Black Friday Sales Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed36b7af",
   "metadata": {},
   "source": [
    "A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month.\n",
    "The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month.\n",
    "\n",
    "Now, they want to build a model to **predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "df478ce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['figure.figsize'] = [15, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "3fa26172",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(550068, 12) (233599, 11) (783667, 12)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv(\"~/Downloads/train.csv\")\n",
    "test = pd.read_csv(\"~/Downloads/test.csv\")\n",
    "\n",
    "# Combined the data\n",
    "combined = pd.concat([train, test], ignore_index= True)\n",
    "\n",
    "print(train.shape, test.shape, combined.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "50d3c87f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>8370.0</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "2b7b7251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
       "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
       "       'Product_Category_2', 'Product_Category_3', 'Purchase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  What defines the Customer's Purchase Habits...\n",
    "combined.columns\n",
    "\n",
    "# Gender, Age, Occupation, Stay in Current City...\n",
    "# marital status, City Category..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "2d100aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 783667 entries, 0 to 783666\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  object \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          537685 non-null  float64\n",
      " 10  Product_Category_3          237858 non-null  float64\n",
      " 11  Purchase                    550068 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 71.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# Lets check the Info of the Data\n",
    "combined.info()\n",
    "\n",
    "# Fix the Stay in Current City."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "cfd40998",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>count</th>\n",
       "      <td>7.836670e+05</td>\n",
       "      <td>783667</td>\n",
       "      <td>783667</td>\n",
       "      <td>783667</td>\n",
       "      <td>783667.000000</td>\n",
       "      <td>783667</td>\n",
       "      <td>783667</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3677</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>P00265242</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2709</td>\n",
       "      <td>590031</td>\n",
       "      <td>313015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>329739</td>\n",
       "      <td>276425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.079300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.409777</td>\n",
       "      <td>5.366196</td>\n",
       "      <td>9.844506</td>\n",
       "      <td>12.668605</td>\n",
       "      <td>9263.968713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.727267e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.522206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.491793</td>\n",
       "      <td>3.878160</td>\n",
       "      <td>5.089093</td>\n",
       "      <td>4.125510</td>\n",
       "      <td>5023.065394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1.001519e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1.003075e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
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      ],
      "text/plain": [
       "             User_ID Product_ID  Gender     Age     Occupation City_Category  \\\n",
       "count   7.836670e+05     783667  783667  783667  783667.000000        783667   \n",
