat_Content',\n",
              " 'Item_Type',\n",
              " 'Outlet_Size',\n",
              " 'Outlet_Location_Type',\n",
              " 'Outlet_Type']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3LkvpwaLkQ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "368109a3-b869-43e4-a95b-df7175152ceb"
      },
      "source": [
        "#print categorical columns\n",
        "for col in cat_column:\n",
        "  print(col)\n",
        "  print(bigmart_data[col].value_counts())\n",
        "  print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Item_Fat_Content\n",
            "Low Fat    5089\n",
            "Regular    2889\n",
            "LF          316\n",
            "reg         117\n",
            "low fat     112\n",
            "Name: Item_Fat_Content, dtype: int64\n",
            "\n",
            "Item_Type\n",
            "Fruits and Vegetables    1232\n",
            "Snack Foods              1200\n",
            "Household                 910\n",
            "Frozen Foods              856\n",
            "Dairy                     682\n",
            "Canned                    649\n",
            "Baking Goods              648\n",
            "Health and Hygiene        520\n",
            "Soft Drinks               445\n",
            "Meat                      425\n",
            "Breads                    251\n",
            "Hard Drinks               214\n",
            "Others                    169\n",
            "Starchy Foods             148\n",
            "Breakfast                 110\n",
            "Seafood                    64\n",
            "Name: Item_Type, dtype: int64\n",
            "\n",
            "Outlet_Size\n",
            "Medium    2793\n",
            "Small     2388\n",
            "High       932\n",
            "Name: Outlet_Size, dtype: int64\n",
            "\n",
            "Outlet_Location_Type\n",
            "Tier 3    3350\n",
            "Tier 2    2785\n",
            "Tier 1    2388\n",
            "Name: Outlet_Location_Type, dtype: int64\n",
            "\n",
            "Outlet_Type\n",
            "Supermarket Type1    5577\n",
            "Grocery Store        1083\n",
            "Supermarket Type3     935\n",
            "Supermarket Type2     928\n",
            "Name: Outlet_Type, dtype: int64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhUY1tklQyW1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "906aca24-48a8-4d16-9cf0-a78016105994"
      },
      "source": [
        "#Average value of item_weight\n",
        "Item_Weight_avg = bigmart_data['Item_Weight'].mean()\n",
        "Item_Weight_avg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.857645184136183"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0krhGWlSRkdo"
      },
      "source": [
        "#missing values in item_weight inpalce by average value\n",
        "bigmart_data['Item_Weight'].fillna(Item_Weight_avg, inplace = True)\n",
        "bigmart_test_data['Item_Weight'].fillna(Item_Weight_avg, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rcZVSEQdSMs7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efc77fc5-942a-463b-fde8-ba366f34ee84"
      },
      "source": [
        "#check missing values item_weight after fillup by average value\n",
        "bigmart_data['Item_Weight'].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMHvElW6S2EC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "55672bf9-b5a2-4c44-af6c-976d5904d0d0"
      },
      "source": [
        "#Replace the missing values in outlet_size\n",
        "\n",
        "outlet_size_mode = bigmart_data.pivot_table(values='Outlet_Size', columns='Outlet_Type', aggfunc=(lambda x: x.mode()[0]))\n",
        "outlet_size_mode\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Grocery Store</th>\n",
              "      <th>Supermarket Type1</th>\n",
              "      <th>Supermarket Type2</th>\n",
              "      <th>Supermarket Type3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Outlet_Size</th>\n",
              "      <td>Small</td>\n",
              "      <td>Small</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Medium</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2 Supermarket Type3\n",
              "Outlet_Size         Small             Small            Medium            Medium"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByB30Bq4UAda",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3b29e39-93ed-4975-ee2d-b230031e0054"
      },
      "source": [
        "missing_values_outlet = bigmart_data['Outlet_Size'].isnull()\n",
        "missing_values_outlet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2       False\n",
              "3        True\n",
              "4       False\n",
              "        ...  \n",
              "8518    False\n",
              "8519     True\n",
              "8520    False\n",
              "8521    False\n",
              "8522    False\n",
              "Name: Outlet_Size, Length: 8523, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRlfSMI3Vobq"
      },
      "source": [
        "#missing values in outlet_size repalce\n",
        "bigmart_data.loc[missing_values_outlet,'Outlet_Size'] = bigmart_data.loc[missing_values_outlet,'Outlet_Type'].apply(lambda x: outlet_size_mode[x])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5M27yuSX3JT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a99ba6bc-6cc8-4252-a82e-d08f99e8a51c"
      },
