main.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Black Friday Sales Prediction - RMSE: 2535\n",
        "# Present Rank : 636\n",
        "Problem Statement : \n",
        "\n",
        "'A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month.\n",
        "The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month.\n",
        "\n",
        "Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.'\n",
        "\n",
        "\n",
        " - Coded by Saanvi Birla, Suryansh Singh Rajawat & Yash Haldar as a DATA WRANGLING PROJECT\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Importing Basic Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "k9cfDp-6yXnw",
        "outputId": "6af3d48d-ff98-4c75-c671-1808ff5e69f8"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import linear_model\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Loading the Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Read train data\n",
        "train = pd.read_csv('train.csv')\n",
        "# Read test data\n",
        "test = pd.read_csv('test.csv')\n",
        "df2 = test.copy() # in order to create the submission file"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Getting basic info or an overview of the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Product_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>City_Category</th>\n",
              "      <th>Stay_In_Current_City_Years</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Product_Category_1</th>\n",
              "      <th>Product_Category_2</th>\n",
              "      <th>Product_Category_3</th>\n",
              "      <th>Purchase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>P00069042</td>\n",
              "      <td>F</td>\n",
              "      <td>0-17</td>\n",
              "      <td>10</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000001</td>\n",
              "      <td>P00248942</td>\n",
              "      <td>F</td>\n",
              "      <td>0-17</td>\n",
              "      <td>10</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>15200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000001</td>\n",
              "      <td>P00087842</td>\n",
              "      <td>F</td>\n",
              "      <td>0-17</td>\n",
              "      <td>10</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000001</td>\n",
              "      <td>P00085442</td>\n",
              "      <td>F</td>\n",
              "      <td>0-17</td>\n",
              "      <td>10</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000002</td>\n",
              "      <td>P00285442</td>\n",
              "      <td>M</td>\n",
              "      <td>55+</td>\n",
              "      <td>16</td>\n",
              "      <td>C</td>\n",
              "      <td>4+</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7969</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
              "0  1000001  P00069042      F  0-17          10             A   \n",
              "1  1000001  P00248942      F  0-17          10             A   \n",
              "2  1000001  P00087842      F  0-17          10             A   \n",
              "3  1000001  P00085442      F  0-17          10             A   \n",
              "4  1000002  P00285442      M   55+          16             C   \n",
              "\n",
              "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
              "0                          2               0                   3   \n",
              "1                          2               0                   1   \n",
              "2                          2               0                  12   \n",
              "3                          2               0                  12   \n",
              "4                         4+               0                   8   \n",
              "\n",
              "   Product_Category_2  Product_Category_3  Purchase  \n",
              "0                 NaN                 NaN      8370  \n",
              "1                 6.0                14.0     15200  \n",
              "2                 NaN                 NaN      1422  \n",
              "3                14.0                 NaN      1057  \n",
              "4                 NaN                 NaN      7969  "
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Product_Category_1</th>\n",
              "      <th>Product_Category_2</th>\n",
              "      <th>Product_Category_3</th>\n",
              "      <th>Purchase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.500680e+05</td>\n",
              "      <td>550068.000000</td>\n",
              "      <td>550068.000000</td>\n",
              "      <td>550068.000000</td>\n",
              "      <td>376430.000000</td>\n",
              "      <td>166821.000000</td>\n",
              "      <td>550068.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.003029e+06</td>\n",
              "      <td>8.076707</td>\n",
              "      <td>0.409653</td>\n",
              "      <td>5.404270</td>\n",
              "      <td>9.842329</td>\n",
