Black Friday Sales Prediction (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32392820",
   "metadata": {},
   "source": [
    "# Black Friday Sales Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415f9cc3",
   "metadata": {},
   "source": [
    "## About the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f27e8a",
   "metadata": {},
   "source": [
    "This dataset comprises of sales transactions captured at a retail store. It’s a classic dataset to explore and expand your feature engineering skills and day to day understanding from multiple shopping experiences. This is a regression problem.The idea and dataset is taken from AnalyticsVidhya where the project is a part of a hackathon."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c44bae0",
   "metadata": {},
   "source": [
    "## Data Description"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7893af1c",
   "metadata": {},
   "source": [
    "Variable : Definition\n",
    "\n",
    "User_ID : User ID\n",
    "\n",
    "Product_ID : Product ID\n",
    "\n",
    "Gender : Sex of User\n",
    "\n",
    "Age : Age in bins\n",
    "\n",
    "Occupation : Occupation (Masked)\n",
    "\n",
    "City_Category : Category of the City (A,B,C)\n",
    "\n",
    "Stay_In_Current_City_Years: Number of years stay in current city\n",
    "\n",
    "Marital_Status: Marital Status\n",
    "\n",
    "Product_Category_1: Product Category (Masked)\n",
    "\n",
    "Product_Category_2 : Product may belongs to other category also (Masked)\n",
    "\n",
    "Product_Category_3: Product may belongs to other category also (Masked)\n",
    "\n",
    "Purchase : Purchase Amount (Target Variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b87b521",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5d25f585",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6ba81e",
   "metadata": {},
   "source": [
    "## Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "8ba30139",
   "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": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv('test.csv')\n",
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "3b23bc30",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000009</td>\n",
       "      <td>P00113442</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>17</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00288442</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000004  P00128942      M  46-50           7             B   \n",
       "1  1000009  P00113442      M  26-35          17             C   \n",
       "2  1000010  P00288442      F  36-45           1             B   \n",
       "3  1000010  P00145342      F  36-45           1             B   \n",
       "4  1000011  P00053842      F  26-35           1             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               1                   1   \n",
       "1                          0               0                   3   \n",
       "2                         4+               1                   5   \n",
       "3                         4+               1                   4   \n",
       "4                          1               0                   4   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  \n",
       "0                11.0                 NaN  \n",
       "1                 5.0                 NaN  \n",
       "2                14.0                 NaN  \n",
       "3                 9.0                 NaN  \n",
       "4                 5.0                12.0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "ced8e437",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for submission\n",
    "\n",
    "submission = pd.DataFrame()\n",
    "\n",
    "submission['User_ID'] = test['User_ID']\n",
    "submission['Product_ID'] = test['Product_ID']\n",
    "submission['Purchase'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e3c09fbe",
   "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>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000009</td>\n",
       "      <td>P00113442</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00288442</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233594</th>\n",
       "      <td>1006036</td>\n",
       "      <td>P00118942</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>1006036</td>\n",
       "      <td>P00254642</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>1006036</td>\n",
       "      <td>P00031842</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>1006037</td>\n",
       "      <td>P00124742</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>1006039</td>\n",
       "      <td>P00316642</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>233599 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User_ID Product_ID  Purchase\n",
       "0       1000004  P00128942       NaN\n",
       "1       1000009  P00113442       NaN\n",
       "2       1000010  P00288442       NaN\n",
       "3       1000010  P00145342       NaN\n",
       "4       1000011  P00053842       NaN\n",
       "...         ...        ...       ...\n",
       "233594  1006036  P00118942       NaN\n",
       "233595  1006036  P00254642       NaN\n",
       "233596  1006036  P00031842       NaN\n",
       "233597  1006037  P00124742       NaN\n",
       "233598  1006039  P00316642       NaN\n",
       "\n",
       "[233599 rows x 3 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d70667ae",
   "metadata": {},
   "source": [
    "## Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "6a6bc044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550068, 12)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "7713ba6d",
   "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": [
    "sales.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e942564e",
   "metadata": {},
   "source": [
    "Looking at the data, we can conclude that our set possesses 12 different parameters: 7 numerical (integer and float) and 5 object variables.\n",
    "\n",
    "Looking into the summary statistics for these 7numercal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "6dd0c29b",
   "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": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5821b524",
   "metadata": {},
   "source": [
    "There are 12 features, looking into each of these features:\n",
    "\n",
    "User ID: Each user has been provided a unique ID. Lets see how many unique users we have in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "35b25bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5891"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.User_ID.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f460a3c",
   "metadata": {},
   "source": [
    "There are 5891 unique users in our dataset and none of the value in this feature is null\n",
    "\n",
    "Product_ID: Each product that is available for sales has a specific/ unique product id associated with it. Lets look into the number of unique products available for sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "89caf9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3631"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.Product_ID.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a9630f",
   "metadata": {},
   "source": [
    "So there are a total of 3631 products available for sales.\n",
    "\n",
    "Gender: Gender is a categorical variables with 2 categries: Male(M) and Female(F)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f7d54bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    75.310507\n",
       "F    24.689493\n",
       "Name: Gender, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.Gender.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c088c988",
   "metadata": {},
   "source": [
    "There are no null values in this feature and Males constitute 75% of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "1e23f217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26-35    219587\n",
       "36-45    110013\n",
       "18-25     99660\n",
       "46-50     45701\n",
       "51-55     38501\n",
       "55+       21504\n",
       "0-17      15102\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.Age.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0788a598",
   "metadata": {},
   "source": [
    "The age is divide in 7 categories i.e 0-17,18-25,26-35,36-45,46-50,51-55,55+ . The bins size here is varing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55257253",
   "metadata": {},
   "source": [
    "Occupation: The Occupation number is the ID number of occupation type of each customer. We can see that around 21 different occupations exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "41b62333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.Occupation.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714501fa",
   "metadata": {},
   "source": [