      "source": [
        "#check missing values after replace outlet_size\n",
        "bigmart_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier              0\n",
              "Item_Weight                  0\n",
              "Item_Fat_Content             0\n",
              "Item_Visibility              0\n",
              "Item_Type                    0\n",
              "Item_MRP                     0\n",
              "Outlet_Identifier            0\n",
              "Outlet_Establishment_Year    0\n",
              "Outlet_Size                  0\n",
              "Outlet_Location_Type         0\n",
              "Outlet_Type                  0\n",
              "Item_Outlet_Sales            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_0_DFUOp2yp"
      },
      "source": [
        "#test data missing values in outlet_size repalce\n",
        "missing_values_outlet = bigmart_test_data['Outlet_Size'].isnull()\n",
        "bigmart_test_data.loc[missing_values_outlet,'Outlet_Size'] = bigmart_test_data.loc[missing_values_outlet,'Outlet_Type'].apply(lambda x: outlet_size_mode[x])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvQViqAorktM",
        "outputId": "214d7cc4-588c-41f6-98a1-d45b5a380ec4"
      },
      "source": [
        "bigmart_test_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier              0\n",
              "Item_Weight                  0\n",
              "Item_Fat_Content             0\n",
              "Item_Visibility              0\n",
              "Item_Type                    0\n",
              "Item_MRP                     0\n",
              "Outlet_Identifier            0\n",
              "Outlet_Establishment_Year    0\n",
              "Outlet_Size                  0\n",
              "Outlet_Location_Type         0\n",
              "Outlet_Type                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2cM4I_dieVM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dff96b1-a4b5-48ea-a6d8-b97aa57861e7"
      },
      "source": [
        "sum(bigmart_data['Item_Visibility']==0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "526"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77HKcn73i5wY"
      },
      "source": [
        "bigmart_data.loc[:,'Item_Visibility'].replace([0],[bigmart_data['Item_Visibility'].mean()],inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wWwoycujekY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9020c068-b313-4396-9761-60e733aac602"
      },
      "source": [
        "sum(bigmart_data['Item_Visibility']==0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jo4aJo_6RyCj"
      },
      "source": [
        "**Creation New Attributes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zmxsu3rgR3YP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5295f2e4-f14b-4185-9af9-e10a83fe73e0"
      },
      "source": [
        "bigmart_data['new_item_type'] = bigmart_data['Item_Identifier'].apply(lambda x: x[:2])\n",
        "bigmart_data['new_item_type']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       FD\n",
              "1       DR\n",
              "2       FD\n",
              "3       FD\n",
              "4       NC\n",
              "        ..\n",
              "8518    FD\n",
              "8519    FD\n",
              "8520    NC\n",
              "8521    FD\n",
              "8522    DR\n",
              "Name: new_item_type, Length: 8523, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "foZJ6HQ4SdLi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58069dc3-eb0f-40c0-8b21-085693008fdf"
      },
      "source": [
        "bigmart_data['new_item_type'] = bigmart_data['new_item_type'].map({'FD':'Food','NC':'Non-Consumable','DR':'Drink'})\n",
        "bigmart_data['new_item_type'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Food              6125\n",
              "Non-Consumable    1599\n",
              "Drink              799\n",
              "Name: new_item_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAgaqcKuU5i8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "ac3113c5-21cf-4276-e3f6-f27ae23185ce"
      },
      "source": [
        "bigmart_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "      <th>new_item_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>443.4228</td>\n",
              "      <td>Drink</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>732.3800</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
              "      <td>Non-Consumable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight  ... Item_Outlet_Sales   new_item_type\n",
              "0           FDA15         9.30  ...         3735.1380            Food\n",
              "1           DRC01         5.92  ...          443.4228           Drink\n",
              "2           FDN15        17.50  ...         2097.2700            Food\n",
              "3           FDX07        19.20  ...          732.3800            Food\n",
              "4           NCD19         8.93  ...          994.7052  Non-Consumable\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5gJYAOaY8JB"
      },
      "source": [
        "**Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZPuazVBZNgV"
      },
      "source": [
        "#Numerical features\n",
        "\n",
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scr4kYSWY5py",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "47185c4c-5e81-4f71-fc6c-5be6e9e1c0b4"
      },