              "      <td>12.668243</td>\n",
              "      <td>9263.968713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.727592e+03</td>\n",
              "      <td>6.522660</td>\n",
              "      <td>0.491770</td>\n",
              "      <td>3.936211</td>\n",
              "      <td>5.086590</td>\n",
              "      <td>4.125338</td>\n",
              "      <td>5023.065394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000001e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.001516e+06</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>5823.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.003077e+06</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>8047.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.004478e+06</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>12054.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.006040e+06</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>23961.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            User_ID     Occupation  Marital_Status  Product_Category_1  \\\n",
              "count  5.500680e+05  550068.000000   550068.000000       550068.000000   \n",
              "mean   1.003029e+06       8.076707        0.409653            5.404270   \n",
              "std    1.727592e+03       6.522660        0.491770            3.936211   \n",
              "min    1.000001e+06       0.000000        0.000000            1.000000   \n",
              "25%    1.001516e+06       2.000000        0.000000            1.000000   \n",
              "50%    1.003077e+06       7.000000        0.000000            5.000000   \n",
              "75%    1.004478e+06      14.000000        1.000000            8.000000   \n",
              "max    1.006040e+06      20.000000        1.000000           20.000000   \n",
              "\n",
              "       Product_Category_2  Product_Category_3       Purchase  \n",
              "count       376430.000000       166821.000000  550068.000000  \n",
              "mean             9.842329           12.668243    9263.968713  \n",
              "std              5.086590            4.125338    5023.065394  \n",
              "min              2.000000            3.000000      12.000000  \n",
              "25%              5.000000            9.000000    5823.000000  \n",
              "50%              9.000000           14.000000    8047.000000  \n",
              "75%             15.000000           16.000000   12054.000000  \n",
              "max             18.000000           18.000000   23961.000000  "
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 550068 entries, 0 to 550067\n",
            "Data columns (total 12 columns):\n",
            " #   Column                      Non-Null Count   Dtype  \n",
            "---  ------                      --------------   -----  \n",
            " 0   User_ID                     550068 non-null  int64  \n",
            " 1   Product_ID                  550068 non-null  object \n",
            " 2   Gender                      550068 non-null  object \n",
            " 3   Age                         550068 non-null  object \n",
            " 4   Occupation                  550068 non-null  int64  \n",
            " 5   City_Category               550068 non-null  object \n",
            " 6   Stay_In_Current_City_Years  550068 non-null  object \n",
            " 7   Marital_Status              550068 non-null  int64  \n",
            " 8   Product_Category_1          550068 non-null  int64  \n",
            " 9   Product_Category_2          376430 non-null  float64\n",
            " 10  Product_Category_3          166821 non-null  float64\n",
            " 11  Purchase                    550068 non-null  int64  \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 50.4+ MB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "User_ID                            0\n",
              "Product_ID                         0\n",
              "Gender                             0\n",
              "Age                                0\n",
              "Occupation                         0\n",
              "City_Category                      0\n",
              "Stay_In_Current_City_Years         0\n",
              "Marital_Status                     0\n",
              "Product_Category_1                 0\n",
              "Product_Category_2            173638\n",
              "Product_Category_3            383247\n",
              "Purchase                           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "User_ID                            0\n",
              "Product_ID                         0\n",
              "Gender                             0\n",
              "Age                                0\n",
              "Occupation                         0\n",
              "City_Category                      0\n",
              "Stay_In_Current_City_Years         0\n",
              "Marital_Status                     0\n",
              "Product_Category_1                 0\n",
              "Product_Category_2             72344\n",