    "City_Category : The city has been categorised into 3 categories i.e A,B,C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c538c5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    231173\n",
       "C    171175\n",
       "A    147720\n",
       "Name: City_Category, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.City_Category.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c84845",
   "metadata": {},
   "source": [
    "Stay_In_Current_City_Years : This depects the numbers of year from which a person is residing in that particular city. It has been divided into 5 categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "d452969a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     193821\n",
       "2     101838\n",
       "3      95285\n",
       "4+     84726\n",
       "0      74398\n",
       "Name: Stay_In_Current_City_Years, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.Stay_In_Current_City_Years.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecf0f74",
   "metadata": {},
   "source": [
    "Marital_Status: This features shows if a person is married or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "9f03953a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    324731\n",
       "1    225337\n",
       "Name: Marital_Status, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.Marital_Status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2b954d",
   "metadata": {},
   "source": [
    "The products have been categorised into three categories with represent three different features:\n",
    "\n",
    "Product_Category_1\n",
    "\n",
    "Product_Category_2\n",
    "\n",
    "Product_Category_3\n",
    "\n",
    "Purchase: This is our final feature which is our dependent variables whose value we want to predict, the purchase amount. It is a contionous variable so this makes it a regression problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e91b618",
   "metadata": {},
   "source": [
    "## Missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "58695c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets combine the data for data prep\n",
    "\n",
    "test['Purchase']=np.nan\n",
    "sales['data']='train'\n",
    "test['data']='test'\n",
    "test=test[sales.columns]\n",
    "combined=pd.concat([sales,test],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "357a18cc",
   "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",
       "      <th>data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000009</td>\n",
       "      <td>P00113442</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>17</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00288442</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000004  P00128942      M  46-50           7             B   \n",
       "1  1000009  P00113442      M  26-35          17             C   \n",
       "2  1000010  P00288442      F  36-45           1             B   \n",
       "3  1000010  P00145342      F  36-45           1             B   \n",
       "4  1000011  P00053842      F  26-35           1             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               1                   1   \n",
       "1                          0               0                   3   \n",
       "2                         4+               1                   5   \n",
       "3                         4+               1                   4   \n",
       "4                          1               0                   4   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  data  \n",
       "0                11.0                 NaN       NaN  test  \n",
       "1                 5.0                 NaN       NaN  test  \n",
       "2                14.0                 NaN       NaN  test  \n",
       "3                 9.0                 NaN       NaN  test  \n",
       "4                 5.0                12.0       NaN  test  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ac56c598",
   "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",
       "      <th>data</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>8370.0</td>\n",
       "      <td>train</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>15200.0</td>\n",
       "      <td>train</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>1422.0</td>\n",
       "      <td>train</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>1057.0</td>\n",
       "      <td>train</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>7969.0</td>\n",
       "      <td>train</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   data  \n",
       "0                 NaN                 NaN    8370.0  train  \n",
       "1                 6.0                14.0   15200.0  train  \n",
       "2                 NaN                 NaN    1422.0  train  \n",
       "3                14.0                 NaN    1057.0  train  \n",
       "4                 NaN                 NaN    7969.0  train  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "10078b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Category_3            383247\n",
       "Product_Category_2            173638\n",
       "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",
       "Purchase                           0\n",
       "data                               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d95be5",
   "metadata": {},
   "source": [
    "Here we can see that there are 2 features which contain the missing values i.e Product_Category_2 and Product_Category_3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "c6be45dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Category_3            0.696727\n",
       "Product_Category_2            0.315666\n",
       "User_ID                       0.000000\n",
       "Product_ID                    0.000000\n",
       "Gender                        0.000000\n",
       "Age                           0.000000\n",
       "Occupation                    0.000000\n",
       "City_Category                 0.000000\n",
       "Stay_In_Current_City_Years    0.000000\n",
       "Marital_Status                0.000000\n",
       "Product_Category_1            0.000000\n",
       "Purchase                      0.000000\n",
       "data                          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percent of missing data relevant to all data\n",
    "percent = (sales.isnull().sum()/sales.isnull().count()).sort_values(ascending=False)\n",
    "percent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a69227",
   "metadata": {},
   "source": [
    "The feature Product_Category_3 has 70% data missing so imputing this much data is not feasible , so it is better to drop this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "fc4fac77",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.drop('Product_Category_3',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f939053",
   "metadata": {},
   "source": [
    "The feature Product_Category_2 has 30% data missing so we can impute values into this using an appromiate method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "b2be3947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0     91317\n",
       "14.0    78834\n",
       "2.0     70498\n",
       "16.0    61687\n",
       "15.0    54114\n",
       "5.0     37165\n",
       "4.0     36705\n",
       "6.0     23575\n",
       "11.0    20230\n",
       "17.0    19104\n",
       "13.0    15054\n",
       "9.0      8177\n",
       "12.0     7801\n",
       "10.0     4420\n",
       "3.0      4123\n",
       "18.0     4027\n",
       "7.0       854\n",
       "Name: Product_Category_2, dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Product_Category_2.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7258520",
   "metadata": {},
   "source": [
    "These are almost 18 categories in which these Product_Category_2 is divided ,imputing the mean value does make sense because that gives a decimal term 9.8 which is not a product category here. So, there are 2 possible ways median or mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "3e1c7857",
   "metadata": {},
   "outputs": [],
   "source": [
    "#imputed missing values with random values in the same probability distribution as given feature already had\n",
    "\n",
    "vc = combined.Product_Category_2.value_counts(normalize = True)\n",
    "miss = combined.Product_Category_2.isna()\n",
    "combined.loc[miss, 'Product_Category_2'] = np.random.choice(vc.index, size = miss.sum(), p = vc.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "50c356da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0     133050\n",
       "14.0    115077\n",
       "2.0     102734\n",
       "16.0     89803\n",
       "15.0     78979\n",
       "5.0      54115\n",
       "4.0      53708\n",
       "6.0      34236\n",
       "11.0     29395\n",
       "17.0     27818\n",
       "13.0     22042\n",