      "source": [
        "#item_weight distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(bigmart_data['Item_Weight'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXrDZhWZaUT_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "ccc54c86-bb05-4812-869b-dc9d4c1e4af8"
      },
      "source": [
        " #Item_Visibility distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(bigmart_data['Item_Visibility'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6k9tsa3pbBXW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "0c7537ff-e03c-42e8-8f10-19fbec37272f"
      },
      "source": [
        "#Item_MRP distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(bigmart_data['Item_MRP'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQFiaVi8bZJK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "776e55a2-4ba1-417a-cfd6-d2a5ce0ff9dc"
      },
      "source": [
        "#Item_Outlet_Sales distribution\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(bigmart_data['Item_Outlet_Sales'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sN7g5c_npQou"
      },
      "source": [
        "bigmart_data['Item_Outlet_Sales'] = np.log(1+bigmart_data['Item_Outlet_Sales'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1VARedu1mGr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "7af7bcf0-d698-4dac-b3ff-74e7c3400d73"
      },
      "source": [
        "bigmart_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "      <th>new_item_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>8.225808</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>6.096776</td>\n",
              "      <td>Drink</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>7.648868</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>6.597664</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>6.903451</td>\n",
              "      <td>Non-Consumable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight  ... Item_Outlet_Sales   new_item_type\n",
              "0           FDA15         9.30  ...          8.225808            Food\n",
              "1           DRC01         5.92  ...          6.096776           Drink\n",
              "2           FDN15        17.50  ...          7.648868            Food\n",
              "3           FDX07        19.20  ...          6.597664            Food\n",
              "4           NCD19         8.93  ...          6.903451  Non-Consumable\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQOf4NvaqCxG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "cd3acd96-2f1e-4cad-c284-cdc973eede95"
      },
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "sns.displot(bigmart_data['Item_Outlet_Sales'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTtpQSLxchWv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "outputId": "5cb01c75-e6af-404e-d6a7-2c907ae8b8a6"
      },
      "source": [
        "#Item_Type_distribution\n",
        "plt.figure(figsize=(30,10))\n",
        "sns.countplot(x ='Item_Type', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13ZuoL7cbpnq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "a29b620e-d9ec-4271-e402-907ae322f5fa"
      },
      "source": [
        "#Outlet_Establishment_Year\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(x ='Outlet_Establishment_Year', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRvCaK-vdIvi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "e9aa16f2-a30c-472d-d315-0811e9d836a3"
      },
      "source": [
        "#Item_Fat_Content_column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x ='Item_Fat_Content', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlyEG_0EeUVZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "outputId": "8d92257f-a065-4300-a969-2b77c054ad6a"
      },
      "source": [
        "#Outlet_Location_Type_column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x ='Outlet_Location_Type', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9iMBbBqh6yR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "f0209435-22a8-4519-e8ab-bf8eee11691f"
      },
      "source": [
        "#Outlet_Type_column\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(x ='Outlet_Type', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwySekifQZhY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "3c3581cd-7f16-4580-e776-1f8215358d5b"
      },
      "source": [
        "#Outlet_Size_column\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(x ='Outlet_Size', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAYkXPppwE2U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff9c6526-7e4d-4253-a931-7f64e8c071b9"
      },
      "source": [
        "bigmart_data['Item_Fat_Content'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5089\n",
              "Regular    2889\n",
              "LF          316\n",
              "reg         117\n",
              "low fat     112\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HY16E8y7wdxZ"
      },
      "source": [
        "bigmart_data.replace({'Item_Fat_Content': {'low fat':'Low Fat','LF':'Low Fat','reg':'Regular'}},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iYedut12xfZx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "537bf51f-55f5-42ac-a4d3-73481799fc98"
      },
      "source": [
        "bigmart_data['Item_Fat_Content'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5517\n",
              "Regular    3006\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HKREPsZdxwhs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "2ed0ec8f-e4b2-4987-dfcb-3e722b7a78fe"
      },
      "source": [
        "#Item_Fat_Content_column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x ='Item_Fat_Content', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwG0oLSJTb9z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d60be944-03b2-4877-d935-a8179a90f497"
      },
      "source": [
        "bigmart_data.loc[bigmart_data['new_item_type'] == 'Non-Consumable', 'Item_Fat_Content'] = 'Non-Edible'\n",
        "bigmart_data['Item_Fat_Content'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat       3918\n",
              "Regular       3006\n",
              "Non-Edible    1599\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TU7wFBGlYCeC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "479ca429-e1bc-4328-cb99-a52f5b08f9f6"
      },
      "source": [
        "#Item_Fat_Content_column adding after non-edible\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x ='Item_Fat_Content', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQc26NL1XzL0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "f1e52330-2b99-4f75-8a0a-699ab8ba1b8d"
      },
      "source": [
        "#new_item_type_column\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.countplot(x ='new_item_type', data = bigmart_data)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIQm8hnEZzhE"
      },
      "source": [
        "**Correlation Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6iE5A4bwZwC5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "1158c5ac-5884-4032-e319-76527860edcb"
      },
      "source": [
        "corr = bigmart_data.corr()\n",
        "sns.heatmap(corr, annot = True, cmap='coolwarm')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a069828d0>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWAbLH5EbyZO"
      },
      "source": [
        "**Label Encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_xueC7Dbqdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "7e63d6e3-6b37-4305-dee9-1656cb2a84ad"
      },
      "source": [
        "bigmart_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "      <th>new_item_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>8.225808</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>6.096776</td>\n",
              "      <td>Drink</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>7.648868</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>6.597664</td>\n",
              "      <td>Food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Non-Edible</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>6.903451</td>\n",
              "      <td>Non-Consumable</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Item_Identifier  Item_Weight  ... Item_Outlet_Sales   new_item_type\n",
              "0           FDA15         9.30  ...          8.225808            Food\n",
              "1           DRC01         5.92  ...          6.096776           Drink\n",
              "2           FDN15        17.50  ...          7.648868            Food\n",
              "3           FDX07        19.20  ...          6.597664            Food\n",
              "4           NCD19         8.93  ...          6.903451  Non-Consumable\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MR2lG2WaIl-T"
      },
      "source": [
        "label = LabelEncoder()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvVTKeZBRzA6"
      },
      "source": [
        "bigmart_data['Item_Identifier'] = label.fit_transform(bigmart_data['Item_Identifier'])\n",
        "bigmart_data['Outlet'] = label.fit_transform(bigmart_data['Outlet_Identifier'])\n",
        "cat_column = ['Item_Fat_Content', 'Item_Type', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'new_item_type']\n",
        "for col in cat_column:\n",
        "  bigmart_data[col] = label.fit_transform(bigmart_data[col])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxyS3X4-SFTr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "7fa79c89-2553-44e5-96cf-7e8ead69e6b4"
      },
      "source": [
        "bigmart_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "      <th>new_item_type</th>\n",
              "      <th>Outlet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>156</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>4</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.225808</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>5.92</td>\n",
              "      <td>2</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>14</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6.096776</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>662</td>\n",
              "      <td>17.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>10</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.648868</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1121</td>\n",
              "      <td>19.20</td>\n",
              "      <td>2</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>6</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6.597664</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1297</td>\n",
              "      <td>8.93</td>\n",
              "      <td>1</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>9</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6.903451</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Item_Identifier  Item_Weight  ...  new_item_type  Outlet\n",