              "Product_Category_3            162562\n",
              "dtype: int64"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# EDA : EXPLORATORY DATA ANALYSIS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Distribution of Purchase Amounts\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(train['Purchase'], bins=30, kde=True, color='skyblue')\n",
        "plt.title('Distribution of Purchase Amounts')\n",
        "plt.xlabel('Purchase Amount')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "bGQU1VmY0FZK",
        "outputId": "c013f399-9ddd-430b-a126-0275798d143f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Age', y='Purchase', data=train, palette='Set3')\n",
        "plt.title('Purchase Amount by Age Group')\n",
        "plt.xlabel('Age Group')\n",
        "plt.ylabel('Purchase Amount')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "qwae77Ie0cDB",
        "outputId": "2371cce5-6840-4828-a9a3-6aefc7547840"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(x='Gender', y='Purchase', data=train, palette='pastel')\n",
        "plt.title('Purchase Amount by Gender')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Purchase Amount')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "QVmQRcqC0iO1",
        "outputId": "6ad0508b-853f-49f2-b49f-b15127e31aae"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.violinplot(x='City_Category', y='Purchase', data=train, palette='viridis')\n",
        "plt.title('Purchase Amount by City Category')\n",
        "plt.xlabel('City Category')\n",
        "plt.ylabel('Purchase Amount')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Importing more libraries for encoding and scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxV_YvUAThYN",
        "outputId": "1d023a9e-637c-4c73-c419-18ea7ac3e57a"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "train['User_ID'] = train['User_ID']\n",
        "test['User_ID'] = test['User_ID']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [],
      "source": [
        "train['Product_ID'] = train['Product_ID'].str.replace('P00', '')\n",
        "test['Product_ID'] = test['Product_ID'].str.replace('P00', '')\n",
        "\n",
        "scaler = StandardScaler()\n",
        "train['Product_ID'] = scaler.fit_transform(train['Product_ID'].values.reshape(-1, 1))\n",
        "test['Product_ID'] = scaler.transform(test['Product_ID'].values.reshape(-1, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [],
      "source": [
        "categorical_col = ['Gender', 'City_Category']\n",
        "numerical_col = ['Age', 'Occupation', 'Stay_In_Current_City_Years', 'Product_Category_1',\n",
        "           'Product_Category_2', 'Product_Category_3']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [],
      "source": [
        "train['Age']=train['Age'].replace('0-17',17)\n",
        "train['Age']=train['Age'].replace('18-25',25)\n",
        "train['Age']=train['Age'].replace('26-35',35)\n",
        "train['Age']=train['Age'].replace('36-45',45)\n",
        "train['Age']=train['Age'].replace('46-50',50)\n",
        "train['Age']=train['Age'].replace('51-55',55)\n",
        "train['Age']=train['Age'].replace('55+',60)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [],
      "source": [
        "test['Age']=test['Age'].replace('0-17',17)\n",
        "test['Age']=test['Age'].replace('18-25',25)\n",
        "test['Age']=test['Age'].replace('26-35',35)\n",
        "test['Age']=test['Age'].replace('36-45',45)\n",
        "test['Age']=test['Age'].replace('46-50',50)\n",
        "test['Age']=test['Age'].replace('51-55',55)\n",
        "test['Age']=test['Age'].replace('55+',60)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [],
      "source": [
        "train['Stay_In_Current_City_Years']=train['Stay_In_Current_City_Years'].replace('4+',4)\n",
        "test['Stay_In_Current_City_Years']=test['Stay_In_Current_City_Years'].replace('4+',4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "filling missing values with zero"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {},
      "outputs": [],
      "source": [
        "train = train.fillna(0)\n",
        "test = test.fillna(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Encoding categorical columns\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "for col in categorical_col:\n",
        "    train[col] = encoder.fit_transform(train[col])\n",
        "    test[col] = encoder.transform(test[col])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Scaling numerical columns\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "for col in numerical_col:\n",
        "    train[col] = scaler.fit_transform(train[col].values.reshape(-1, 1))\n",
        "    test[col] = scaler.transform(test[col].values.reshape(-1, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User_ID</th>\n",
              "      <th>Product_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Occupation</th>\n",
              "      <th>City_Category</th>\n",
              "      <th>Stay_In_Current_City_Years</th>\n",
              "      <th>Marital_Status</th>\n",
              "      <th>Product_Category_1</th>\n",