       "9.0      11901\n",
       "12.0     11316\n",
       "10.0      6378\n",
       "3.0       6006\n",
       "18.0      5874\n",
       "7.0       1235\n",
       "Name: Product_Category_2, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Product_Category_2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ddbd2ba4",
   "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                 0\n",
       "Purchase                      233599\n",
       "data                               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43322cf1",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "b311bac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the train data part from combined dataset for eda\n",
    "\n",
    "sales_1 = combined[combined['data']=='train']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62379ab9",
   "metadata": {},
   "source": [
    "### Univariate Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "13b3c292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sales_1['Gender'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7599684",
   "metadata": {},
   "source": [
    "The graph shows that there are almost 3 times more male customers than female customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3d024e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sales_1['Age'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfba670c",
   "metadata": {},
   "source": [
    "The graph shows that the majority of the customers that purchase things during the sales season mainly belong to the age group of 26-35 and 36-45."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "82f976cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sales_1['Occupation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e34ced",
   "metadata": {},
   "source": [
    "The graph shows that top three Occupations with the majority of buyers is 4,0,7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "e1903475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sales_1['City_Category'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "769063a4",
   "metadata": {},
   "source": [
    "The graph shows that people from city B buy majorly during the sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "160c66cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sales_1['Stay_In_Current_City_Years'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126ee73b",
   "metadata": {},
   "source": [
    "The graph shows that majority people buying during sales have lived in the current city for an year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "188ba2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(sales_1['Marital_Status'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5799f8e4",
   "metadata": {},
   "source": [
    "The graphs shows that single people tend to buy more things during sales."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ca6a16",
   "metadata": {},
   "source": [
    "## Bivariate Analysis / Multivariate Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "7d08500a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avearge amount spend by different age groups\n",
    "\n",
    "data = sales_1.groupby('Age')['Purchase'].mean()\n",
    "plt.plot(data.index,data.values,marker='o',color='g')\n",
    "plt.xlabel('Age group');\n",
    "plt.ylabel('Average_Purchase amount in $');\n",
    "plt.title('Age group vs average amount spent');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "648a3978",
   "metadata": {},
   "source": [
    "The average amount spend by age group 51-55 is most during the festive season sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "6ca2d381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEXCAYAAAC+mHPKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABCfElEQVR4nO3deXxcVfnH8c83XdKmbbq3dF/ontJCm1ZQNnFDRHaQHzsoFQQtisKPH4obiwiKIggW2S2b4IYCggqo2FpaKNAkTfd939OkTZvm+f1xT9ppyHLTZnqzPO/Xa16Ze+723JnJPHPPufccmRnOOedcbTKSDsA551zj4AnDOedcLJ4wnHPOxeIJwznnXCyeMJxzzsXiCcM551wsnjBcoyHpOEmFScfhXHPVbBOGpGMl/UfSVkmbJL0laUKYd5mkfx+CGEzSkHrYzgWSZkraLmm1pJclHVsfMR6sur6WkiZKeknSlvC+zJB0OYCZ/cvMhqcsu0TSJw8iNkn6p6RbKpVfKmmhpKwD3bZrviQ9JunWpOOorD6+b5plwpCUDfwZ+AXQBegDfB8oTTKuAyHpG8DPgNuBnkB/4JfA6QewrZZxytJF0jHAP4A3gSFAV+Bq4LPp2J9Fd61+EfiGpJwQQ3fgbuBLZlZSH/s5lK/hoRQSbrP8Dmm2zKzZPYBcYEs180YCO4E9wPaK5YDPAe8C24DlwPdS1vkL8NVK23kfOKOWOAwYEp5/D3gOeAIoAvKA3FrW7xhiPLeGZR4Dbk2ZPhFYkTK9BLgxxFtK9EVd8UW6DPhnWO4KoADYDPwVGFDpOK4C5of59wOq7rWsIdZ/A/fXMH9v7MCTQDmwI2z7hoN4H74FTCf6AfU08GAoPxWYDWwB/gOMSVnnf4GF4b3KB85MmXcZ8BZwD7AJuDW8rm8CW4ENwLPVxPIKcG2lsveAs8Jreg+wLmznfWB0Ndu5PLxfRcAi4Msp8wqAU1OmW4aYxoXpo8Pxbgn7PjFl2TeA28Lx7QjHVe2+wjo3AKuBVcCX2P9zn0mUoJcBa4EHgbbVHNPhRD8oNoZ4pwKdKn2WvxVel2LgYaIfUS+H2P4GdE5Z/jSi/7Mt4bhGVvW/Wfn/iPA5BK4P78Vq4PIwbxKwG9hF9Ll8sYrjqPZ9DPt5EHgtxPwm+/+vjQjzNgGFwHmVYryf6P+gCPgvcHiY989wTMUhri/E+a78UOwHslJjfwDZ4UP3ONGv186V5l8G/LtS2YnAEURfKmPCh/uMMO884L8py44N229dSxyVE8ZO4BSgBXAHML2W9U8GyoCWNSyz94Oe+mGv9E82G+gHtAUGhrieANqFsjOABUQJoCXwbeA/lY7jz0AnojOc9cDJ1b2W1cSZRZRYPl7DMlXF/smU6QN9H1qEf67fEX1xdQDGEf1DfyTMvzTsLzOscy7QO3wevhD+EXulHHMZ8NXwerUlSkQ3h+XbAMdWE8slwFsp06OIvtAygc8As8LrXJGQe1Wznc8RfcEKOAEoYV9CuAWYWmnZueF5n/CanRJi/VSY7h7mvxFeo5xwbK1q2dfJwJqwfBZRok/93P8M+BPRmX4H4EXgjmqOaUiIJxPoTvQl+LNKn4fpREmiT3j/3gGOCuv8A/huWHZYeM8+FY7hBqLPeOvK/5uV/4+IPodlwA/CuqeEY+5c1f9cFcdR7fsY1i0Cjg8x/5zw/0P0/7icKEG3JPqMbgByUtbdBEwM86cCz1T1fXPA350Hs3JjfoQ36TGiXwpl4UPbM+UfvsYvufBBvyc8zwxv1NAwfTfwyxgxVE4Yf0uZNwrYUcv6FwJrallmvw8vVX/pXpEyPTDENTil7GXgiynTGeEfZEDKcRybMv854H/jvpZhuT5hOyNqWKaq2FMTxgG9D2HZnLD/08P0A8APKy1TCJxQzfqzU9a9DFhWaf4TwBSgby1xdCD6Iqt4bW8DHgnPTwLmEZ0BZNTx8/4HYHJ4PoToSykrTE8FbgnPbwSerLTuX4FLw/M3gB/UYV+PkJIA2HcGO4Toy7KY8Cs4zD8GWBzzmM4A3q30ebgwZfoF4IGU6a8CfwjPvwM8V+kzvZJwNkXtCWMHKT/UiJLT0ZWXrSbuat/HsG7ql3x7oh9S/Yh+mPyr0vK/Yl8SfAz4dcq8Uwg/BKo6pgN5NNv6RzMrMLPLzKwvMJro1+LPqlte0kckvS5pvaStRFUw3cK2Som+JC8Kdbr/Q/RLqq7WpDwvAdrUUv+9EehWD3Xky2spGwD8PDREbyH6UhbRl3yFyrG3r2MMm4mqmHrVcb29DuZ9MLO88LTi7wDg+opjDsfdj+hzgqRLJM1OmTea8HkIKr+mNxC9ZjMk5Um6opo4ioiqFM4PRecTfaFjZv8A7iOqdlgraUpoj/sQSZ+VND1cOLCF6Muj4vO6gKgK6fOhYf804KmU4z630nEfy/7vy37HVtO+wuu1vJp1uxOddcxK2dcrobyqY+oh6RlJKyVtA37D/q85RGf+FXZUMV3xuewNLK2YYWblIbbUz3RNNppZWcp07M98jPdxecqy24n+33oTvTcfqfTeXAgclrLuwf4f1qjZJoxUZjaXKDuPriiqYrGniM5C+plZR6J6RqXMf5zozfsEUGJm09IW8D7TiKqxzqhhmWKif8oKh1WxTFXHm1q2nKheulPKo62Z/SdGjFVt+8MLRQ3M04Cz4yxfw7br631YDtxW6ZizzOxpSQOAh4Brga5m1gmYw/6fh/1iM7M1ZnalmfUGvgz8soYrVp4G/idcBNAWeD1lO/ea2XiiM6JhRHX2+5GUSfTr+m6is+ZOwEuV4nuaKKGeDuSHJFJx3E9WOu52Zvajqo4txr5WA31T1u2X8nwD0Zd4Tsq+OppZdV9yd4R9jzGzbOCiSsdUF6uIvoArjkMhtpWhqITa/2+qU+tnvpb3ce9rJKk9UXXdKqL35s1K7017M7u6DrEdlGaZMCSNkHS9pL5huh/RP8/0sMhaoK+k1imrdQA2mdlOSROBC1K3Gb6YyoGfcGBnF3VmZluJ6qPvl3SGpCxJrcIvvh+HxWYDp0jqIukw4LoD2NWDwE0pVxJ1lHRuzHWrei2rcwNwmaRvSeoa9jVW0jM1bHtwakE9vg8PAVeFM0tJaifpc5I6ENUlG1FbDeGy39E1bAtJ51Z83ojOpoyoqqEqLxF9mf2AqHG8PGxjQoinFdEPgZ3VbKM1UfXceqBM0meBT1da5plQdjX7zi4g+tX+eUmfkdRCUhtJJ6bEXtd9PQdcLmlkOJvZewlzOK6HgHsk9QjH2EfSZ6rZVwfCxROS+lBFsqyD54DPSfpEeD2vJ7roo+JH0GzggvAanEzUNhPXhz6XqWK8j6couuy/NfBDona55UTthMMkXRz+z1uFbY2sj7jiaJYJg6j+9iPAfyUVEyWKOUQfGogax/KANZI2hLKvAD+QVET0oX+uiu0+QdQw/ps0xr4fM/sp8A2ihuj1RL9CriWqR4boS/M9ovrdV4FnD2AfvwfuBJ4JVQFziH+pa1WvZXX7+Q9R/e5JwCJJm4jq/V+qZpU7gG+H0/NvppQf9PtgZjOBK4mqDjYTNYheFublEyWkaUT/hEcQXTVUkwlEn7ftRGeqk81scTX7LiVqgP8k+3+ZZxN9wW4mqk7ZSPTLvvL6RcDXiD6jm4l+3Pyp0jKrQ/wfJeUzEb6YTgf+j32fp29RzXdFbfsys5eBe4nOkhaEfcK+S9hvDOXTw2frb8Dee20q+T5RQ+9Womq731WzXK3MrJDoDOUXRGc6nwc+b2a7wiKTQ9kWojPWP9Rh8w8Do8Lnsqr1ansfnwK+S1QVNT7sv+K1/jRRNeUqouqnO4kSdhzfAx4PcZ1Xh+PZS6ExxNUDSZcAk8ysQdw011z5+9BwhV/Dc4iuNiurbfnmRtJjRBd2fDvpWKrSXM8w6l043f4K0S9ilxB/HxoeSWdKai2pM9Ev4hc9WTROnjDqQahzXU9UPfFUSvlxirrr+NCjDtvuX902JPVPw+GklaIrhKo6lgvrYdtpex/cQfky0fuykKiu/pA10rr65VVSzjnnYvEzDOecc7E0yU7RKnTr1s0GDhyYdBjOOddozJo1a4OZVXnzZJNOGAMHDmTmzJlJh+Gcc42GpKXVzfMqKeecc7F4wnDOOReLJwznnHOxeMJwzjkXiycM55xzsXjCcO4QWrt2KtOmDeSNNzKYNm0ga9dOTTok52JLe8KQNFnSnNAlxHWh7IeS3lc0AM2rkioGpfmUpFmSPgh/T0rZzvhQvkDSvZIOtB985xKxdu1UCgsnUVq6FDBKS5dSWDjJk4ZrNNKaMCSNJuoieiLR+MqnShoK3GVmY8zsSKI+3iv6yN9A1MXwEURjKKeOZ/AA0QDrQ8Pj5HTG7lx9W7ToZsrLS/YrKy8vYdGimxOKyLm6SfcZxkhgupmVhN4p3wTONLNtKctUDEaDmb1rZqtCeR7REKWZknoB2WY2zaLOr56g5lHmnGtwSkuX1ancuYYm3QljDnC8pK6h2+lTCMMPSrpN0nKiwUFuqWLds4kGeC8lGmd3Rcq8FVQz9q6kSZJmSpq5fv36ejwU5w5OZmbVg9a1bNkF7wTUNQZpTRhmVkDU//1rRIO7vweUhXk3m1k/ogHur01dT9FQoHcSdYsMVY/bW+V/mJlNMbNcM8vt3r3K7lCcS0RmZlWjY2ZQVraR2bNPoLg4/5DH5FxdpL3R28weNrNxZnY80ZCD8yst8hTR2QQAYezg3wOXmNnCULyC/QeS70s0RKFzjcLq1Y+ybdubdOlyGpmZAwCRmTmAESMeZ/jwX1NcPIeZM49k0aKb2bNnR9LhOleltHc+KKmHma0Lg/2cBRwjaaiZVSSO04C5YdlORGP13mRme8dINrPVkookHQ38F7iEaCxe5xq87dvfY/78r9Cp00kcccTvkFp8aJmuXU9j4cJvsmzZ7axb9wzDhv2SLl0+k0C0zlXvUNyH8YKkfOBF4Boz2wz8KFxq+z7RoOaTw7LXAkOA74RLbmdL6hHmXQ38mmjA+IXAy4cgducOSlnZVvLyzqFly86MGvVUlckCoHXr7owc+Thjx/4DqSXvv38yeXnnU1q6+hBH7Fz1mvSIe7m5uebdm7ukmBl5eWexYcOLHHnkG3TqdGys9crLS1m27E6WLr2djIxMBg++nd69r6o22ThXnyTNMrPcqub5nd7Opcny5T9hw4Y/cPjhP46dLAAyMjIZOPAWJkz4gA4dJjB//rW8885HKSp6N43ROlc7TxjOpcGWLf9i0aL/pVu3s+jb9+sHtI2srKGMHfsaI0dOZefOJcyalcuCBd+grGx7PUfrXDyeMJyrZ6Wla8jP/wJt2w5ixIhHOJhebCTRs+cFTJw4l169rmTFint4++2RrF//h/oL2LmYPGE4V4/Ky8soKPgfysq2kJPzAi1bdqyX7bZq1Znhwx/kqKP+Q8uWncnLO5MPPjidnTv9LnF36HjCcK4eLVnyHbZseYNhwx6gffsx9b79jh2PYfz4WQwefBebN/+NGTNGsmzZ3ZSX7673fTlXmScM5+rJhg0vsmzZj+jV60oOO+zStO0nI6MV/ft/k4kT8+nc+RMsWvQtZs3KZevW6Wnbp3PgCcO5erFjxyLmzr2E9u2PYsiQew/JPtu0GcDo0X8kJ+d37N69kXff/Sjz5l3N7t1bDsn+XfPjCcO5g7Rnz07y8s4FICfneVq0aHPI9i2J7t3PZOLEAvr2ncyqVVOYMWMEa9c+7R0aunrnCcO5g7RgwdfYvv0dRox4grZtq+pgMP1atuzAkCH3MH7827Rp04+Cggt4//3PUFKyIJF4XNPkCcO5g7BmzeOsXv0Q/fv/L926fT7pcOjQYRzjxk1n6ND72LZtOm+/PZolS35IeXlp0qG5JsAThnMHaPv295k372o6dfo4Awf+MOlw9pJa0KfPNUycOJdu3U5nyZJbePvtsWze/EbSoblGzhOGcwdgX6eCnRg58ikyMtLe8XOdZWb2JifnWY444mXMdvHeex+noOAydu3ygcXcgfGE4VwdmRlz517Bjh2LGDXqWTIzD0s6pBp17XoyEybMoX//m1i3biozZoxg9eqHMStPOjTXyHjCcK6OVqy4hw0bfsfgwT+iU6fjkg4nlhYtshg8+HZyc2fTrl0OhYVfYvbsEykuzks6NNeIeMJwrg62bPk3CxfeQLduZ9Kv3/VJh1Nn7drlcOSRbzB8+MMUF+eFUf7+jz17SpIOzTUCnjCci2nXrrXk558XOhV89KA6FUySlEGvXlcwceJcevS4kGXL7uDtt0ezceMrSYfmGrg6JwxJrSW1S0cwzjVUZnvIz7+AsrLN5OQ8X2+dCiYpGuXvMcaOfR2pNR988Fny8r7go/y5atWaMCRNljQ8PD8BWA7Ml/SVdAfnXEOxePEtbNnyD4YO/SXt249NOpx61bnziUyY8B4DB/6QDRv+yIwZI1i58n7M9iQdmmtg4pxhXEo0jjbAt4HTgKFEY2w71+Rt2PBnli27ncMO+yK9el2edDhpEY3y920mTJhDdvZHwih/x/gof24/NSYMSd8FegM3S7oVOBL4DPBNoIOkWyQdX8s2JkuaIylP0nWh7IeS3pc0W9KrknqnLH+TpAWSCiV9JqV8vKQPwrx71VgrkF2jsmPHYubOvZj27Y9k6NBfJB1O2mVlDWHMmL8ycuRT7Ny5LIzy93XKyoqSDs01AKqtgzJJLwEzgW5AhpldJSkDeMvMjqll3dHAM8BEYBfwCtGZyVoz2xaW+RowKmx3FPB0WL438DdgmJntkTQDmAxMB14C7jWzl2vaf25urs2cObPG43OuOnv27OTdd49lx44F5Oa+k1g/UUnZvXsLixffxKpVvyIzsw9DhtxLt25nNNrGfhePpFlmllvVvDhVUl8EMoGNwLdC2TDg1zHWHQlMN7MSMysD3gTOrEgWQTugImudDjxjZqVmtpioKmyipF5AtplNsyjDPQGcEWP/zh2wBQuuY/v2WYwcmVyngklq1aoTw4Y9EEb560Je3lnMmXM6O3cuTTo0l5BaE4aZrTazG83sO2ZWFMrmmtnDMbY/BzheUldJWcApQD8ASbdJWg5cCNwSlu9D1KheYUUo6xOeVy7/EEmTJM2UNHP9eu8CwR2YNWueZPXqX9Gv3w1063Za0uEkqmPHoxk/fhaHH343mzf/nRkzRrFs2V0+yl8zlNb7MMysALgTeI2oOuo9oCzMu9nM+gFTgWvDKlWd61oN5VXtc4qZ5ZpZbvfu3Q/yCFxztH37B8yb92U6djyBQYNuSzqcBiEjoyX9+l3PxIkFdO78SRYtuiGM8jct6dDcIZT2G/fM7GEzG2dmxwObgPmVFnkKODs8X0E4Awn6AqtCed8qyp2rV2Vl28jLO5uWLTsyatQzDbJTwSS1adOfI474Izk5v6esbBPvvvsxCguvYvfuzUmH5g6BtCcMST3C3/7AWcDTkoamLHIaMDc8/xNwvqRMSYOILt+dYWargSJJR4eroy4B/pju2F3zYmYUFn6x0XQqmKTu3c9gwoR8+va9jtWrHwqj/D3lo/w1cbF+PknKJDoLGJi6jpn9IMbqL0jqCuwGrjGzzZJ+HW4GLAeWAleF7eVJeg7IJ6q6usb23T10NfAY0BZ4OTycqzcrVvyc9eufZ/DgO+nUqcarxR0Vo/z9lJ49L2bevKsoKLiQNWseZejQB8jKGpJ0eC4Nar2sFkDSK8BWYBaw9/ZPM/tJ+kI7eH5ZrYtr69a3mD37RLp0+RyjR//eLx2tI7M9rFr1KxYtuony8lIGDLiZ/v1vICMjM+nQXB3VdFlt3IQxx8xG13tkaeYJw8Wxa9c6Zs4cR0ZGG8aPn0mrVp2SDqnRKi1dzYIFX2f9+mdp23Y4w4Y9QOfOH086LFcHB3sfBsB/JB1RjzE51yDs61RwIzk5z3uyOEiZmb3IyXmGMWNewWw37713EgUFl/oof01E3IRxLDArdNfxfuii4/10BubcobBkyffYsuXvDB16Px06HJl0OE1Gly6fCaP83cy6dU/7KH9NRNxrBj+b1iicS8DGjS+xdOmtHHbY5fTqdUXS4TQ5LVq0ZfDgW+nZ8wLmzbuawsIvsWbNYwwb9iDt2uUkHZ47ALV1PpgdnhZV83CuUdqxYwkFBRfRrt1Yhg69P+lwmrR27UaFUf4eobi4IIzyd5OP8tcI1VYl9VT4O4uoA8JZKQ9vTXaNUnl5Kfn552K2h5yc52nRom3SITV5kujV63ImTpxLz54XsWzZj8Iof351fGNSY8Iws1PD30FmNjj8rXg0v97YXJOwYMHXKSqayYgRj/v9AodY69bdGDHiUY488g0yMjL54INTyMs7j9JS77ihMfAxvV2zsnbtVFateoB+/b5F9+5nJB1Os9Wp0wnk5s5m0KBb2bjxRWbMGMGKFff5KH8NnCcM12wUF+dRWDiJjh2PY9Cg25MOp9nLyMhkwICbwyh/x7BgwVd5552jKSp6J+nQXDU8YbhmoaysiDlzzqZFiw6MGvWsdyrYgLRtezhjxrzCyJFPs3PncmbNmsD8+df5KH8NUKyEIenJOGXONURRp4JfYseO+Ywa9QyZmb2SDslVIomePc9n4sS59O79ZVauvJcZM0Yyf/51TJs2gDfeyGDatIGsXTs16VCbtbhnGPtdNC2pBTC+/sNxrv6tXPkL1q9/jsGDb6dz5xOTDsfVIBrl75eMGzcNyGDlyp9TWroMMEpLl1JYOMmTRoJquw/jJklFwBhJ28KjCFiHdy/uGoGtW6excOH1dO16Gv36fav2FVyDkJ39kSo7gCwvL2HevGvYuPEv7Nq1LoHImrcaK3LN7A7gDkl3mNlNhygm5+rFrl3ryc8/j8zMfowY8RiSN9k1JqWly6ss37NnKx98cCoAmZkDyM6eSIcOE8jOnkj79uNp2bL9oQyzWYnV8mdmN0nqAwxg//Ew/pmuwJw7GGZ7KCi4kF271jNu3DRateqcdEiujjIz+1NaurSK8n6MHPkbioreZtu2GRQVvc369b8Nc0VW1qj9kki7dkeQkdH60AbfRMUdQOlHwPlEAxtVXChtgCcM1yAtWfIDNm9+jWHDHqJDh6OSDscdgMGDb6OwcBLl5fu6EMnIyGLw4Dvo1On4/Qa52rVrPUVFb+9NIhs3/pk1ax4FQMqkffsjyc6eQIcOE8nOnkjbtkP9jPMAxB0PoxAYY2al6Q+p/vh4GM3Txo2v8MEHp3DYYZcyfPgjPhhSI7Z27VQWLbqZ0tJlZGb2Z/Dg2+jZ88Ja1zMzdu5cGpLIjHAmMovy8mIAWrTIDmcgURLp0GECmZl9/LNC/Qyg9DJwrpltr+/g0skTRvOzc+dSZs4cR2ZmX8aNm0aLFllJh+QaCLM9FBcXpCSRtykufg+zMgBat+61txorSiK5zbIqs6aEEffupRJgtqS/A3vPMszsa/UQn3P1ory8lLy88zArC50KerJw+0gtaN9+NO3bj6ZXr8sB2LNnJ9u3z94viWzc+Ke967RtOzQliUygffujmnVnlXETxp/Co84kTQauBAQ8ZGY/k3QX8HlgF7AQuNzMtkhqBfwaGBdieyJcqYWk8cBjQFvgJWCyxTk9cs3GggXXU1Q0g5ycF8jKGpp0OK4RaNGiDR07Hk3HjkfvLdu9ewvbt8/a26C+ZcubrFsXddwttaRduyP2SyJZWaOaTc8BsaqkDnjj0mjgGWAiUXJ4BbgaGAT8w8zKJN0JYGY3SroAOM3MzpeURdTIfqKZLZE0A5gMTCdKGPeaWY19I3uVVPOxdu1TFBRcSN++1zNkyN1Jh+OamNLSVftdlVVU9DZlZVuAqCG+Q4dxe9tCsrMn0qbNoEbbHnLQVVKSFhNdFbWfGF2cjwSmm1lJ2M6bwJlm9uOUZaYD51RsEmgnqSXRmcQuYJukXkC2mU0L23kCOAPwzvQdxcX5FBZeSceOxzJ48B1Jh+OaoMzM3mRmnk63bqcDYFbOjh0LUxrU32blyvupuC6oZcuu+zWoZ2dPoHXrnkkeQr2Iex6Vmm3aAOcCXWKsNwe4TVJXYAdwCh8eeOkK4Nnw/HngdGA1kAV83cw2ScoFVqSsswLoU9UOJU0CJgH0798/RoiuMSsrKyIv72xatGgfOhVslXRIrhmQMsjKGkpW1tC9V22Vl++muHjO3raQoqIZbNp0KxCNY56Z2T+lQX0CHTqMp2XLDgkeRd3FvXFvY6Win0n6N3BLLesVhCqn14DtwHtAWcV8STeH6YrOYSYS3efRG+gM/EvS34jaPz60+Wr2OQWYAlGVVM1H5hozM2PevEmUlMxj7Ni/kZnZO+mQXDOWkdGKDh2OokOHo+jd+8sA7NlTTFHRO/slkfXrnw9riKyskXvbQjp0mEj79mMa9E2GcaukxqVMZhCdccRKjWb2MPBw2M7thDMFSZcCpwKfSGm8vgB4xcx2A+skvRX29S+gb8pm+wI+RFczt3Ll/axb9wyDBt1O584fTzoc5z6kRYt2dOp0HJ06Hbe3bNeuDZVuMvwLa9Y8BoDUOtxkuC+JZGUNazA3GcatkvpJyvMyYAlwXpwVJfUws3WS+gNnAcdIOhm4ETihon0jWAacJOk3RFVSRwM/M7PVkookHQ38F7gE+EXM2F0TtHXrdBYu/AZdu55K//43Jh2Oc7G1bt2Nrl0/S9eunwWiM+XS0mV720K2bZvBmjWPsXLlfUDFTYa5+90jUt1Nhgd6o2Ncab1KCkDSv4CuwG7gG2b2d0kLgEygoqprupldJak98Cgwiqga6lEzuytsJ5d9l9W+DHy1tstq/SqppmnXrg3MmjUOqQXjx7/TLG+uck2b2R5KSubul0SKi98nqnyB1q0P2++qrA4dctm06eUqu1IZPnxKnZJGfdzp3RH4LlDRecubwA/MbGvsKBLgCaPpMdvD+++fwpYtbzBu3H/o0MGHZXHNw549Oykufm9vW0hR0duUlMzdO19qufeu9VSZmQM45pglsfdTH3d6P0J0xVNFNdTFRGcCZ8WOwrl6sHTprWze/CrDhv3Kk4VrVlq0aEN29kfIzv7I3rKysq0UFUU3GS5eXPUIFNEAVPUjbsI43MzOTpn+vqTZ9RaFczFs2vRXliz5Pj17XkKvXlcmHY5ziWvZsiOdO59E584nsWrVg9V0B19/txfEbXrfIenYiglJHyO6r8K5Q2LnzmXk519Iu3ajGTbsgUZ7F61z6TJ48G1kZOzff1rUHfxt9baPuGcYVwOPh7YMAZuAy+otCudqUF6+K3QquMs7FXSuGhUN2+m8SirujXuzgbGSssP0tnqLwLlaLFz4TYqK/suoUb8lK2tY0uE412D17HlhvSaIyuLeuNeJ6N6HgUDLiuoA797cpdvatc+wcuUv6Nv36/TocU7tKzjn0iZuldRLRJ0EfkBFxyjOpVlxcQGFhV8iO/tjDB58Z9LhONfsxU0YbczsG2mNxLkUZWXbQ6eC7cjJ8U4FnWsI4l4l9aSkKyX1ktSl4pHWyFyzta9TwUJGjXqKzMwqOyZ2zh1icc8wdgF3ATezr5dYA2obD8O5Olu16gHWrXuaQYNupXPnTyQdjnMuiJswvgEMMbMN6QzGuW3bZrBgwXV06fI5+vev+s5V51wy4lZJ5QEltS7l3EHYvXsjeXnnkpnZh5Ejn2gwXTo75yJxzzD2ALMlvQ6UVhT6ZbWuvpiVU1BwEbt2reGoo96iVStvInOuoYmbMP4QHs6lxdKlt7Fp0ysMG/Yg2dlVdpTpnEtY3Du9H093IK752rTpNZYs+S49e15Er16Tkg7HOVeNuHd6DwXuIBrYqE1FuZn5VVLuoOzcuZyCggvIyhrFsGEPeqeCzjVgcVsVHwUeIBqe9ePAE8CT6QrKNQ/l5bvIzz+P8vJSRo9+gRYt2iUdknOuBnETRlsz+zvRCH1Lzex7wEnpC8s1BwsXfott26YzfPgjZGUNTzoc51wt4jZ671R0jeN8SdcCK4Ee6QvLNXXr1j3HypX30rfvdd6poHONRNwzjOuALOBrwHjgIuDSOCtKmixpjqQ8SdeFsrskzZX0vqTfh95wK5YfI2laWP4DSW1C+fgwvUDSvfLK7karuHguhYVfJDv7GO9U0LlGJFbCMLO3zWy7ma0ws8vN7Gwzm17bepJGA1cCE4GxwKmhAf01YLSZjQHmATeF5VsCvwGuMrMc4ERgd9jcA8AkYGh4nBz/MF1DsWdPMXl555CR0YZRo54jI6N10iE552JK9620I4HpZlZiZmXAm8CZZvZqmIao2/S+4fmngffN7D0AM9toZnsk9QKyzWyamRlRo/sZaY7d1TMzo7Dwy5SU5DNy5NO0adO39pWccw1GuhPGHOB4SV0lZQGnAP0qLXMF8HJ4PgwwSX+V9I6kG0J5H2BFyjorQtmHSJokaaakmevXr6+3A3EHb9WqX7Fu3VQGDvwBXbp8MulwnHN1FLfR+4CYWYGkO4mqoLYD7xFdmguApJvD9NSUeI4FJhD1XfV3SbOAqoaEtSrKMLMpwBSA3NzcKpdxh962bTNZsGAyXbp8lgED/i/pcJxzByDWGYakYZL+LmlOmB4j6dtx1jWzh81snJkdD2wC5odtXAqcClwYqpkgOnN408w2mFkJ0Uh/40J5av1FX2BVnP275EWdCp5D69aHMXLkk96poHONVNz/3IeIGqZ3A5jZ+8D5cVaU1CP87Q+cBTwt6WTgRuC0kBgq/BUYIykrNICfAOSb2WqgSNLR4eqoS4A/xozdJSjqVPBidu1aTU7O87Rq1TXpkJxzByhulVSWmc2odCVrWXULV/KCpK5EyeYaM9ss6T4gE3gtbHO6mV0V5v0UeJuoyuklM/tL2M7VwGNAW6I2j5dxDd7SpbezadPLDB36S7KzJyQdjnPuIMRNGBskHU5oN5B0DrA6zopmdlwVZUNqWP43RJfWVi6fCYyOGa9rADZt+htLltxCjx4X0rv3VUmH45w7SHETxjVEDckjJK0EFhPdvOdclUpLV4ZOBUcyfPivvFNB55qAuN2bLwI+KakdkGFmRekNyzVm5eW7ycs7j/LyHeTkeKeCzjUVca+Smiwpm+hS13vCPRKfTm9orrFatOgGtm37D8OHP0y7diOSDsc5V0/iXiV1hZltI7oTuwdwOfCjtEXlGq11637LihU/o0+fr9Gjx3lJh+Ocq0dxE0ZFBfQpwKOh6w6vlHb7KSkppLDwCrKzj+bww+9KOhznXD2L2+g9S9KrwCDgJkkdgPL0heUai7Vrp7Jo0c2Uli5DaomU6Z0KOtdExU0YXwSOBBaZWUm4r+LytEXlGoW1a6dSWDiJ8vLo3kuz3UAGW7f+kzZtLkw2OOdcvYvbvXk50aW0wyQdD+QAndIYl2sEFi26eW+yqGBWyqJFNycUkXMunWKdYUj6EjCZqA+n2cDRwDR8mNZmrbR0WZ3KnXONW9xG78lEPcguNbOPA0cB3nd4M5eZWWUP82Rm9j/EkTjnDoW4CWOnme0EkJRpZnOB4ekLyzV0ZuW0bPnhjgQzMrIYPPi2BCJyzqVb3ISxIoy7/QeiDgP/iHcv3qwtXXo7xcXv0bPnZWRmDgBEZuYAhg+fQs+e3uDtXFMUt2uQM8PT70l6HegIvJK2qFyDtmnTa3s7FRwx4hHvJ8q5ZiL2iHuSWgA9ia6WAjgM8NbNZmbnzuWhU8FR3qmgc81M3Kukvgp8F1jLvhv2DBiTprhcA1Revov8/PMoLy9l9GjvVNC55ibuGcZkYLiZbUxnMK5hW7jwW2zbNp1Ro35LVpZf8+BccxO30Xs5sDWdgbiGbe3aZ1i58l769v06PXqck3Q4zrkE1HiGIekb4eki4A1JfwFKK+ab2U/TGJtrIIqLCygs/BLZ2R9j8OA7kw7HOZeQ2qqkOoS/y8KjdXi4ZqKsbDt5eWfTokU7cnKeJSOjVdIhOecSUmPCMLPvH+wOJE0GriTqDv0hM/uZpLuAzwO7gIXA5Wa2JWWd/kA+8D0zuzuUjQceA9oCLwGTzcwONj5XPTNj3rwrKSkpZOzY16q9s9s51zzEHXHvtXDjXsV0Z0l/jbHeaKJkMREYC5wqaSjwGjDazMYA84CbKq16D/BypbIHgEnA0PA4OU7s7sCtXHk/69Y9w6BBt9K5s3cb5lxzF7fRu3vqGYCZbSYaea82I4HpZlZiZmXAm8CZZvZqmAaYTtSpIQCSziBqM8lLKesFZJvZtHBW8QRwRszY3QHYunU6Cxd+g65dP0///jcmHY5zrgGImzD2hGoiACQNILoPozZzgOMldZWURTRiX79Ky1xBOJuQ1A64EahcFdYHWJEyvSKUuTTYtWs9+fnnkpnZlxEjHkeK+zFxzjVlce/D+D/g35LeDNPHE1UP1cjMCiTdSVQFtR14D6g4s0DSzWF6aij6PnCPmW2vdAdxVbcTV5mwJE2qiK1/f+81ta7M9lBQcAG7dq1n3LhptGrVOemQnHMNRK0JQ9HPy47AOKJxMAR83cw2xNmBmT0MPBy2dTvhTEHSpcCpwCdSGq8/Apwj6cdEAzSVS9oJvEBKtVV4XmXnh2Y2BZgCkJub643idbRkyffZvPlvDB/+azp0OCrpcJxzDUitCcPMyiVda2bPAX+u6w4k9TCzdaFK6yzgGEknE1U9nWBme4dsM7PjUtb7HrDdzO4L00WSjgb+C1wC/KKusbiabdz4EkuX/pDDDrucXr2+mHQ4zrkGJm6V1GuSvgk8CxRXFJrZphjrvhDGAN8NXGNmmyXdB2SG7ULUMH5VLdu5mn2X1b7Mh6+icgdhx44lFBRcRLt2Yxk69P6kw3HONUCKcyuDpMVVFJuZDa7/kOpPbm6uzZw5M+kwGrzy8lLeffdYSkrmM378TLKyhiQdknMuIZJmmVluVfPijocxqH5Dcg3JggXXUVQ0k9Gj/+DJwjlXrbjdm19SVbmZPVG/4bhDbc2aJ1m16kH69buRbt1OTzoc51wDFrcNY0LK8zbAJ4B3iG6gc43U9u0fMG/el+nU6UQGDbo16XCccw1c3Cqpr6ZOS+oIPJmWiNwhUVa2lby8s2nZshMjRz5NRkbswRedc83UgX5LlBD15+QaITNj7twr2LFjEUce+TqZmYclHZJzrhGI24bxIvvurM4ARgHPpSsol14rVtzDhg2/4/DD76ZTp+NqX8E554h/hnF3yvMyYKmZrahuYddwbdnyLxYuvIFu3c6ib99v1L6Cc84FtY241wa4ChgCfAA8nNLLrGtkSkvXkJ//Bdq2HcyIEY9Sqb8u55yrUW3dkD4O5BIli88CP0l7RC4tysvLyM8/n7KyLeTkvEDLltlJh+Sca2Rqq5IaZWZHAEh6GJiR/pBcOixe/G22bn2TESOeoH37I5IOxznXCNV2hrG74olXRTVeGzb8keXL76RXry9z2GEXJx2Oc66Rqu0MY6ykbeG5gLZhWkR9SXm9RgO3Y8dCCgoupX378QwZ8rOkw3HONWI1JgwzaxFnI5I6h2FbXQOyZ88O5sw5GymDnJznadGiTdIhOecasfoae/Pv9bQdV4/mz7+W4uL3GDnyN7RtOzDpcJxzjVx9JQy/PrOBWb36YdaseYQBA75D166nJB2Oc64JqK+E4UOhNiBFRe8yb941dO78KQYO/G7S4Tjnmoj6Shiugdi9ezN5eWfTunV3Ro6cihSrGco552pVX12UepVUA2BWzty5l1JauoIjj/wnrVt3Tzok51wTEvsMQ9Kxki4Pz7tLSh2F7xP1Hpmrs2XLfszGjS9y+OE/oWPHo5MOxznXxMRKGJK+C9wI3BSKWgG/qZhvZpvqPzRXF5s3v87ixTfTo8f59OlzbdLhOOeaoLhnGGcCpwHFAGa2CugQZ0VJkyXNkZQn6bpQdpekuZLel/R7SZ1C+ackzZL0Qfh7Usp2xofyBZLulfect1dp6Ury888nK2s4w4Y95J0KOufSIm7C2GVmRrgaSlK7OCtJGg1cCUwExgKnShoKvAaMNrMxwDz2nblsAD4f+q+6lP1H9XsAmEQ0cNNQ4OSYsTdp5eW7ycv7Anv2FIdOBdsnHZJzromKmzCek/QroJOkK4G/AQ/FWG8kMN3MSkJfVG8CZ5rZqyl9U00H+gKY2bvh7AUgD2gjKVNSLyDbzKaFxPUEcEbM2Ju0RYtuZNu2txg+/Ne0azcy6XCcc01Y3DG975b0KWAbMBy4xcxei7HqHOA2SV2BHcApwMxKy1wBPFvFumcD75pZqaQ+QOqATSuAPlXtUNIkojMR+vfvHyPExmvduudZseIe+vT5Kj17np90OM65Ji72ZbUhQcRJEqnrFEi6M6y3HXiPaMQ+ACTdHKanpq4nKQe4E/h0RVFVm69mn1OAKQC5ublN9obCkpJCCgsvJzv7aA4//O7aV3DOuYMU9yqpIknbKj2WhwbrwTWta2YPm9k4Mzse2ATMD9u8FDgVuDBUM1Xsqy/we+ASM1sYilcQqq2CvsAqmqk9e4qZM+dsMjLaMGrUc2RktE46JOdcMxD3DOOnRF/QTxH92j8fOAwoBB4BTqxuRUk9zGydpP7AWcAxkk4mukz3BDMrSVm2E/AX4CYze6ui3MxWh6R1NPBf4BLgF3EPsikxM+bNu4qSknzGjPkrbdr0Szok51wzEbfR+2Qz+5WZFZnZtlDtc4qZPQt0rmXdFyTlAy8C14Ru0O8juiz3NUmzJT0Ylr2WaPzw74Ty2ZJ6hHlXA78GFgALgZdjH2UTsmrVg6xd+xsGDvw+Xbp8KulwnHPNSNwzjHJJ5wHPh+lzUubV2E5gZsdVUTakmmVvBW6tZt5MYHSsaJuobdtmsGDBdXTp8lkGDLg56XCcc81M3DOMC4GLgXXA2vD8Ikltic4KXJrt3r2RvLxzad26FyNH/gbJ+410zh1acS+rXQR8vprZ/66/cFxVzMopKLiIXbvWcNRRb9GqVZekQ3LONUOxEoakNsAXgRxg7zifZnZFmuJyKZYuvZVNm15h2LAHyc7OTToc51wzFbde40miq6I+Q3S3dl+gKF1BuX02bforS5Z8j549L6ZXr0lJh+Oca8biJowhZvYdoNjMHgc+BxyRvrAcwM6dy8jPv5B27UYzbNiD3qmgcy5RcRPG7vB3S+hQsCMwMC0ROQDKy0vJyzsXs13k5DxPixZZSYfknGvm4l5WO0VSZ+DbwJ+A9sB30haVY8GC6ykqmkFOzgtkZQ1LOhznnKs9YSi6fnNbuOHun0CNXYG4g7d27VOsWnU/ffteT/fuZyUdjnPOATGqpMysHL/X4pApLs6jsPBKOnY8lsGD70g6HOec2ytuG8Zrkr4pqZ+kLhWPtEbWDJWVFTFnztm0aNGBUaOeJSOjVdIhOefcXnHbMCrut7gmpczw6ql6Y2YUFn6RHTvmM3bs38nM7J10SM45t5+4d3oPSncgzd3Klfeyfv1vGTz4R3TufGLS4Tjn3IfEHQ8jS9K3JU0J00MlnZre0JqPrVv/w8KF36Rr19Pp1++GpMNxzrkqxW3DeBTYBXw0TK+gml5lXd3s2rWOvLxzycwcwIgRj/nNec65BituwjjczH5MuIHPzHZQ9bCprg7M9pCffwFlZZvIyXmeVq06JR2Sc85VK26j967QlbkBSDocKE1bVM3E4sXfZcuWvzN8+CN06HBk0uE451yN4iaM7wGvAP0kTQU+BlyWppiahQ0b/syyZbdx2GFfpFevy5MOxznnahX3KqlXJc0CjiaqippsZhvSGlkTtmPHYubOvZj27Y9i6NBmOTS5c64Rijsexp+Ap4E/mVlxekNq2vbs2UleXjTCbdSpYNuEI3LOuXjiNnr/BDgOyJf0W0nnhEGVaiVpsqQ5kvIkXRfK7pI0V9L7kn4vqVPK8jdJWiCpUNJnUsrHS/ogzLtXjfRyogULvsb27e8wYsQTtG3r9z065xqPWAnDzN40s68Q3dk9BTiPaHzvGoWu0K8EJgJjgVMlDQVeA0ab2RhgHnBTWH4UcD7RyH4nA7+U1CJs7gFgEjA0PE6OeYwNxurVj7F69UP0738T3bpVN+Ktc841THHPMAhXSZ0NXAVMAB6PsdpIYLqZlZhZGdFofWea2athGmA60Qh+AKcDz5hZqZktBhYAEyX1ArLNbJqZGfAEcEbc2BuC7dvfY/78q+nU6eMMHPiDpMNxzrk6i3un97NAAXAScD/RfRlfjbHqHOB4SV0lZQGnAP0qLXMF8HJ43gdYnjJvRSjrE55XLq8q1kmSZkqauX79+hghpt/u3VuYM+dsWrbswqhRT5OREffiNOecazjqcqf34WZ2lZn9AzhG0v21rWRmBcCdRFVQrwDvARVnFki6OUxPrSiqajM1lFe1zylmlmtmud27d68txLSLOhW8nNLSpeTkPEfr1j2TDsk55w5I3DaMV4AjJN0paQlRtyBzY677sJmNM7PjgU3AfABJlwKnAheGaiaIzhxSz0D6AqtCed8qyhu85cvvZsOGPzB48I/p2PFjSYfjnHMHrMaEIWmYpFskFQD3EX1xy8w+bmaxbiCQ1CP87Q+cBTwt6WTgRuA0MytJWfxPwPmSMiUNImrcnmFmq4EiSUeHq6MuAf5Yt0M99LZseZNFi26ie/dz6Nv3uqTDcc65g1JbZfpc4F/A581sAYCkr9dxHy9I6krUD9U1ZrZZ0n1AJtHATBA1jF9lZnmSngPyiaqqrjGzPWE7VwOPAW2J2jxepgErLV1NXt4XaNv2cIYPf9g7FXTONXq1JYyziS5zfV3SK8Az1LHTQTM7roqyITUsfxtwWxXlM4HRddl3UsrLy8jPP589e4oYO/ZvtGyZnXRIzjl30GqskjKz35vZF4ARwBvA14Gekh6Q9OlDEF+jtHjx/7F16z8ZPnwK7ds3ihznnHO1itvoXWxmU83sVKIG59nA/6YzsMZq/frfs3z5XfTufTU9e16YdDjOOVdvYt+4V8HMNpnZr8zspHQE1JiVlMxn7tzL6NBhAkOG3JN0OM45V6/qnDBc1fbsKSEv72ykluTk/JaMjMykQ3LOuXrltxzXAzNj3ryvUFw8hyOOeIk2bQYkHZJzztU7P8OoB6tX/5q1ax9nwIBb6Nq10fWJ6JxzsXjCOEhFRbOYP/9aOnf+NAMHfifpcJxzLm08YRyE3bs3kZd3Dq1b92TkyKns64ndOeeaHm/DOEBm5RQUXExp6UqOOupftG7dLemQnHMurTxhHKBly+5g06aXGDr0PrKzP5J0OM45l3ZeJXUANm36G4sX30KPHhfQu/dXkg7HOecOCU8YdbRz5woKCv6HrKwRDB8+xTsVdM41G54w6qC8fBf5+edRXr6TnJwXaNGiXdIhOefcIeNtGHWwcOENbNs2jVGjnqVduxFJh+Occ4eUn2HEtG7ds6xc+XP69JlMjx7nJR2Oc84dcp4wYiguLqCw8EtkZ3+Uww//cdLhOOdcIjxh1KKsbDt5eWeTkdGWnJznyMhonXRIzjmXCG/DqEHUqeAkSkoKGTv2VTIz+yQdknPOJcYTRg1Wrfol69Y9zaBBt9G58yeSDsc55xKV9iopSZMlzZGUJ+m6UHZumC6XlJuybCtJj0v6QFKBpJtS5o0P5Qsk3as03QCxdu1Upk0byBtvZDB//rW0a3ck/fv74ILOOZfWhCFpNHAlMBEYC5wqaSgwBzgL+GelVc4FMs3sCGA88GVJA8O8B4BJwNDwqPd+xNeunUph4SRKS5cCBsCOHYWsW/d0fe/KOecanXSfYYwEpptZiZmVAW8CZ5pZgZkVVrG8Ae0ktQTaAruAbZJ6AdlmNs3MDHgCOKO+g1206GbKy0v2Kysv38GiRTfX966cc67RSXfCmAMcL6mrpCzgFKBfDcs/DxQDq4FlwN1mtgnoA6xIWW5FKPsQSZMkzZQ0c/369XUKtrR0WZ3KnXOuOUlrwjCzAuBO4DXgFeA9oKyGVSYCe4DewCDgekmDgaraK6yafU4xs1wzy+3evXud4s3M7F+ncueca07S3uhtZg+b2TgzOx7YBMyvYfELgFfMbLeZrQPeAnKJzij6pizXF1hV37EOHnwbGRlZ+5VlZGQxePBt9b0r55xrdA7FVVI9wt/+RA3dNbUgLwNOUqQdcDQw18xWA0WSjg5XR10C/LG+Y+3Z80KGD59CZuYAQGRmDmD48Cn07Hlhfe/KOecanUNxH8YLkroCu4FrzGyzpDOBXwDdgb9Imm1mnwHuBx4lavsQ8KiZvR+2czXwGFFj+MvhUe969rzQE4RzzlVB0UVHTVNubq7NnDkz6TCcc67RkDTLzHKrmud9STnnnIvFE4ZzzrlYPGE455yLxROGc865WJp0o7ek9cDSA1y9G7ChHsNp6vz1qht/verGX6+6OZjXa4CZVXnXc5NOGAdD0szqrhRwH+avV93461U3/nrVTbpeL6+Scs45F4snDOecc7F4wqjelKQDaGT89aobf73qxl+vuknL6+VtGM4552LxMwznnHOxeMJwzjkXiyeMSiSdLKlQ0gJJ/5t0PA2dpEckrZM0J+lYGgNJ/SS9LqlAUp6kyUnH1JBJaiNphqT3wuv1/aRjamgktZD0rqQ/p3tfnjBSSGpB1MX6Z4FRwP9IGpVsVA3eY8DJSQfRiJQB15vZSKLxXq7xz1iNSoGTzGwscCRwsqSjkw2pwZkMFFQ1Q9KS+tyRJ4z9TQQWmNkiM9sFPAOcnnBMDZqZ/ZNoJEUXg5mtNrN3wvMion/0Ksend2CR7WGyVXj4lTqBpL7A54BfH4r9ecLYXx9gecr0Cvyf2aWJpIHAUcB/Ew6lQQtVLrOBdcBrZuav1z4/A24Ayg/Fzjxh7E9VlPmvGVfvJLUHXgCuM7NtScfTkJnZHjM7EugLTJQ0OuGQGgRJpwLrzGxWpfKbJc0OSbZ3xXNJ9x/sPg/FEK2NyQqgX8p0X2BVQrG4JkpSK6JkMdXMfpd0PI2FmW2R9AZRm5lfZAEfA06TdArQBsiW9Bszuwi4DaI2jJBs64WfYezvbWCopEGSWgPnA39KOCbXhEgS8DBQYGY/TTqehk5Sd0mdwvO2wCeBuYkG1UCY2U1m1tfMBhJ9V/0jJIu08YSRwszKgGuBvxI1Rj5nZnnJRtWwSXoamAYMl7RC0heTjqmB+xhwMXBSSlXBKUkH1YD1Al6X9D7RD7rXzCztl4+6qnnXIM4552LxMwznnHOxeMJwzjkXiycM55xzsXjCcM45F4snDOecc7F4wnDOOReLJwx3SIVuC/IkvR/uQfiIpOskZdXzfpZI6naA635T0lxJc0K32pfUZ2wx9t9J0ldiLDdM0kuhK/4CSc9J6ikpV9K9YZkTJX30AGKYJOnZlOlsSQslDarrtlzT4QnDHTKSjgFOBcaZ2Riiu3aXA9cB9ZowDpSkq4BPARPNbDRwPFX3MVbd+i1rmo6pE1BjwpDUBvgL8ICZDQndpT8AdDezmWb2tbDoiUCdEwbwENBX0ifD9A+AR8xs8QFsqyLmFge6rmsgzMwf/jgkD+As4MVKZV8DdgEfAK+HsgeAmUAe8P1Q9gng9ynrfQr4XQ37WgJ0AwYS3bX/UNjeq0DbGtZbBhxe0zbD81zgjfD8e8CUsO2nqpjuTtR31Nvh8bGU9R4B3gAWAV8L5c8AO4DZwF3VxHIF8EQ1804E/hyOfQ2wMmzrOGAx0Coslx2OqVU12xlD1GdTbnh/WgMXATPC9n4FtKjuPUt5zW4B/k3UfcXXgHzgfeCZpD+T/qjbI/EA/NF8HkD78EUzD/glcEIo3/tFHKa7hL8twpfpGKJf+XOJfkETvog/X8O+UhNGGXBkKH8OuKiadToAm2vbZnheOWHMIiSiKqafAo4Nz/sT9SNVsdx/gMwQ60ai8R4GAnNqeS1/CkyuZt6JwJ9T9vHNlHmPAmeE55OAn9Syn58AW4GPAyOBF1MSzi+BS6p7z1JesxtStrcKyAzPOyX9mfRH3R5eJeUOGYsGwhlP9EW1HnhW0mVVLHqepHeAd4EcYJRF3zBPAheFzuiOAV6OuevFZjY7PJ9F9IVcFXHg3dn/ycx2VDP9SeC+0N30n4h6Fe0Q5v3FzErNbAPReA89D3D/cf0auDw8v5wogdTkfmClmb1OdJY3Hng7HMsngMFhuQ+9ZynbeDbl+fvAVEkXESVy14h49+bukDKzPUS/QN+Q9AFwaer80Kj6TWCCmW2W9BhR180Qfbm9COwEfmtRZ5FxlKY83wO0rSa2bZKKJQ02s0VVLFLGvna/NpXmFdcwnQEcUymhEHVc+6HY4v5P5gEnxFx2LzN7S9JASScQVSfV1k14OfsG5xHwuJndlLpALe8Z7P9afI6oXeg04DuScurwPrqE+RmGO2QkDZc0NKXoSGApUERUHQRRvXoxsFVST6Lx1QEws1VEVRrfJhpLPB3uAO6XlB1izpY0KcxbQvQLG+DsOmzzVaJekAnbPLKW5VNfj+o8BXxU0udStnuypCNibOsJ4GlqP7uo7O/AOZJ6hP11kTSAGt6zVJIygH7hbOUGosb99nWMwSXIE4Y7lNoDj0vKD91Vj2JfA/HLkl43s/eIqjXyiBqE36q0janAcjPLT1OMDwCvE1W7zAHeBErCvO8DP5f0L6Kzgbi+BuSGS4nzgatqWtjMNgJvhct676pmmR1EV5x9VdL8sN3LiKq1Ur0InBkuYT4ulE0FOhMljdjCa/5t4NXw/r0G9IrxnlVoAfwmnFm+C9xjZlvqEoNLlndv7hoVSfcB75rZw0nH0lhJOgc43cwuTjoW17h4G4ZrNCTNIqr6uD7pWBorSb8gqjLyQZtcnfkZhmvUJP2X6LLUVBeb2Qe1rHc/0eh3qX5uZnWt10+r0CbxZKXiUjP7SD3uo1G8Fi55njCcc87F4o3ezjnnYvGE4ZxzLhZPGM4552LxhOGccy6W/wcqPaQvvB8DYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avearge amount spend based on the time of stay in the current city\n",
    "\n",
    "data = sales_1.groupby('Stay_In_Current_City_Years')['Purchase'].mean()\n",
    "plt.plot(data.index,data.values,marker='o',color='y')\n",
    "plt.xlabel('Stay_In_Current_City_Years');\n",