              "0              156         9.30  ...              1       9\n",
              "1                8         5.92  ...              0       3\n",
              "2              662        17.50  ...              1       9\n",
              "3             1121        19.20  ...              1       0\n",
              "4             1297         8.93  ...              2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gf8IX1U1UJ6q"
      },
      "source": [
        "**Onehot encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vjg8EvibStMU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "3add34bb-8ea8-4c33-9664-6a48404c940e"
      },
      "source": [
        "bigmart_data = pd.get_dummies(bigmart_data, columns=['Item_Fat_Content', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'new_item_type'])\n",
        "bigmart_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "      <th>Outlet</th>\n",
              "      <th>Item_Fat_Content_0</th>\n",
              "      <th>Item_Fat_Content_1</th>\n",
              "      <th>Item_Fat_Content_2</th>\n",
              "      <th>Outlet_Size_0</th>\n",
              "      <th>Outlet_Size_1</th>\n",
              "      <th>Outlet_Size_2</th>\n",
              "      <th>Outlet_Location_Type_0</th>\n",
              "      <th>Outlet_Location_Type_1</th>\n",
              "      <th>Outlet_Location_Type_2</th>\n",
              "      <th>Outlet_Type_0</th>\n",
              "      <th>Outlet_Type_1</th>\n",
              "      <th>Outlet_Type_2</th>\n",
              "      <th>Outlet_Type_3</th>\n",
              "      <th>new_item_type_0</th>\n",
              "      <th>new_item_type_1</th>\n",
              "      <th>new_item_type_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>156</td>\n",
              "      <td>9.30</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>4</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>8.225808</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>5.92</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>14</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>6.096776</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>662</td>\n",
              "      <td>17.50</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>10</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>7.648868</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1121</td>\n",
              "      <td>19.20</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>6</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>6.597664</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1297</td>\n",
              "      <td>8.93</td>\n",
              "      <td>0.066132</td>\n",
              "      <td>9</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>6.903451</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Item_Identifier  Item_Weight  ...  new_item_type_1  new_item_type_2\n",
              "0              156         9.30  ...                1                0\n",
              "1                8         5.92  ...                0                0\n",
              "2              662        17.50  ...                1                0\n",
              "3             1121        19.20  ...                1                0\n",
              "4             1297         8.93  ...                0                1\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtYllVECVOFy"
      },
      "source": [
        "**input spilt features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyksRrYYVVUx"
      },
      "source": [
        "x = bigmart_data.drop(columns = ['Outlet_Identifier', 'Item_Outlet_Sales'])\n",
        "y = bigmart_data['Item_Outlet_Sales']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vs6M-pHm0hBP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "caddd83b-d059-4939-eb52-c69260a2f0b7"
      },
      "source": [
        "print(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Item_Identifier  Item_Weight  ...  new_item_type_1  new_item_type_2\n",
            "0                 156        9.300  ...                1                0\n",
            "1                   8        5.920  ...                0                0\n",
            "2                 662       17.500  ...                1                0\n",
            "3                1121       19.200  ...                1                0\n",
            "4                1297        8.930  ...                0                1\n",
            "...               ...          ...  ...              ...              ...\n",
            "8518              370        6.865  ...                1                0\n",
            "8519              897        8.380  ...                1                0\n",
            "8520             1357       10.600  ...                0                1\n",
            "8521              681        7.210  ...                1                0\n",
            "8522               50       14.800  ...                0                0\n",
            "\n",
            "[8523 rows x 23 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oR-QoPew01I3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1223cfe1-a05a-4269-f434-e92523fd41be"
      },
      "source": [
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       8.225808\n",
            "1       6.096776\n",
            "2       7.648868\n",
            "3       6.597664\n",