       "unique           NaN       3677       2       7            NaN             3   \n",
       "top              NaN  P00265242       M   26-35            NaN             B   \n",
       "freq             NaN       2709  590031  313015            NaN        329739   \n",
       "mean    1.003029e+06        NaN     NaN     NaN       8.079300           NaN   \n",
       "std     1.727267e+03        NaN     NaN     NaN       6.522206           NaN   \n",
       "min     1.000001e+06        NaN     NaN     NaN       0.000000           NaN   \n",
       "25%     1.001519e+06        NaN     NaN     NaN       2.000000           NaN   \n",
       "50%     1.003075e+06        NaN     NaN     NaN       7.000000           NaN   \n",
       "75%     1.004478e+06        NaN     NaN     NaN      14.000000           NaN   \n",
       "max     1.006040e+06        NaN     NaN     NaN      20.000000           NaN   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "count                      783667   783667.000000       783667.000000   \n",
       "unique                          5             NaN                 NaN   \n",
       "top                             1             NaN                 NaN   \n",
       "freq                       276425             NaN                 NaN   \n",
       "mean                          NaN        0.409777            5.366196   \n",
       "std                           NaN        0.491793            3.878160   \n",
       "min                           NaN        0.000000            1.000000   \n",
       "25%                           NaN        0.000000            1.000000   \n",
       "50%                           NaN        0.000000            5.000000   \n",
       "75%                           NaN        1.000000            8.000000   \n",
       "max                           NaN        1.000000           20.000000   \n",
       "\n",
       "        Product_Category_2  Product_Category_3       Purchase  \n",
       "count        537685.000000       237858.000000  550068.000000  \n",
       "unique                 NaN                 NaN            NaN  \n",
       "top                    NaN                 NaN            NaN  \n",
       "freq                   NaN                 NaN            NaN  \n",
       "mean              9.844506           12.668605    9263.968713  \n",
       "std               5.089093            4.125510    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": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe of the data\n",
    "combined.describe(include= \"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4be75625",
   "metadata": {},
   "source": [
    "* Age is binned.\n",
    "* If there is any popular PRODUCT ID - P00265242\n",
    "* Most of the transactions are done by Males.\n",
    "* People in the age bracket of 26-35 are purchasing more than the rest.\n",
    "* People from City Category B are purchasing the most.\n",
    "* The Purchase Variable has min of $12 and Max of 23961 dollars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "fbab4276",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Product_Category_3</th>\n",
       "      <td>545809</td>\n",
       "      <td>0.696481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_2</th>\n",
       "      <td>245982</td>\n",
       "      <td>0.313886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchase</th>\n",
       "      <td>233599</td>\n",
       "      <td>0.298085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Category</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Missing Vals  Percentage\n",
       "Product_Category_3                545809    0.696481\n",
       "Product_Category_2                245982    0.313886\n",
       "Purchase                          233599    0.298085\n",