              "      <th>Product_Category_2</th>\n",
              "      <th>Product_Category_3</th>\n",
              "      <th>Purchase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>-1.028774</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.059570</td>\n",
              "      <td>0.294864</td>\n",
              "      <td>0</td>\n",
              "      <td>0.109801</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.610809</td>\n",
              "      <td>-1.083654</td>\n",
              "      <td>-0.614641</td>\n",
              "      <td>8370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000001</td>\n",
              "      <td>0.722139</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.059570</td>\n",
              "      <td>0.294864</td>\n",
              "      <td>0</td>\n",
              "      <td>0.109801</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.118912</td>\n",
              "      <td>-0.118323</td>\n",
              "      <td>1.625106</td>\n",
              "      <td>15200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000001</td>\n",
              "      <td>-0.845799</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.059570</td>\n",
              "      <td>0.294864</td>\n",
              "      <td>0</td>\n",
              "      <td>0.109801</td>\n",
              "      <td>0</td>\n",
              "      <td>1.675656</td>\n",
              "      <td>-1.083654</td>\n",
              "      <td>-0.614641</td>\n",
              "      <td>1422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000001</td>\n",
              "      <td>-0.869157</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.059570</td>\n",
              "      <td>0.294864</td>\n",
              "      <td>0</td>\n",
              "      <td>0.109801</td>\n",
              "      <td>0</td>\n",
              "      <td>1.675656</td>\n",
              "      <td>1.168785</td>\n",
              "      <td>-0.614641</td>\n",
              "      <td>1057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000002</td>\n",
              "      <td>1.077382</td>\n",
              "      <td>1</td>\n",
              "      <td>2.094842</td>\n",
              "      <td>1.214734</td>\n",
              "      <td>2</td>\n",
              "      <td>1.660861</td>\n",
              "      <td>0</td>\n",
              "      <td>0.659449</td>\n",
              "      <td>-1.083654</td>\n",
              "      <td>-0.614641</td>\n",
              "      <td>7969</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   User_ID  Product_ID  Gender       Age  Occupation  City_Category  \\\n",
              "0  1000001   -1.028774       0 -2.059570    0.294864              0   \n",
              "1  1000001    0.722139       0 -2.059570    0.294864              0   \n",
              "2  1000001   -0.845799       0 -2.059570    0.294864              0   \n",
              "3  1000001   -0.869157       0 -2.059570    0.294864              0   \n",
              "4  1000002    1.077382       1  2.094842    1.214734              2   \n",
              "\n",
              "   Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
              "0                    0.109801               0           -0.610809   \n",
              "1                    0.109801               0           -1.118912   \n",
              "2                    0.109801               0            1.675656   \n",
              "3                    0.109801               0            1.675656   \n",
              "4                    1.660861               0            0.659449   \n",
              "\n",
              "   Product_Category_2  Product_Category_3  Purchase  \n",
              "0           -1.083654           -0.614641      8370  \n",
              "1           -0.118323            1.625106     15200  \n",
              "2           -1.083654           -0.614641      1422  \n",
              "3            1.168785           -0.614641      1057  \n",
              "4           -1.083654           -0.614641      7969  "
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Model starts from here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "id": "tZ85BxeE2ONB",
        "outputId": "14944eeb-044d-435d-b82e-ffe27e5ea15d"
      },
      "outputs": [],
      "source": [
        "X = train.drop(['Purchase'], axis=1)\n",
        "y = train[['Purchase']]\n",
        "X_test = test\n",
        "\n",
        "# Split the data for training and validation\n",
        "X_train, X_val, y_train, y_val = train_test_split(\n",
        "    X, y,\n",
        "    test_size=0.20,\n",
        "    random_state=42,\n",
        "    shuffle=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "io1OSpV8Tqib",
        "outputId": "340c450b-1488-49d9-8e71-68ff4a288e71"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-rmse:4990.44397\tvalidation_1-rmse:4977.99978\n",
            "[100]\tvalidation_0-rmse:3260.28388\tvalidation_1-rmse:3306.54537\n",
            "[200]\tvalidation_0-rmse:2743.80110\tvalidation_1-rmse:2844.70054\n",