            "4       6.903451\n",
            "          ...   \n",
            "8518    7.929984\n",
            "8519    6.310436\n",
            "8520    7.085159\n",
            "8521    7.521100\n",
            "8522    6.642056\n",
            "Name: Item_Outlet_Sales, Length: 8523, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6L6MPAGi2PBB"
      },
      "source": [
        "**Spliting Data into Train and test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbudvB8G2Onn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72b747bb-b681-41e1-a6ec-0fc9f2737b66"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)\n",
        "print(x.shape, x_train.shape, x_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8523, 23) (6818, 23) (1705, 23)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mi9MPByVzhan",
        "outputId": "94459702-8b2a-4858-9c01-679967d2e326"
      },
      "source": [
        "y_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7173    7.416681\n",
              "3315    7.991982\n",
              "5932    7.820476\n",
              "7872    6.897415\n",
              "5946    3.848874\n",
              "          ...   \n",
              "1099    7.579910\n",
              "2514    7.608066\n",
              "6637    7.604756\n",
              "2575    7.224909\n",
              "7336    7.513136\n",
              "Name: Item_Outlet_Sales, Length: 6818, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5fQVl6fzvt6",
        "outputId": "c34b755a-1f03-4871-dc77-67d8e6cb210b"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1112    7.343204\n",
              "1751    8.764990\n",
              "7648    6.976914\n",
              "7362    5.914905\n",
              "5332    4.626947\n",
              "          ...   \n",
              "3503    8.356271\n",
              "975     7.109396\n",
              "6190    7.347931\n",
              "32      8.029109\n",
              "4433    8.609201\n",
              "Name: Item_Outlet_Sales, Length: 1705, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06K3uDKi3twj"
      },
      "source": [
        "**ML Model Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6HN7VrTMUhP"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, r2_score, mean_squared_error,mean_absolute_error\n",
        "from sklearn.model_selection import cross_val_score, cross_validate\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmKKOZda3tFj"
      },
      "source": [
        "**Linear Regression Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05zpRWJVdLPc"
      },
      "source": [
        "def train (model, x_train, y_train):\n",
        "  model.fit(x_train, y_train)\n",
        "  pred = model.predict(x_train)\n",
        "  cv_score = cross_val_score(model, x_train, y_train, scoring='neg_mean_squared_error', cv=10)\n",
        "  cv_score = np.abs(np.mean(cv_score))\n",
        "  cv_score_std = np.sqrt(np.mean(cv_score))\n",
        "  lr_accuracy = round(model.score(x_train,y_train) *100 , 2)\n",
        "  r2_score_p = r2_score(y_train, pred)\n",
        "  print(\"Accuracy: \",lr_accuracy )\n",
        "  print(\"RMSE : %.4g\" % np.sqrt(metrics.mean_squared_error(y_train, pred)))\n",
        "  print(\"MSE:\",mean_squared_error(y_train,pred))\n",
        "  print(\"R2_Score: \",r2_score_p )\n",
        "  print(\"CV_Score: \",cv_score)\n",
        "  print(\"standard deviation: \",cv_score_std)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KeFMp81kDfk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "d79bd5f4-c61a-4456-f279-f5785bd70aae"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
        "model = LinearRegression(normalize=True)\n",
        "print(\"Model Report: Linear Regression\")\n",
        "train(model, x_train, y_train)\n",
        "coef = pd.Series(model.coef_, x.columns).sort_values()\n",
        "coef.plot(kind='bar',title=\"Model Cofficients\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Report: Linear Regression\n",
            "Accuracy:  72.26\n",
            "RMSE : 0.5356\n",
            "MSE: 0.28683210152097516\n",
            "R2_Score:  0.7225968338404131\n",
            "CV_Score:  0.28870171227384994\n",
            "standard deviation:  0.5373096986597673\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a06015750>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4U2XD988vt6r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "2b5dd919-9a2e-4559-bfea-04a11506367c"
      },
      "source": [
        "model = Ridge(normalize=True)\n",
        "print(\"Model Report: Ridge\")\n",
        "train(model, x_train, y_train)\n",
        "coef = pd.Series(model.coef_, x.columns).sort_values()\n",
        "coef.plot(kind='bar',title=\"Model Cofficients\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Report: Ridge\n",
            "Accuracy:  58.7\n",
            "RMSE : 0.6535\n",
            "MSE: 0.4270501081239044\n",
            "R2_Score:  0.5869881666864194\n",
            "CV_Score:  0.4283231636112806\n",
            "standard deviation:  0.6544640277442914\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a01ed3fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89vd9NvpwSYC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "f32fa490-77c1-471c-9d64-38f4e42e4279"
      },
      "source": [
        "model = Lasso()\n",
        "print(\"Model Report: Lasso\")\n",
        "train(model, x_train, y_train)\n",