    "plt.ylabel('Average_Purchase amount in $');\n",
    "plt.title('Stay_In_Current_City_Years vs average amount spent');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f2fc6e",
   "metadata": {},
   "source": [
    "The people who have been living in the current city for 2 or more years are on an average spending more amount in the black friday sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "69b31e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avearge purchase based on Marital_Status\n",
    "\n",
    "data = sales_1.groupby('Marital_Status')['Purchase'].mean()\n",
    "plt.bar(data.index,data.values)\n",
    "plt.xlabel('Marital_Status');\n",
    "plt.ylabel('Average_Purchase amount in $');\n",
    "plt.title('Avearge purchase based on Marital_Status');\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6997066f",
   "metadata": {},
   "source": [
    "Purchasers who married or not, have almost same average of purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "2cfab66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 10 products which made the highest sales\n",
    "\n",
    "data = sales_1.groupby(\"Product_ID\").sum()['Purchase']\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "data.sort_values(ascending=False)[0:10].plot(kind='bar')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Product ID')\n",
    "plt.ylabel('Total amount purchased in Million $')\n",
    "plt.title('Top 10 Products with highest sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "88d16aac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#comparing based on Marital_Status and Gender\n",
    "\n",
    "sns.countplot(x='Marital_Status',data=sales_1,hue='Gender')\n",
    "plt.title('Comparing based on Marital_Status and Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a65c2c",
   "metadata": {},
   "source": [
    "Males tend to purchase more and Unmarried Males are around 45% in the data and they show to purchase 9000$ on average.\n",
    "\n",
    "Products that are most purchased by each of the age group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "71c7badc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "0-17     P00255842\n",
       "18-25    P00265242\n",
       "26-35    P00265242\n",
       "36-45    P00025442\n",
       "46-50    P00265242\n",
       "51-55    P00265242\n",
       "55+      P00265242\n",
       "dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a =pd.crosstab(sales_1['Age'],sales_1['Product_ID'])\n",
    "a.idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "fea63f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Occupations and City Category\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x='Occupation',data=sales_1,hue='City_Category')\n",
    "plt.title('Comparing Occupations and City Category')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c31bb44",
   "metadata": {},
   "source": [
    "People from Occupation 4,0,7 are buying the most and most of the people from these occupations belong to City_Category B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "c0f3434a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the purchase habits of different genders across the different city categories.\n",
    "\n",
    "g = sns.FacetGrid(sales_1,col=\"City_Category\")\n",
    "g.map(sns.barplot, \"Gender\", \"Purchase\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077c61af",
   "metadata": {},
   "source": [
    "For City_categories B and C, Males tend to dominate the purchasing, whereas it is the opposite for City Category_C, where Females tend to puchase more than men."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241bcfd4",
   "metadata": {},
   "source": [
    "## Data preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "60f38c02",
   "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>Purchase</th>\n",
       "      <th>data</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>15.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>train</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>15200.0</td>\n",
       "      <td>train</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>14.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>train</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>1057.0</td>\n",
       "      <td>train</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>15.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>train</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  Purchase   data  \n",
       "0                15.0    8370.0  train  \n",
       "1                 6.0   15200.0  train  \n",
       "2                14.0    1422.0  train  \n",
       "3                14.0    1057.0  train  \n",
       "4                15.0    7969.0  train  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for datapreprocessing again working with the combined dataset\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209bb07c",
   "metadata": {},
   "source": [
    "1.User_ID and Product_ID:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "99bbe421",
   "metadata": {},
   "outputs": [],
   "source": [
    "# User_ID data preprocess. e.g. 1000002 -> 2\n",
    "\n",
    "combined['User_ID'] = combined['User_ID'] - 1000000\n",
    "\n",
    "# Product_ID preprocess e.g. P00069042 -> 69042\n",
    "\n",
    "combined['Product_ID'] = combined['Product_ID'].str.replace('P00', '')\n",
    "\n",
    "#object to int\n",
    "combined['Product_ID'] = pd.to_numeric(combined['Product_ID'],errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "4a43894d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     783667 non-null  int64  \n",
      " 1   Product_ID                  783667 non-null  int64  \n",
      " 2   Gender                      783667 non-null  object \n",
      " 3   Age                         783667 non-null  object \n",
      " 4   Occupation                  783667 non-null  int64  \n",
      " 5   City_Category               783667 non-null  object \n",
      " 6   Stay_In_Current_City_Years  783667 non-null  object \n",
      " 7   Marital_Status              783667 non-null  int64  \n",
      " 8   Product_Category_1          783667 non-null  int64  \n",
      " 9   Product_Category_2          783667 non-null  float64\n",
      " 10  Purchase                    550068 non-null  float64\n",
      " 11  data                        783667 non-null  object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 77.7+ MB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215feb67",
   "metadata": {},
   "source": [
    "2.Product_Category_2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b248ec7",
   "metadata": {},
   "source": [
    "All the unique values in product category 2 are integers. But the data type shown in info is float so we can change it by converting the numbers in float to integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "919f67b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.Product_Category_2 = combined.Product_Category_2.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "65ed1215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age', 'City_Category', 'Stay_In_Current_City_Years', 'data'], dtype='object')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features with datatype object\n",
    "\n",
    "cat_cols = combined.select_dtypes(['object']).columns\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e2797b",
   "metadata": {},
   "source": [
    "3.Stay_In_Current_City_Years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee02e0c3",
   "metadata": {},
   "source": [
    "For Stay in current city years we need to convert the object datatype to int. It contains a category which has '4+' that needs to be altered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "1688f7ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4+ to 4\n",
    "combined['Stay_In_Current_City_Years'] =np.where(combined['Stay_In_Current_City_Years'].str[:2]==\"4+\",4,combined['Stay_In_Current_City_Years'])\n",
    "\n",
    "#object to int\n",
    "combined['Stay_In_Current_City_Years'] = pd.to_numeric(combined['Stay_In_Current_City_Years'],errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83418e4c",
   "metadata": {},
   "source": [
    "4.Gender:\n",
    "\n",
    "Gender 'F' for female are represented by the value fo 0.\n",
    "\n",
    "Gender 'M' for male are represented by the value fo 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "9bb3c085",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined['Gender'] = combined['Gender'].map({'F':0, 'M':1}).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8e83920",
   "metadata": {},
   "source": [
    "5.Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "68351e42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31    313015\n",
       "42    156724\n",
       "22    141953\n",
       "48     65278\n",
       "53     54784\n",