            "[300]\tvalidation_0-rmse:2574.39568\tvalidation_1-rmse:2721.18446\n",
            "[400]\tvalidation_0-rmse:2487.51303\tvalidation_1-rmse:2674.72187\n",
            "[500]\tvalidation_0-rmse:2424.67278\tvalidation_1-rmse:2645.78956\n",
            "[600]\tvalidation_0-rmse:2371.54193\tvalidation_1-rmse:2624.04810\n",
            "[700]\tvalidation_0-rmse:2320.75818\tvalidation_1-rmse:2600.36398\n",
            "[800]\tvalidation_0-rmse:2280.97242\tvalidation_1-rmse:2585.57218\n",
            "[900]\tvalidation_0-rmse:2245.73642\tvalidation_1-rmse:2575.10683\n",
            "[1000]\tvalidation_0-rmse:2215.31760\tvalidation_1-rmse:2567.15664\n",
            "[1100]\tvalidation_0-rmse:2186.94691\tvalidation_1-rmse:2560.72422\n",
            "[1200]\tvalidation_0-rmse:2159.79712\tvalidation_1-rmse:2554.73367\n",
            "[1300]\tvalidation_0-rmse:2134.35852\tvalidation_1-rmse:2549.95452\n",
            "[1400]\tvalidation_0-rmse:2110.47459\tvalidation_1-rmse:2546.39647\n",
            "[1500]\tvalidation_0-rmse:2087.46179\tvalidation_1-rmse:2543.39989\n",
            "[1600]\tvalidation_0-rmse:2065.43019\tvalidation_1-rmse:2541.18117\n",
            "[1700]\tvalidation_0-rmse:2043.65441\tvalidation_1-rmse:2538.87033\n",
            "[1800]\tvalidation_0-rmse:2022.60019\tvalidation_1-rmse:2537.11247\n",
            "[1900]\tvalidation_0-rmse:2001.89509\tvalidation_1-rmse:2536.14136\n",
            "[1999]\tvalidation_0-rmse:1982.20061\tvalidation_1-rmse:2535.21031\n",
            "\n",
            "Validation RMSE: 2535.21\n"
          ]
        }
      ],
      "source": [
        "# Import required libraries\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from xgboost import XGBRegressor\n",
        "\n",
        "# Prepare the data\n",
        "X = train.drop(['Purchase'], axis=1)\n",
        "y = train[['Purchase']]\n",
        "X_test = test\n",
        "\n",
        "# Store original IDs before splitting\n",
        "original_test_product_ids = df2['Product_ID'].copy()\n",
        "original_test_user_ids = df2['User_ID'].copy()\n",
        "\n",
        "# Split the data for training and validation\n",
        "X_train, X_val, y_train, y_val = train_test_split(\n",
        "    X, y,\n",
        "    test_size=0.20,\n",
        "    random_state=42,\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "# Initialize XGBoost with optimized parameters\n",
        "xgb_reg = XGBRegressor(\n",
        "    learning_rate=0.01,          # Reduced to allow more granular improvements\n",
        "    max_depth=12,                # Increased to capture more complex patterns\n",
        "    min_child_weight=1,          # Reduced to allow for finer splits\n",
        "    n_estimators=2000,           # Increased number of trees\n",
        "    subsample=0.8,               # Random sampling of training data\n",
        "    colsample_bytree=0.8,        # Random sampling of features\n",
        "    colsample_bylevel=0.8,       # Sampling at each level\n",
        "    gamma=0.1,                   # Minimum loss reduction\n",
        "    reg_alpha=0.1,               # L1 regularization\n",
        "    reg_lambda=1.0,              # L2 regularization\n",
        "    random_state=42,\n",
        "    tree_method='hist',          # Faster histogram-based algorithm\n",
        "    objective='reg:squarederror',\n",
        "    booster='gbtree',\n",
        "    eval_metric='rmse'           # Use RMSE as evaluation metric\n",
        ")\n",
        "\n",
        "# Fit the model with early stopping\n",
        "xgb_reg.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    eval_set=[(X_train, y_train), (X_val, y_val)],    # Increased patience for better convergence\n",
        "    verbose=100                  # Print progress every 100 iterations\n",
        ")\n",
        "\n",
        "# Make predictions on validation set and calculate RMSE\n",
        "y_pred_val = xgb_reg.predict(X_val)\n",
        "validation_rmse = np.sqrt(mean_squared_error(y_val, y_pred_val))\n",
        "print(f\"\\nValidation RMSE: {validation_rmse:.2f}\")\n",
        "\n",
        "# Make predictions on test set\n",
        "y_pred_test = xgb_reg.predict(X_test)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Storing the output in the csv file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ollsucKTvtm",
        "outputId": "cafaced7-7c9f-408f-bd67-f346c2af9711"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Predictions saved to 'predictions_XGBoost.csv'\n"
          ]
        }
      ],
      "source": [
        "output = pd.DataFrame({\n",
        "    'Product_ID': original_test_product_ids,  # Use original Product IDs\n",
        "    'User_ID': original_test_user_ids,        # Use original User IDs\n",
        "    'Purchase': y_pred_test           # Keep predictions as floats\n",
        "})\n",
        "\n",
        "# Save predictions to CSV\n",
        "output.to_csv('predictions_XGBoost.csv', index=False)\n",
        "print(\"\\nPredictions saved to 'predictions_XGBoost.csv'\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}