        "coef = pd.Series(model.coef_, x.columns).sort_values()\n",
        "coef.plot(kind='bar',title=\"Model Cofficients\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Report: Lasso\n",
            "Accuracy:  26.38\n",
            "RMSE : 0.8725\n",
            "MSE: 0.7612136945239549\n",
            "R2_Score:  0.2638094276573153\n",
            "CV_Score:  0.761855148755895\n",
            "standard deviation:  0.872843140980036\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a01da4b10>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gmt1CMqQwn_"
      },
      "source": [
        "**Decision Tree Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "Y2dZ9fomO-yB",
        "outputId": "66bab056-cb4a-4851-bfac-771a8611c560"
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "model = DecisionTreeRegressor()\n",
        "print(\"Model Report : Decision Tree Regressor\")\n",
        "train(model, x_train, y_train)\n",
        "coef = pd.Series(model.feature_importances_, x.columns).sort_values(ascending=False)\n",
        "coef.plot(kind='bar',title=\"Model Cofficients\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Report : Decision Tree Regressor\n",
            "Accuracy:  100.0\n",
            "RMSE : 4.446e-06\n",
            "MSE: 1.9769677319570817e-11\n",
            "R2_Score:  0.9999999999808802\n",
            "CV_Score:  0.5842186932101587\n",
            "standard deviation:  0.7643420001610266\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a00098190>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLcJH-rTRr_w"
      },
      "source": [
        "**Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oneH6ZD7yq7p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "240edd0c-7461-49aa-f75c-13c78f05d27f"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model = RandomForestRegressor(n_estimators=100,max_depth=6, min_samples_leaf=50,n_jobs=4)\n",
        "print(\"Model Report: Random Forest Regressor\")\n",
        "train(model, x_train, y_train)\n",
        "coef = pd.Series(model.feature_importances_, x.columns).sort_values(ascending=False)\n",
        "coef.plot(kind='bar',title=\"Model Cofficients\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Report: Random Forest Regressor\n",
            "Accuracy:  74.8\n",
            "RMSE : 0.5105\n",
            "MSE: 0.26056001550772706\n",
            "R2_Score:  0.7480052863917364\n",
            "CV_Score:  0.2717003880429042\n",
            "standard deviation:  0.5212488734212326\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a01d0d7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hr7gUniR6aQS"
      },
      "source": [
        "**XGBoost Regressor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OC0Sl-t34HB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29376f31-e453-4f4e-fbe0-943aa0b4104e"
      },
      "source": [
        "#prediction on training data\n",
        "regressor = XGBRegressor(n_estimators=1000, learning_rate=0.05)\n",
        "regressor.fit(x_train, y_train)\n",
        "training_data_prediction = regressor.predict(x_train)\n",
        "r2_train = metrics.r2_score(y_train, training_data_prediction)\n",
        "xg_accuracy = round(regressor.score(x_train,y_train)*100,2)\n",
        "r2_score(y_train, regressor.predict(x_train))\n",
        "cv_score = cross_val_score(regressor, x_train,y_train, cv=10, scoring='neg_mean_squared_error')\n",
        "cv_score = np.abs(np.mean(cv_score))\n",
        "cv_score_std = np.sqrt(np.mean(cv_score))\n",
        "print(\"Model Report: XGBRegressor\")\n",
        "print(\"Accuracy: \",xg_accuracy )\n",
        "print(\"RMSE : %.4g\" % np.sqrt(metrics.mean_squared_error(y_train, regressor.predict(x_train))))\n",
        "print(\"MSE:\",mean_squared_error(y_train,regressor.predict(x_train)))\n",
        "print(\"R2_Score: \",r2_train )\n",
        "print(\"CV_Score: \",cv_score)\n",
        "print(\"standard deviation: \",cv_score_std)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10:24:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:24:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:24:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:24:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:24:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:24:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:24:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:24:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Model Report: XGBRegressor\n",
            "Accuracy:  80.29\n",
            "RMSE : 0.4514\n",
            "MSE: 0.20375260673575954\n",
            "R2_Score:  0.8029452842898213\n",
            "CV_Score:  0.2838434497184702\n",
            "standard deviation:  0.5327696028476758\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzCa3Ys15fOt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "769215d6-a86d-4eba-d3cd-c7513d4e4180"
      },
      "source": [
        "#prediction on test data\n",
        "test_data_prediction = regressor.predict(x_test)\n",
        "r2_test = metrics.r2_score(y_test, test_data_prediction)\n",
        "regressor = XGBRegressor(n_estimators=1000, learning_rate=0.05)\n",
        "regressor.fit(x_test, y_test)\n",
        "xg_accuracy = round(regressor.score(x_test,y_test)*100,2)\n",
        "cv_score = cross_val_score(regressor, x_test,y_test, cv=5, scoring='neg_mean_squared_error')\n",
        "cv_score = np.abs(np.mean(cv_score))\n",