       "User_ID                                0    0.000000\n",
       "Product_ID                             0    0.000000\n",
       "Gender                                 0    0.000000\n",
       "Age                                    0    0.000000\n",
       "Occupation                             0    0.000000\n",
       "City_Category                          0    0.000000\n",
       "Stay_In_Current_City_Years             0    0.000000\n",
       "Marital_Status                         0    0.000000\n",
       "Product_Category_1                     0    0.000000"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Values\n",
    "miss = combined.isnull().sum()\n",
    "percent = combined.isnull().sum()/combined.shape[0]\n",
    "\n",
    "df = {\"Missing Vals\":miss, \"Percentage\":percent}\n",
    "pd.DataFrame(df).sort_values(by = \"Percentage\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "acea534d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets explore the data...\n",
    "train.hist(edgecolor=\"black\", figsize = (15, 10))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe8cd2e",
   "metadata": {},
   "source": [
    "* 16 is the most preferred prod_cat for Customers in Prod Cat 3\n",
    "* Purchase is +vely Skewed.\n",
    "* Prod_Cat2 - 16, 8 appears to be the preferred ones.\n",
    "\n",
    "* We will explore this data more...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "25d0e9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    783667.000000\n",
       "mean          5.366196\n",
       "std           3.878160\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           5.000000\n",
       "75%           8.000000\n",
       "max          20.000000\n",
       "Name: Product_Category_1, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Product Category\n",
    "combined.Product_Category_1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "a8de928f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    537685.000000\n",
       "mean          9.844506\n",
       "std           5.089093\n",
       "min           2.000000\n",
       "25%           5.000000\n",
       "50%           9.000000\n",
       "75%          15.000000\n",
       "max          18.000000\n",
       "Name: Product_Category_2, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Product_Category_2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "b08eca16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16.0\n",
       "Name: Product_Category_3, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Product_Category_1.mode(),\n",
    "combined.Product_Category_2.mode(), \n",
    "combined.Product_Category_3.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fda44bd",
   "metadata": {},
   "source": [
    "* 5, 8 and 16 appear to be the most popular products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "bf3e71a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the Data in Num and Cat Variables\n",
    "num_cols = combined.select_dtypes(include = np.number).columns\n",
    "cat_cols = combined.select_dtypes(exclude = np.number).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "94d8c4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix the Marital Status\n",
    "combined[\"Marital_Status\"]=combined.Marital_Status.astype(\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "96f826b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['User_ID', 'Occupation', 'Marital_Status', 'Product_Category_1',\n",
      "       'Product_Category_2', 'Product_Category_3', 'Purchase'],\n",
      "      dtype='object')\n",
      "Index(['Product_ID', 'Gender', 'Age', 'City_Category',\n",
      "       'Stay_In_Current_City_Years'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(num_cols), print(cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "7706ee80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Categories...\n",