       "60     30579\n",
       "9      21334\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modify age column\n",
    "\n",
    "combined['Age'] = combined['Age'].map({'0-17': 9,\n",
    "                               '18-25': 22,\n",
    "                               '26-35': 31,\n",
    "                               '36-45': 42,\n",
    "                               '46-50': 48,\n",
    "                               '51-55': 53,\n",
    "                               '55+': 60})\n",
    "combined['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2718152c",
   "metadata": {},
   "source": [
    "6.City_Category : dummy variables for this feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "fd0d0cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = pd.get_dummies(combined,columns=['City_Category'],drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "ba5eb54c",
   "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>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>Purchase</th>\n",
       "      <th>data</th>\n",
       "      <th>City_Category_B</th>\n",
       "      <th>City_Category_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>69042</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>248942</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>87842</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>85442</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>285442</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID  Product_ID  Gender  Age  Occupation  Stay_In_Current_City_Years  \\\n",
       "0        1       69042       0    9          10                           2   \n",
       "1        1      248942       0    9          10                           2   \n",
       "2        1       87842       0    9          10                           2   \n",
       "3        1       85442       0    9          10                           2   \n",
       "4        2      285442       1   60          16                           4   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Purchase   data  \\\n",
       "0               0                   3                  15    8370.0  train   \n",
       "1               0                   1                   6   15200.0  train   \n",
       "2               0                  12                  14    1422.0  train   \n",
       "3               0                  12                  14    1057.0  train   \n",
       "4               0                   8                  15    7969.0  train   \n",
       "\n",
       "   City_Category_B  City_Category_C  \n",
       "0                0                0  \n",
       "1                0                0  \n",
       "2                0                0  \n",
       "3                0                0  \n",
       "4                0                1  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "0b71c081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 13 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     783667 non-null  int64  \n",
      " 1   Product_ID                  783667 non-null  int64  \n",
      " 2   Gender                      783667 non-null  int32  \n",
      " 3   Age                         783667 non-null  int64  \n",
      " 4   Occupation                  783667 non-null  int64  \n",
      " 5   Stay_In_Current_City_Years  783667 non-null  int64  \n",
      " 6   Marital_Status              783667 non-null  int64  \n",
      " 7   Product_Category_1          783667 non-null  int64  \n",
      " 8   Product_Category_2          783667 non-null  int64  \n",
      " 9   Purchase                    550068 non-null  float64\n",
      " 10  data                        783667 non-null  object \n",
      " 11  City_Category_B             783667 non-null  uint8  \n",
      " 12  City_Category_C             783667 non-null  uint8  \n",
      "dtypes: float64(1), int32(1), int64(8), object(1), uint8(2)\n",
      "memory usage: 70.3+ MB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "8b3954b4",
   "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>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>Purchase</th>\n",
       "      <th>data</th>\n",
       "      <th>City_Category_B</th>\n",
       "      <th>City_Category_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>69042</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>248942</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>87842</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>85442</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>285442</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID  Product_ID  Gender  Age  Occupation  Stay_In_Current_City_Years  \\\n",
       "0        1       69042       0    9          10                           2   \n",
       "1        1      248942       0    9          10                           2   \n",
       "2        1       87842       0    9          10                           2   \n",
       "3        1       85442       0    9          10                           2   \n",
       "4        2      285442       1   60          16                           4   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Purchase   data  \\\n",
       "0               0                   3                  15    8370.0  train   \n",
       "1               0                   1                   6   15200.0  train   \n",
       "2               0                  12                  14    1422.0  train   \n",
       "3               0                  12                  14    1057.0  train   \n",
       "4               0                   8                  15    7969.0  train   \n",
       "\n",
       "   City_Category_B  City_Category_C  \n",
       "0                0                0  \n",
       "1                0                0  \n",
       "2                0                0  \n",
       "3                0                0  \n",
       "4                0                1  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "76a5fce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data back into train and test as it was already provided\n",
    "\n",
    "sales = combined[combined['data']=='train']\n",
    "del sales['data']\n",
    "test_input = combined[combined['data']=='test']\n",
    "test_input.drop(['Purchase','data'],axis=1,inplace=True)\n",
    "\n",
    "del combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "80e3b44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap to show the correlation between various variables of the train data set\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "cor = sales.corr()\n",
    "ax = sns.heatmap(cor,annot=True)\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6c0b13",
   "metadata": {},
   "source": [
    "The variables which show a significant correlation in the data are:\n",
    "\n",
    "1.Marital_status and Age\n",
    "\n",
    "2.Product_Category_1 and Purchase\n",
    "\n",
    "3.City_Category_B and City_category_A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1170d95",
   "metadata": {},
   "source": [
    "## Model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "3505c75a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data into X and y\n",
    "X = sales.drop('Purchase',axis=1)\n",
    "y = sales['Purchase']\n",
    "\n",
    "#train test split for model building\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.30,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "f33b5b6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients:\n",
      " [ 1.50676051e-02 -3.71031298e-03  5.35706747e+02  1.49740693e+01\n",
      "  6.83457000e+00  1.25765080e+01 -6.05041539e+01 -4.10865642e+02\n",
      " -4.64540490e+01  1.79761704e+02  6.99818664e+02]\n",
      "\n",
      " intercept: 11266.49654247417\n"
     ]
    }
   ],
   "source": [
    "#Linear regression\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train,y_train) # training the algorithm\n",
    "\n",
    "# Getting the coefficients and intercept\n",
    "\n",
    "print('coefficients:\\n', lr.coef_)\n",
    "print('\\n intercept:', lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "ef61b5fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.13223233692778624\n",
      "rmse: 4684.996453385466\n"
     ]
    }
   ],
   "source": [
    "#Predicting on the test data\n",
    "\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "84fec607",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 4685.54669001201\n"
     ]
    }
   ],
   "source": [
    "# Ridge Regression\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "RR = Ridge(alpha=0.05,normalize=True)\n",
    "RR.fit(X_train, y_train)\n",
    "\n",
    "y_pred = RR.predict(X_test)\n",