        "cv_score_std = np.sqrt(np.mean(cv_score))\n",
        "print(\"Model Report: XGBRegressor\")\n",
        "print(\"Accuracy: \",xg_accuracy )\n",
        "print(\"RMSE : %.4g\" % np.sqrt(metrics.mean_squared_error(y_train, regressor.predict(x_train))))\n",
        "print(\"MSE:\",mean_squared_error(y_train,regressor.predict(x_train)))\n",
        "print(\"R2_Score: \",r2_test )\n",
        "print(\"CV_Score: \",cv_score)\n",
        "print(\"standard deviation: \",cv_score_std)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10:25:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:18] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[10:25:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "Model Report: XGBRegressor\n",
            "Accuracy:  88.86\n",
            "RMSE : 0.5565\n",
            "MSE: 0.3097373690617788\n",
            "R2_Score:  0.7211471624558792\n",
            "CV_Score:  0.32199415408130716\n",
            "standard deviation:  0.567445287302051\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhFYk93RUkdH"
      },
      "source": [
        "**Gradient Boost**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUF1YMf48cjG"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "params = {'n_estimators': 600, 'max_depth': 6,\n",
        "        'learning_rate': 0.1, 'loss': 'huber','alpha':0.95}\n",
        "gbr = GradientBoostingRegressor(**params).fit(x_train,y_train)\n",
        "params1 = {'n_estimators': 500, 'max_depth': 6,\n",
        "        'learning_rate': 0.2, 'loss': 'huber','alpha':0.95}\n",
        "gbr1 = GradientBoostingRegressor(**params).fit(x_train,y_train)\n",
        "\n",
        "params2 = {'n_estimators': 400, 'max_depth': 6,\n",
        "        'learning_rate': 0.1, 'loss': 'huber','alpha':0.95}\n",
        "gbr2 = GradientBoostingRegressor(**params).fit(x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blfsBXF-8_0N",
        "outputId": "2d63ec64-9cd7-40ec-a60a-b6fc341f754b"
      },
      "source": [
        "rf_accuracy = round(gbr.score(x_train,y_train)*100,2)\n",
        "rf_accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94.08"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jD_AKi94-7Zd",
        "outputId": "b72cc49f-fbec-4d8e-b49e-6e2a0cdbabaa"
      },
      "source": [
        "rf_accuracy1 = round(gbr1.score(x_train,y_train)*100,2)\n",
        "rf_accuracy1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94.36"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPC5d2Sa-_FC",
        "outputId": "b6c32fba-6468-422f-e337-37d2501b5bb3"
      },
      "source": [
        "rf_accuracy2 = round(gbr2.score(x_train,y_train)*100,2)#Best score\n",
        "rf_accuracy2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94.27"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVQCeW3k_ARS"
      },
      "source": [
        "cv_score = cross_val_score(gbr2, x_train,y_train, cv=10, scoring='neg_mean_squared_error')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FE51ujV_HlW",
        "outputId": "c3572dc7-1ca5-4464-e79c-5a0399c977bb"
      },
      "source": [
        "  print(\"Model Report: Gradient Boost\")\n",
        "  print(\"Accuracy: \",rf_accuracy1 )\n",
        "  pred = gbr2.predict(x_train)\n",
        "  print(\"RMSE : %.4g\" % np.sqrt(metrics.mean_squared_error(y_train, pred)))\n",
        "  print(\"MSE:\",mean_squared_error(y_train,pred))\n",
        "  print(\"R2_Score: \",r2_score(y_train, pred) )\n",
        "  cv_score = np.abs(np.mean(cv_score))\n",
        "  print(\"CV_Score: \",cv_score)\n",
        "  print(\"standard deviation: \",np.sqrt(np.abs(cv_score)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Report: Gradient Boost\n",
            "Accuracy:  94.36\n",
            "RMSE : 0.2434\n",
            "MSE: 0.059250039419092855\n",
            "R2_Score:  0.942697667231871\n",
            "CV_Score:  0.32141721243369703\n",
            "standard deviation:  0.5669366917334748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQi29hjeyQbS"
      },
      "source": [
        "**SVM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmQO2knY8-8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e164bb5-ad69-48da-c33b-557f3b6db7ae"
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "sv_regressor = SVR(kernel='rbf')\n",
        "sv_regressor.fit(x_train,y_train)\n",
        "sv_accuracy = round(sv_regressor.score(x_test,y_test)*100,2)\n",
        "yfit = sv_regressor.predict(x_train)\n",
        "score = sv_regressor.score(x_train,y_train)\n",
        "print(\"Accuracy: \",sv_accuracy )\n",
        "print(\"RMSE : %.4g\" % np.sqrt(metrics.mean_squared_error(y_train, sv_regressor.fit(x_train,y_train).predict(x_train))))\n",
        "print(\"MSE:\", mean_squared_error(y_train, yfit))\n",
        "print(\"R2_Score: \",score)  \n",
        "cv_score = cross_val_score(sv_regressor, x_train, y_train, scoring='neg_mean_squared_error', cv=5)\n",
        "cv_score = np.abs(np.mean(cv_score))\n",
        "print(\"CV_Score: \",cv_score)\n",
        "print(\"standard deviation: \",np.sqrt(np.abs(cv_score)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  21.25\n",
            "RMSE : 0.9008\n",
            "MSE: 0.8113515314725334\n",
            "R2_Score:  0.21531975498756462\n",
            "CV_Score:  0.8130418373801673\n",
            "standard deviation:  0.9016883260751285\n"
          ]
        }
      ]
    }
  ]
}