    "a=3\n",
    "b=2\n",
    "c=1\n",
    "#plt.figure(figsize=[12,10])\n",
    "for i in cat_cols[1:]:\n",
    "  plt.subplot(a,b,c)\n",
    "  sns.countplot(combined.loc[:,i])\n",
    "  plt.xlabel(i)\n",
    "  c=c+1\n",
    "plt.xticks(rotation=90)  \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1794a849",
   "metadata": {},
   "source": [
    "* Males are more than females in this data and hence, it is clear that most of the purchase is done by males only.\n",
    "* The most popular age bracket is 26-35 followed by 36-45\n",
    "* City Cat B then C followed by A in the descending order of Purchase.\n",
    "* Most of the people making purchase are Independent of any person in life.\n",
    "* Most of them have stayed for min 1 year...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "dc04761c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Special Character from Stay and Age\n",
    "combined['Stay_In_Current_City'] = combined.Stay_In_Current_City_Years.str.strip(\"+\").astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "91293d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same will done in Age...\n",
    "combined['Age'] = combined.Age.str.strip(\"+\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "d6c25ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='count'>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the Age var\n",
    "sns.countplot(x = \"Age\", data = combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "bdb96df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Stay In City, Marital Status and Occupation has an effect on Tgt Variable...\n",
    "\n",
    "# FacetGrid Plot\n",
    "\n",
    "g = sns.FacetGrid(combined, col = \"Stay_In_Current_City\")\n",
    "g.map(sns.barplot, \"Occupation\", \"Purchase\")\n",
    "plt.show()\n",
    "\n",
    "#Inference: that there is no effect on \n",
    "#Purchase irrespective of the Marital Status, his stay in current city & Occupation..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bba44820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Occupation', ylabel='Purchase'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the Occupation vs Purchase\n",
    "sns.scatterplot(combined.Occupation, combined.Purchase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4c6091c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hexplot or jointplot\n",
    "\n",
    "sns.jointplot(x = \"Occupation\", y = \"Purchase\", data = combined, kind = \"hex\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d631ad",
   "metadata": {},
   "source": [
    "* Most of the Purchase is between 5000-10000 range.\n",
    "* The Occupation in the range 0-7 have the highest purchase where 4 appears to be the profession where people make the most of the purchases.\n",
    "\n",
    "#### How this Info can be used\n",
    "* Company can target Occupations between 0 - 7 and run marketing campaigns to encourage people to buy more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ecedbe60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f7a7c261760>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Check City Category with Gender and Purchase...\n",
    "\n",
    "g = sns.FacetGrid(combined, col = \"City_Category\") # Facetgrid Object\n",
    "g.map(sns.barplot, \"Gender\", \"Purchase\") # func, x, y\n",
    "\n",
    "\n",
    "# We see that City Category C has the highest no of Purchases Witnessed.\n",
    "# For City Cat B & C, males tend to dominate the Purchase...\n",