    "\n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65da2e4c",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "6433c7c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 2735.998553274134\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree Model\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "DT = DecisionTreeRegressor(max_depth=15, min_samples_leaf=100)\n",
    "\n",
    "DT.fit(X_train, y_train)\n",
    "\n",
    "y_pred = DT.predict(X_test)\n",
    "\n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "97fdebcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse: 2884.4735501907207\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree 2\n",
    "\n",
    "DT2 = DecisionTreeRegressor(max_depth=8, min_samples_leaf=150)\n",
    "\n",
    "DT2.fit(X_train, y_train)\n",
    "\n",
    "y_pred = DT2.predict(X_test)\n",
    "\n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d16ac33a",
   "metadata": {},
   "source": [
    "## Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "942d3b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.6910077941504174\n",
      "rmse: 2795.6403342877447\n"
     ]
    }
   ],
   "source": [
    "#Fitting the model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(random_state = 3,max_depth=10,n_estimators=25)\n",
    "\n",
    "rf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "b66b16c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.6885333480972113\n",
      "rmse: 2806.811921914396\n"
     ]
    }
   ],
   "source": [
    "# another random forest\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf3 = RandomForestRegressor(random_state=3,max_depth=10,min_samples_split=500,oob_score=True)\n",
    "\n",
    "\n",
    "rf3.fit(X_train,y_train)\n",
    "\n",
    "y_pred = rf3.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "03b3e4f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.7125158393239708\n",
      "rmse: 2696.5873701999635\n"
     ]
    }
   ],
   "source": [
    "# random forest 4\n",
    "\n",
    "rf4 = RandomForestRegressor(n_estimators=30,random_state=3,max_depth=15,min_samples_split=100,oob_score=True)\n",
    "\n",
    "\n",
    "rf4.fit(X_train,y_train)\n",
    "\n",
    "y_pred = rf4.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "05b4ca91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.7361136125301322\n",
      "rmse: 2583.544992467269\n"
     ]
    }
   ],
   "source": [
    "#XGBoost Model1\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "\n",
    "xgb1 = XGBRegressor(n_estimators=1000, learning_rate=0.05)\n",
    "\n",
    "xgb1.fit(X_train,y_train)\n",
    "\n",
    "y_pred = xgb1.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "68c906d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.7486920629214626\n",
      "rmse: 2521.219386344897\n"
     ]
    }
   ],
   "source": [
    "## XGBoost2\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb2 = XGBRegressor(n_estimators=500,max_depth=10,learning_rate=0.05)\n",
    "\n",
    "xgb2.fit(X_train,y_train)\n",
    "\n",
    "y_pred = xgb2.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "e980bd01",
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb2 = XGBRegressor(n_estimators=500,max_depth=10,learning_rate=0.05)\n",
    "\n",
    "xgb2.fit(X,y)\n",
    "predict = xgb2.predict(test_input)\n",
    "\n",
    "submission['Purchase'] = predict\n",
    "\n",
    "submission.to_csv('Sample_Submission_XGB2.csv')\n",
    "\n",
    "#score: 2522.743"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "50e01743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.6228365360098811\n",
      "rmse: 3088.6759266561635\n"
     ]
    }
   ],
   "source": [
    "## XGBoost3\n",
    "\n",
    "xgb3 = XGBRegressor(n_estimators=6,max_depth=500)\n",
    "\n",
    "xgb3.fit(X_train,y_train)\n",
    "\n",
    "y_pred = xgb3.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "5ea6fcc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.7339580400801886\n",
      "rmse: 2594.0754558933836\n"
     ]
    }
   ],
   "source": [
    "#XGBoost4\n",
    "\n",
    "xgb4 = XGBRegressor(learning_rate=1.0, max_depth=6, min_child_weight=40, seed=0)\n",
    "\n",
    "xgb4.fit(X_train,y_train)\n",
    "\n",
    "y_pred = xgb4.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "5582bd64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.7442852801365247\n",
      "rmse: 2543.228603697683\n"
     ]
    }
   ],
   "source": [
    "#XGBoost5\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb5 = XGBRegressor(n_estimators=450,max_depth=8,learning_rate=0.076)\n",
    "\n",
    "xgb5.fit(X_train,y_train)\n",
    "\n",
    "y_pred = xgb5.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "d0832781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score: 0.7466628781821589\n",
      "rmse: 2531.3777086957602\n"
     ]
    }
   ],
   "source": [
    "#XGBoost6\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb6 = XGBRegressor(n_estimators=470,max_depth=9,learning_rate=0.06)\n",
    "\n",
    "xgb6.fit(X_train,y_train)\n",
    "\n",
    "y_pred = xgb6.predict(X_test)\n",
    "\n",
    "print('r2_score:', metrics.r2_score(y_test,y_pred)) \n",
    "print('rmse:', np.sqrt(metrics.mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c805acd4",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "Comparing all the models, we conclude that the XGBRegressor model is the best model to be able to predict purchase amount from our dataset.\n",
    "\n",
    "Parameters and score:\n",
    "\n",
    "XGBRegressor(n_estimators=500,max_depth=10,learning_rate=0.05)\n",
    "\n",
    "r2_score: 0.7466628781821589\n",
    "\n",
    "rmse:2531.3777086957602"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "92b696f0",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41001</th>\n",
       "      <td>15228.0</td>\n",
       "      <td>13575.380859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16151</th>\n",
       "      <td>1088.0</td>\n",
       "      <td>960.772583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507262</th>\n",
       "      <td>16426.0</td>\n",
       "      <td>16369.493164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368698</th>\n",
       "      <td>12042.0</td>\n",
       "      <td>17701.302734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353782</th>\n",
       "      <td>8699.0</td>\n",
       "      <td>7004.935547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual     Predicted\n",
       "41001   15228.0  13575.380859\n",
       "16151    1088.0    960.772583\n",
       "507262  16426.0  16369.493164\n",
       "368698  12042.0  17701.302734\n",
       "353782   8699.0   7004.935547"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "df1 = df.head(25)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "6fbfecbc",
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Product_Category_1</th>\n",
       "      <td>0.862691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_ID</th>\n",
       "      <td>0.027054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Category_C</th>\n",
       "      <td>0.023098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_2</th>\n",
       "      <td>0.013622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <td>0.012006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.010626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Category_B</th>\n",
       "      <td>0.010435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <td>0.010292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0.010217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <td>0.010099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <td>0.009859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            importance\n",
       "Product_Category_1            0.862691\n",
       "Product_ID                    0.027054\n",
       "City_Category_C               0.023098\n",
       "Product_Category_2            0.013622\n",
       "User_ID                       0.012006\n",
       "Age                           0.010626\n",
       "City_Category_B               0.010435\n",
       "Occupation                    0.010292\n",
       "Gender                        0.010217\n",
       "Marital_Status                0.010099\n",
       "Stay_In_Current_City_Years    0.009859"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "\n",
    "imp = pd.DataFrame(xgb2.feature_importances_,index=X.columns,columns=['importance'])\n",
    "imp.sort_values(by='importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "16fef6e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(kind='bar',figsize=(10,8))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "0346f8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1.Predicted,df1.Actual)\n",
    "plt.plot(y_pred,y_pred,'r')\n",
    "plt.xlabel('y predicted')\n",
    "plt.ylabel('y actual')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab209802",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "~ython3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}