    "# Where as in City A, we see that females are the biggest spenders..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3e2c631f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ae+wMVjoum9RKsWPfI35maV3Ez/D5PX5of/rtVXVMd1+dCfY9VXX/JL+eUZJ5qT05fsTObK2L2FnFc5O8sLvfVlU/ldGFDz+y0shV9esZNeW5fdlrFjsS8NO7JqOzOzulu2+qqi9W1Q9ndLbsp3ewjGOTvH+ZYRckeUZ3f7KqfjajM1ZL7ZXkqz1qC2lSy/1p2O4bS96/NqOzjwdl9KN335lV/XWSBy0z6EXd/VdLylZb3x2qqu/M6LalY6qqMwr0rqpzsvJ6VZJruvuJO5j3Tyf5lWUGXdffaotsu61JDht7f2iSW4a22v5iKPsf3b3qwx2qau+M2lE8drXxNgjxJJ6S5eMp3X3L8HpbVb09yfEZ3Z74xbF5/lFGVx1sNGJDbCQrxEZVfUdGyfc3dff4s0K2Jvmz4U/b5VV1b5KH1+iBXt+X5JbuXtp25puTvCvJS7r7rozaKc2QoPx8RredbsliET/iJ1k+fp6U5Cdq9BC3+yXZv6r+uLufkwnjZ5V90LLHgavVfR0SO2In2cnjslXm/4ZMEDv2PeJnsKHjZ7vu/mpVfTCjNq2vzgT7niS/muSIJJ8cEpuHJvl4VR3f3duy+PEjdsROsoPYWcYZGT3TJ0n+NKPP8D77nqHsjCRPT/KU7Se31nTf0zO6tH6jdxl9qT6W5BfGyp6Q5N/k22+P+ckkb1wy7U9m9AP6mztYxo8PG/2g4f2+GdqASnJHkgMyur3j0gy3iyT570l+bmwef5vk1LE6P27of1e+1U7SWfnW7TH/LqMzfZuSbE7yvzMK/hMzdnvuMO4+ST6b5AtJNu2Gz3TF9V0y3olZ5vaYJP9Xkj9cUvahJD+Y5NSMDu72yuhs4pcz+nHfJ6M2wp44jP8dSY7exfXYe/hMjhjm/8nV5pkVmqDJaGf8oXl/19eiE0/iaZX1eECSB431/22Sk4b3B4+N98IkF837uyw2xMYaxkYluTDJ7y0z7BeT/Oeh/1EZ3ZJZy4x35Fj/8zLc8jlsk01D/yMyup3zYfOOB/EjfnZX/CxT1/EmaCaNn2X3QRk9fPWTw2dzxO767MWO2FkPsZNVjsvGxvlgVm+CZqXYse8RPxs9fjYnecjQv19GTYk8fXg/0b5nyfxuyLeaoFn4+BE7YmeC9fmX78FY2bVJThz6n5LkihWmPSmjtvY3Lylfs9jZK0ylR1vnmUl+tKo+X1XXZNSe3dIrXD6Q5KiqurKqnjWUXZLRLS1v2MEy3p3kD5L81TD/K/Ktuxb+n4x+nC5N8pmxyS5K8itV9YmqemRGZ//OrKpPZnT26+RhvBck+Q9VdXlG7U99bSh/e0bt8X0yo7Nk5/TobOpy9fvnYf0u7tHtNLtkB+s7iWdnVP9xb0vyfw6vWzM6u/yHGX12XxvW4ZQkvzl8RldmdEvR1Lr77oxu439fRj8GF3f3NcuNW1VvSfKRJI+uqq1VdebY4NOyhzQ/I57E0yoOTPI3w/wuT/Ku7n7vMOz8qrqqqj6V5Icy+hO3oYgNsbGKJ2X0jJEfHrb7lcPVvMnoiplHVNXVGW2rM4bv0lIvr6qrhxh6ar519ciTk3xqqOtbk/xid395F+u75sSP+JnSpPGz7D5oOOa7OKM/ee9Ncvbu+OzXktgRO6tY8bisqp5ZVVszaobhXVV1n+YbBisdv9n3iJ9p1ncS6yV+Dk7ygeG7/3dJLu3u7XdPTbrvWcnCx4/YETurWSV39gtJfntY1n/N6OTHcl6Z0Z0Dlw7fne0tUqxZ7NTOxTS7Q1Udl9HDJda67abxOtw/yT92d1fVaRk9LOLkHU23ZB57ZdT206nd/blZ1HOF5Z6Y5Ge7+2d3croHdvffD7fRXJ5R+4XL/vCxOMTTrhFPG5fY2DViY88mfnaN+NlziZ1dI3b2bOJn14ifPZfY2TViZ21oA36NVdWLM3pIwGrtUq2FY5O8sqoqyVezkw8urKqjMrrd5O1r+cOwi95Zo4ed7JPkN/wwLD7xNFfiaR0TG3MlNhac+Jkr8bPAxM5ciZ0FJ37mSvwsMLEzV2JnJ7gCfh2o0VN4T11S/Kfdfd486rPeVdXhSR7f3X8+56qwDomnnSOe9hxiY+eIDcaJn50jfthO7OwcscM48bNzxA/biZ2dI3bWhgQ8AAAAAADMwB73ENaTTjqpk+h0e3I3FbGj06UzJfGj001H7Oh00xM/Ot10xI5ONx2xo9Ols4I9LgF/xx13zLsKsJDEDkxP/MB0xA5MT/zAdMQOTEfswMr2uAQ8AAAAAACsBQl4AAAAAACYAQl4AAAAAACYAQl4AAAAAACYAQl4AAAAAACYAQl4AAAAAACYAQl4AAAAAACYgZkl4Kvq9VV1W1VdPVb2sKq6tKo+N7w+dGzYuVV1XVV9tqqeNlZ+bFVdNQz7/aqqoXzfqvqTofxjVXX4rNYFAAAAAAB21iyvgL8gyUlLyl6c5LLuPjLJZcP7VNVRSU5LcvQwzauqatMwzauTnJXkyKHbPs8zk3ylu/+PJL+b5DdntiYAsIc655xzcvrpp+ecc86Zd1UAAFiF4zaA9WnvWc24uz+8zFXpJyc5ceh/Y5IPJvnVofyi7r4ryfVVdV2S46vqhiT7d/dHkqSqLkzyjCTvGaZ56TCvtyZ5ZVVVd/ds1ggA9jzbtm3LzTffPO9qALAHOeecc7Jt27YcdNBBOf/88+ddHVgYjtsA1qeZJeBXcGB335ok3X1rVR0wlB+S5KNj420dyr459C8t3z7NTcO87q6qryX5ziR3zK76ADA7x/7KhfOuwn086I47synJjXfcuS7rd8UrTp93FQDYzSQRWQTr8bjIcRtMz8lfZmm9PIS1linrVcpXm+a+M686q6q2VNWW22+/fcoqwp5H7MD0xA9MR+zA9MQPTEfswHQ2UuxsP/m7bdu2eVeFDWitr4D/YlUdPFz9fnCS24byrUkOGxvv0CS3DOWHLlM+Ps3Wqto7yYOTfHm5hXb3a5K8JkmOO+44TdTAhMQOTG+jxM+9+zzg215h1jZK7MA87Gz8rMcrZBNX8bL2Nsq+x3Eba22jxA7M2lon4C9JckaSlw+v7xgrf3NV/U6S78roYauXd/c9VXVnVZ2Q5GNJTk/y35fM6yNJTknyfu2/A8Du9Y0jnzrvKsDCciszAGvJcRuLYD2eWE3W98lfJ34X38wS8FX1loweuPrwqtqa5CUZJd4vrqozk9yY5NQk6e5rquriJJ9OcneSs7v7nmFWz01yQZL9Mnr46nuG8tcl+Z/DA1u/nOS0Wa0LAADsLO1Yw3RcxQsAbCQzS8B397NXGPSUFcY/L8l5y5RvSXLMMuX/lCGBDwDAnmu9XaW0nSupYDqu4gVgrTn5yyytdRM0AHOhGQAAAABgOU7+MksS8MAeQTMAAAAwGRevwHTEDrAcCXjYCXamO7bebrHfTjMAAKw1tzIDi8rFKzAdsQMsRwIedoKdKQAwKbcyAwAAEvAAAAAALIz1dlfzduv5zuvE3dcwLxLwrEvrcUeV2JkuMs0AAACw3qzH/xTJ+v7f4z8PAItGAh7YI2gGAAD2LJ7dAwDAeiABDwCwjkkiwnQ8uweAtebOa5jORv/PIwEPO8HOFKaz0XemMEuSiACsNf97YDruvIbpbPT/PBLwsBPsTGE6G31nCrAnW2/tQ2+3ntuwTrRjzfrmfw/AxrUej4s2+nGbBDwAQNbngWiyvg9GJRABAABWJwEPsMGstwRdsr4TiIkkIgAAADAbEvAAAOuYdnhhOmIHAGAxbPTjNgl4AIB1TDu8MB2xAwCwGDb6cdte864AAAAAAABsRK6AB2DmNvrtZAAAAADLkYAHYOY2+u1kAAAAAMuRgF8g55xzTrZt25aDDjoo559//ryrAwAAAADAKiTgF8i2bdty8803z7saAAAAAABMwENYAQAAAABgBiTgAQAAAABgBiTgAQAAAABgBrQBv4Jjf+XCeVfhPh50x53ZlOTGO+5cl/W74hWnz7sKAAAAAADrhivgAQAAAABgBuaSgK+qF1bVNVV1dVW9paruV1UPq6pLq+pzw+tDx8Y/t6quq6rPVtXTxsqPraqrhmG/X1U1j/UBAAAAAICl1jwBX1WHJPnlJMd19zFJNiU5LcmLk1zW3UcmuWx4n6o6ahh+dJKTkryqqjYNs3t1krOSHDl0J63hqgAAAAAAwIrm1QTN3kn2q6q9k9w/yS1JTk7yxmH4G5M8Y+g/OclF3X1Xd1+f5Lokx1fVwUn27+6PdHcnuXBsmg3p3n0ekHv23T/37vOAeVcFAAAAAIAdWPOHsHb3zVX1W0luTPKPSf6yu/+yqg7s7luHcW6tqgOGSQ5J8tGxWWwdyr459C8tv4+qOiujK+Xz3d/93btzddbUN4586ryrwB5mo8QOzIP4gemIHZie+IHpiB2YjtiBycyjCZqHZnRV+xFJvivJA6rqOatNskxZr1J+38Lu13T3cd193ObNm3e2yrDHEjswPfED0xE7MD3xA9MROzAdsQOTmUcTND+S5Pruvr27v5nkz5L8QJIvDs3KZHi9bRh/a5LDxqY/NKMma7YO/UvLAQAAAABg7uaRgL8xyQlVdf+qqiRPSXJtkkuSnDGMc0aSdwz9lyQ5rar2raojMnrY6uVDczV3VtUJw3xOH5sGAAAAAADmah5twH+sqt6a5ONJ7k7yiSSvSfLAJBdX1ZkZJelPHca/pqouTvLpYfyzu/ueYXbPTXJBkv2SvGfoAAAAAABg7tY8AZ8k3f2SJC9ZUnxXRlfDLzf+eUnOW6Z8S5JjdnsFAQAAAABgF82jCRoAAAAAANjwJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJOABAAAAAGAGJkrAV9Wjquqyqrp6eP/Yqvq/Z1s1AAAAAABYXJNeAf9HSc5N8s0k6e5PJTltVpUCAAAAAIBFN2kC/v7dffmSsrt3d2UAAAAAAGCjmDQBf0dVPTJJJ0lVnZLk1pnVCgAAAAAAFtzeE453dpLXJHlMVd2c5Pokz5lZrQAAAAAAYMFNlIDv7i8k+ZGqekCSvbr7ztlWCwAAAAAAFttETdBU1fOrav8k/5Dkd6vq41X11GkXWlUPqaq3VtVnquraqnpiVT2sqi6tqs8Nrw8dG//cqrquqj5bVU8bKz+2qq4ahv1+VdW0dQIAAAAAgN1p0jbgf767v57kqUkOSPJzSV6+C8v9b0ne292PSfK4JNcmeXGSy7r7yCSXDe9TVUclOS3J0UlOSvKqqto0zOfVSc5KcuTQnbQLdQIAAAAAgN1m0gT89ivLfzzJG7r7k2NlO2W4kv7JSV6XJN39z9391SQnJ3njMNobkzxj6D85yUXdfVd3X5/kuiTHV9XBSfbv7o90dye5cGwaAAAAAACYq0kT8FdU1V9mlIB/X1U9KMm9Uy7zEUluT/KGqvpEVb12aFv+wO6+NUmG1wOG8Q9JctPY9FuHskOG/qXl91FVZ1XVlqracvvtt09ZbdjziB2YnviB6YgdmJ74gemIHZiO2IHJTJqAPzOjJmGe0N3/kGSfjJqhmcbeSb4/yau7+/uSfGOY90qWu9K+Vym/b2H3a7r7uO4+bvPmzTtbX9hjiR2YnviB6YgdmJ74gemIHZiO2IHJ7D3JSN19b1Vdn+RRVXW/XVzm1iRbu/tjw/u3ZpSA/2JVHdzdtw7Ny9w2Nv5hY9MfmuSWofzQZcoBAAAAAGDuJroCvqr+fZIPJ3lfkpcNry+dZoHdvS3JTVX16KHoKUk+neSSJGcMZWckecfQf0mS06pq36o6IqOHrV4+NFNzZ1WdUFWV5PSxaQAAAAAAYK4mugI+yfOTPCHJR7v7h6rqMRkl4qf1vCRvqqp9knwho+Zs9kpycVWdmeTGJKcmSXdfU1UXZ5SkvzvJ2d19zzCf5ya5IMl+Sd4zdAAAAAAAMHeTJuD/qbv/qapSVft292fGrmDfad19ZZLjlhn0lBXGPy/JecuUb0lyzLT1AAAAAACAWZk0Ab+1qh6S5M+TXFpVX4n21gEAAAAAYEWTPoT1mUPvS6vqA0kenOS9M6sVAAAAAAAsuEmvgE9VbUpyYJLrh6KDMmqrHQAAAAAAWGKiBHxVPS/JS5J8Mcm9Q3EneeyM6gUAAAAAAAtt0ivgn5/k0d39pVlWBgAAAAAANoq9JhzvpiRfm2VFAAAAAABgI1n1Cviq+g9D7xeSfLCq3pXkru3Du/t3Zlg3AAAAAABYWDtqguZBw+uNQ7fP0AEAAAAAAKtYNQHf3S9bq4oAAAAAAMBGMlEb8FV1aVU9ZOz9Q6vqfTOrFQAAAAAALLhJH8K6ubu/uv1Nd38lyQEzqREAAAAAAGwAkybg76mq797+pqq+J0nPpkoAAAAAALD4dvQQ1u1+LcnfVNWHhvdPTnLWbKoEAAAAAACLb4cJ+KraK8mDk3x/khOSVJIXdvcdM64bAAAAAAAsrB0m4Lv73qr6pe6+OMk716BOAAAAAACw8CZtA/7SqnpRVR1WVQ/b3s20ZgAAAAAAsMAmbQP+54fXs8fKOskjdm91AAAAAABgY5goAd/dR8y6IgAAAAAAsJFMlICvqtOXK+/uC3dvdQAAAAAAYGOYtAmaJ4z13y/JU5J8PIkEPAAAAAAALGPSJmieN/6+qh6c5H/OpEYAAAAAALAB7DXldP+Q5MjdWREAAAAAANhIJm0D/i+S9PB2ryRHJbl4VpUCAAAAAIBFN2kb8L811n93kv/d3Vt3ZcFVtSnJliQ3d/fTq+phSf4kyeFJbkjyU939lWHcc5OcmeSeJL/c3e8byo9NckGS/ZK8O8nzu7sDAAAAAABztmoTNFV1v6p6QZJTkzwmyf/q7v+1q8n3wfOTXDv2/sVJLuvuI5NcNrxPVR2V5LQkRyc5KcmrhuR9krw6yVkZNYdz5DAcAAAAAADmbkdtwL8xyXFJrkryY0l+e3cstKoOTfJvk7x2rPjkYXnbl/uMsfKLuvuu7r4+yXVJjq+qg5Ps390fGa56v3BsGgAAAAAAmKsdJeCP6u7ndPcfJjklyQ/upuX+XpJzktw7VnZgd9+aJMPrAUP5IUluGhtv61B2yNC/tPw+quqsqtpSVVtuv/323bICsCcQOzA98QPTETswPfED0xE7MB2xA5PZUQL+m9t7uvvu3bHAqnp6ktu6+4pJJ1mmrFcpv29h92u6+7juPm7z5s0TLhYQOzA98QPTETswPfED0xE7MB2xA5PZ0UNYH1dVXx/6K8l+w/tK0t29/xTLfFKSn6iqH09yvyT7V9UfJ/liVR3c3bcOzcvcNoy/NclhY9MfmuSWofzQZcoBAAAAAGDuVr0Cvrs3dff+Q/eg7t57rH+a5Hu6+9zuPrS7D8/o4arv7+7nJLkkyRnDaGckecfQf0mS06pq36o6IqOHrV4+NFNzZ1WdUFWV5PSxaQAAAAAAYK52dAX8Wnp5kour6swkNyY5NUm6+5qqujjJp5PcneTs7r5nmOa5SS5Isl+S9wwdAAAAAADM3VwT8N39wSQfHPq/lOQpK4x3XpLzlinfkuSY2dUQAAAAAACms6OHsAIAAAAAAFOQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBmQgAcAAAAAgBlY8wR8VR1WVR+oqmur6pqqev5Q/rCqurSqPje8PnRsmnOr6rqq+mxVPW2s/NiqumoY9vtVVWu9PgAAAAAAsJx5XAF/d5L/2N3fm+SEJGdX1VFJXpzksu4+Msllw/sMw05LcnSSk5K8qqo2DfN6dZKzkhw5dCet5YoAAAAAAMBK1jwB3923dvfHh/47k1yb5JAkJyd54zDaG5M8Y+g/OclF3X1Xd1+f5Lokx1fVwUn27+6PdHcnuXBsGgAAAAAAmKu5tgFfVYcn+b4kH0tyYHffmoyS9EkOGEY7JMlNY5NtHcoOGfqXlgMAAAAAwNzNLQFfVQ9M8rYkL+jur6826jJlvUr5css6q6q2VNWW22+/fecrC3sosQPTEz8wHbED0xM/MB2xA9MROzCZuSTgq+o7Mkq+v6m7/2wo/uLQrEyG19uG8q1JDhub/NAktwzlhy5Tfh/d/ZruPq67j9u8efPuWxHY4MQOTE/8wHTEDkxP/MB0xA5MR+zAZNY8AV9VleR1Sa7t7t8ZG3RJkjOG/jOSvGOs/LSq2reqjsjoYauXD83U3FlVJwzzPH1sGgAAAAAAmKu957DMJyX5mSRXVdWVQ9mvJXl5kour6swkNyY5NUm6+5qqujjJp5PcneTs7r5nmO65SS5Isl+S9wwdAAAAAADM3Zon4Lv7b7J8++1J8pQVpjkvyXnLlG9Jcszuqx0AAAAAAOwec3sIKwAAAAAAbGQS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMS8AAAAAAAMAMLn4CvqpOq6rNVdV1VvXje9QEAAAAAgGTBE/BVtSnJHyT5sSRHJXl2VR0131oBAAAAAMCCJ+CTHJ/kuu7+Qnf/c5KLkpw85zoBAAAAAECqu+ddh6lV1SlJTurufz+8/5kk/6q7f2nJeGclOWt4++gkn13Tiu5eD09yx7wrsYdb9G1wR3efNMmIGyx2ksXfdotuI3z+e2r8bIRtt+gWfRvsqbGTLP62W3SL/vlPHDvJhoufRd92G8Gib4M9dd+z6NttI1j0bbCnxk6y+Ntu0W2Ez3/Z+Fn0BPypSZ62JAF/fHc/b741m52q2tLdx827Hnsy22Bx2Xbz5fNfXLbd/NkGi8u2my+f/+Ky7ebPNlhMttv82QaLy7abr438+S96EzRbkxw29v7QJLfMqS4AAAAAAPAvFj0B/3dJjqyqI6pqnySnJblkznUCAAAAAIDsPe8K7IruvruqfinJ+5JsSvL67r5mztWatdfMuwLYBgvMtpsvn//isu3mzzZYXLbdfPn8F5dtN3+2wWKy3ebPNlhctt18bdjPf6HbgAcAAAAAgPVq0ZugAQAAAACAdUkCHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACHgAAAAAAZkACfhdU1UFVdVFVfb6qPl1V766qR1XVd1XVW4dxHl9VP74Lyzi9qq6uqmuGZbxoB+M/o6qOmnZ581RVL62qm6vqyqr6TFW9uqru8x2tqhOr6oJV5vPMquqqesxMK7yKqjqpqj5bVddV1YtXGe/1VXVbVV29pPxPhs/hyqq6oaqunHml50w87V4bLJ5uqKqrhnXZMlY+vo5X7sp3Yz0TG7vXRomNqjqsqj5QVdcO2+35S4Y/b9gPXVNV568wj9+oqk8Nn8VfVtV3DeWHV9U/jsXW/1iLdZoF8bN7bZT4GavHpqr6RFW9c0n5JPGz4j6oqs6t0THgZ6vqabNej1kQO7vXRoqdWvm47NRhW95bVcetMv2ysWPfs9PLED/3HW9dx09V3a+qLq+qTw7b7WVLhu9w3zM27ouGdXn48H5DxI/Y2b02SuwMdVgpd/b4qvro9n1SVR2/wvSvGD6DT1XV26vqIUP5msWOBPyUqqqSvD3JB7v7kd19VJJfS3Jgd9/S3acMoz4+yVQ/DlX1Y0lekOSp3X10ku9P8rUdTPaMJDP9caiqTTOc/e929+MzWof/X5J/M8U8np3kb5KcthvrNbHh8/mDJD+W0Xo8e5Uf7AuSnLS0sLuf1d2PHz6LtyX5s9nUdn0QTzOz8PE05oeGmFj6h+53t8dKd797LjWbIbExMxshNu5O8h+7+3uTnJDk7O37mqr6oSQnJ3nssE1/a4V5vKK7Hzt8Fu9M8p/Ghn1+LLZ+cWZrMUPiZ2Y2Qvxs9/wk144X7ET8JMvsg4Y4PC3J0Rkd471qxttjtxM7M7ORYme547Krk/y7JB+eYPqVjt/seyZbhvhZzPi5K8kPd/fjMtr+J1XVCcnO7Xuq6rAkP5rkxiWDFjp+xM7MbITYSVbInSU5P8nLhnX8T8P75Vya5JjufmyS/y/JuWPD1iR2JOCn90NJvtnd/3J2pLuv7O6/Hs6gXF1V+yT5z0meNZxJeVZVfa6qNidJVe1Vo6tjHr7CMs5N8qLuvmWY/z919x8N0/5CVf3dcPb0bVV1/6r6gSQ/keQVw/IeOXTvraorquqvt5+xGso/OszjP1fV3w/lNZwZurpGVzY8ayg/sUZX2r05yVU1umruX662q6rzquqXd+Pnu0+S+yX5ys5MVFUPTPKkJGdm7Mdh+KxfNZzlfGeNzqSeMgw7tqo+NHxG76uqg3ex7scnua67v9Dd/5zkoox2pvfR3R9O8uVV1qeS/FSSt+xindY78SSeWJ7YEBvL6u5bu/vjQ/+dGSURDxkGPzfJy7v7rmH4bSvM4+tjbx+QpHelTuuQ+BE/q9Xj0CT/NslrlwyaKH5WcXKSi7r7ru6+Psl1GR0bLhKxI3Z2Wndf292fndX8F4j4ET/L6pG/H95+x9BtP/bamX3P7yY5J47bxM7OWdjYSVbNnXWS/Yf+Bye5ZYXp/7K77x7efjTJobtap53W3bopuiS/nNGZpOWGHZ7k6qH/Z5O8cmzYS5K8YOh/apK3rbKMLyd58ArDvnOs/78ked7Qf0GSU8aGXZbkyKH/XyV5/9D/ziTPHvp/McnfD/0/mdGZoU1JDszorOrBSU5M8o0kR4yt48eH/r2SfH68TmPL/+skVy7T/cgy4740yc3D8K8kefMK635ikgtWGPacJK8b+v82yfcP/ackefdQ14OG+Z+S0U7vb5NsHsZ7VpLXLzPfn15hPd66zLinJHnt2PufGf8OrPZ9WWbYk5Nsmff3fdZdxNPhEU9XZpl4Gsa/PsnHk1yR5Kwl63hDkk8leX2Sh877uyw2xEbWMDaWfBduTLL/8P7KJC9L8rEkH0ryhFWmPS/JTRldubh5bH7fSPKJYfofnHcsiB/xk92/b3lrkmOHur5zrPzKTBA/WWEflOSVSZ4zNt7rxrf3InQRO4dH7FyZnTwuGxv+wSTHrbLtX5rlY+fw2PeIn40fP5uG4X+f5DfHyq/MZPuen0jy34b+G5I8fKPET8TO4RE7V2aV/z1ZJneW5HuHz/SmYV2/Z4Lv2l9kOFbLGsbO3mGtvT7JO5L8XpKfT/KGKedzTFX9lyQPSfLAJO9bOsJwpuoHkvxpVW0v3nd4fWJGt9IkyZvzrVuc/nWSt3T3PUm+WFUfSvKEJF9PcnmPruRJd99QVV+qqu/L6EfkE939paV16O4f3Mn1+t3u/q2q+o4kb62q07r7op2Y/tkZfbbJ6MrzZ2d0gPivk/xpd9+bZFtVfWAY59FJjkly6fAZbUpy6zLr8aYkb5qwDrVMWU847VLPzsa/+n1XiKfVbYR4SpIndfctVXXAMO/P9OgM+KuT/EZG8fUbSX47o+8BYmNHNkpsbP/835bRH4/tV7TvneShGTVN84QkF1fVI3o4ylyyzF9P8utVdW6SX8roT8ytSb67u79UVccm+fOqOrq//Yr5jUz8rG7h46eqnp7ktu6+oqpOXDJ40vhZaR+0O48DF43YWd3Cx85gpeOySa0UO/Y94mc1GyJ+hs/v8TVqf/rtVXVMd1+dCfY9VXX/JL+eUZJ5qT05fsTO6jZE7KziuUle2N1vq6qfyujChx9ZaeSq+vWMmvLcvuw1ix0J+Oldk9HZnZ3S3TdV1Rer6oczOlv20ztYxrFJ3r/MsAuSPKO7P1lVP5vRGaul9kry1R61hTSp5f40bPeNJe9fm9HZx4My+tG778yq/jrJg5YZ9KLu/quVFtTd36yq92Z0BfhEPw5V9Z1JfjijH87OKNC7qs7JyutVSa7p7ifuYN4/neRXlhl0XX+rLbLttiY5bOz9oUluqVFbbX8xlP2PHru1aoVl7p1RO4rHrjbeBiGexFOyfDylv3WL4G1V9faMbuX/cHd/cWyef5TRVQcbjdgQG8kKsTEcSL8tyZu6e/xZIVuT/Nnwp+3yqro3ycNr9ECv70tyS3cvbTvzzUneleQlPbr9efst0FdU1eeTPCrJliwW8SN+kuXj50lJfqJGD3G7X5L9q+qPu/s5mTB+VtkHLXscuFrd1yGxI3aSnTwuW2X+b8gEsWPfI34GGzp+tuvur1bVBzNq0/rqTLDvSfKrSY5I8skhsXloko9X1fHdvS2LHz9iR+wkO4idZZyR0TN9kuRPMzQtuHTfM5SdkeTpSZ6y/eTWWu57tAE/vfcn2beqfmF7QVU9oar+zZLx7sx9g+O1Sf44ycXDWbCV/L9Jzq+qg4b571vfagPqQUluHf58j//A/MvyhjM211fVqcP0VVWPG8b7aEa3wiTf/iCFD2fUntamGrWj9eQkl69Qv7dntMN4QpY5OzjU4Qf7Ww8zGO9W/GHYXteMzix+frXxljglyYXd/T3dfXh3H5bRLZL/OqMHRvxkjdqpOjDf+jH9bJLNVfXEYbnfUVVHL7Meb1phPZb7Yfi7JEdW1RE1aqPstCSXdPdNY9NN8mTlH0nyme7euhOfwaIST+Jp2XiqqgdU1YO292d0xcfVw/vxtuSeub18gxEbYmOl2KiMrvC4trt/Z8ngP8/oYDlV9aiM2ny8o7t/bpjf9oPQI8em+YkknxnKN9fwMKiqekSSI5N8YSc+o/VC/IifZeOnu8/t7kO7+/CMPtv39yj5nkwePyvtgy5JctrwXTgio/hZafusV2JH7Oz0cdlKJo0d+54k4mejx8/mGl35nqraL8P//WHwn2cH+57uvqq7DxjW4fCMkvbf393bNkj8iB2xs1KObTW35FsPlv3hJJ8b5r9033NSRiexfqK7/2H7xGsZOxLwU+ruzuiA4Uer6vNVdU1G7SstvcLlA0mOquEBEUPZJRnd0rLqrTE9eiL8HyT5q2H+V+Rbdy38Pxm1D3ZpvvWjnYzOZP1KVX2iqh6Z0Q/HmVX1yYzO9p08jPeCJP+hqi7PqP2prw3lb8+oPb5PZvQDeE6PzqYuV79/HtZvRz9yO+OFVXVlRgdieyd51U5M++yM6j/ubUn+z+F16zDfP8zos/vasA6nJPnN4TO6MqMfpan16MEOv5TRD+a1GX0+1yw3blW9JclHkjy6qrZW1Zljg0/LHtL8jHgST6s4MMnfDPO7PMm7uvu9w7Dza/Qgm09l9NCeF+7istYdsSE2VvGkjJ4x8sPDdr+yRlfzJqMrZh5RVVdntK3OGL5LS728Rg+E+lRGSZTtV488Ocmnhrq+NckvdveKDwxfr8SP+JnSpPGz7D5oOOa7OMmnk7w3ydm78bNfE2JH7KxixeOyqnpmVW3NqBmGd1XVssmjrHz8Zt8jflazEeLn4CQfGL77f5fk0u7efvfUpPuelSx8/IgdsbOaVXJnv5Dkt4dl/dckZ60wi1dmdCLl0uG7s/2i2DWLndq5mGZ3qKrjMmqHaWfbbtqddbh/kn/s7q6q0zJ6WMTJO5puyTz2yqjtp1O7+3OzqOcKyz0xyc9298/u5HQP7O6/r9FtNJdn1H7hsj98LA7xtGvE08YlNnaN2NiziZ9dI372XGJn14idPZv42TXiZ88ldnaN2Fkb2oBfY1X14oweErBau1Rr4dgkr6yqSvLV7OSDC6vqqIza63v7Wv4w7KJ31uiWr32S/IYfhsUnnuZKPK1jYmOuxMaCEz9zJX4WmNiZK7Gz4MTPXImfBSZ25krs7ARXwK8DNXoK76lLiv+0u8+bR33Wu6o6PMnju/vP51wV1iHxtHPE055DbOwcscE48bNzxA/biZ2dI3YYJ352jvhhO7Gzc8TO2pCABwAAAACAGfAQVgAAAAAAmIE9LgF/0kkndRKdbk/upiJ2dLp0piR+dLrpiB2dbnriR6ebjtjR6aYjdnS6dFawxyXg77jjjnlXARaS2IHpiR+YjtiB6YkfmI7YgemIHVjZHpeABwAAAACAtSABDwAAAAAAMyABDwAAAAAAMyABDwAAAAAAMyABDwAAAAAAM7D3vCsAwMZ3zjnnZNu2bTnooINy/vnnz7s6AAAAAGtiZlfAV9Xrq+q2qrp6rOxhVXVpVX1ueH3o2LBzq+q6qvpsVT1trPzYqrpqGPb7VVVD+b5V9SdD+ceq6vBZrQsAu2bbtm25+eabs23btnlXBQAAAGDNzLIJmguSnLSk7MVJLuvuI5NcNrxPVR2V5LQkRw/TvKqqNg3TvDrJWUmOHLrt8zwzyVe6+/9I8rtJfnNmawIAAAAAADtpZk3QdPeHl7kq/eQkJw79b0zywSS/OpRf1N13Jbm+qq5LcnxV3ZBk/+7+SJJU1YVJnpHkPcM0Lx3m9dYkr6yq6u6ezRoBLIZjf+XCeVfhPh50x53ZlOTGO+5cl/W74hWnz7sKAAAAwAa01g9hPbC7b02S4fWAofyQJDeNjbd1KDtk6F9a/m3TdPfdSb6W5DtnVnMAAAAAANgJa52AX0ktU9arlK82zX1nXnVWVW2pqi233377lFWEPY/YgemJH5iO2IHpiR+YjtiB6YgdmMxaJ+C/WFUHJ8nwettQvjXJYWPjHZrklqH80GXKv22aqto7yYOTfHm5hXb3a7r7uO4+bvPmzbtpVWDjEzswPfED0xE7MD3xA9MROzAdsQOTWesE/CVJzhj6z0jyjrHy06pq36o6IqOHrV4+NFNzZ1WdUFWV5PQl02yf1ylJ3q/9d4D16d59HpB79t0/9+7zgHlXBQAAAGDNzOwhrFX1loweuPrwqtqa5CVJXp7k4qo6M8mNSU5Nku6+pqouTvLpJHcnObu77xlm9dwkFyTZL6OHr75nKH9dkv85PLD1y0lOm9W6ALBrvnHkU+ddBQAAAIA1N7MEfHc/e4VBT1lh/POSnLdM+ZYkxyxT/k8ZEvgAAAAAALDezCwBDwAAe7Jzzjkn27Zty0EHHZTzzz9/3tUBAADmQAIeAABmYNu2bbn55pvnXQ2AneYEIgDsPhLwAAAstGN/5cJ5V2FZD7rjzmxKcuMdd667Ol7xitPnXQVgHXMCEYC1tNFP/ErAAwAAwByst5Nz2zmBCMBa2ugnfiXgAYAVbfQrEWCW7t3nAd/2CgAA87beTqwm6/vEb7LrJ38l4IE9giQiTGejX4kAs/SNI5867yoATMUJRADYfSTggT2CJCKLYD2e6d/oVyIAAPflBOJictERsKg2+olfCXgAYEUb/UAIAGAa6/HChAdd87lsuuvrLpwAFs5GP/ErAQ87wRUFwJ5mox8IAQAAwCxJwC8Qyd/504wJAADMlv89MB13LgKsTxLwC0TyFwAA2Oj874HpuHMRYH2SgAd2q/XY1mCyvh8kqS1EAAAAgI1JAp51ab0lSLdbz0ncRCIXAAAA5kUTWsByJOBXsB6Tq5K/AADA7rIe/1Mk/vcAi0sTWsByJOABAIANx1WIAACsBxLwsBM8VR4AYDG4ChFg41qPd8ck7uABlicBDzvBU+UXl5MnwKJyFS+wp3HcBgBsJBLwwB7ByRNgUbmKl/VuPV7hl7gKcZE5bgMWlROIwHIk4BeIH3IAmJ31mKBL1ncSUQIRAOBbnEAEliMBv0D8kAMAAAAALA4JeACAdcwdcDAdsQMAwHogAQ8AsI65Aw6mI3YAAFgP9pp3BQAAAAAAYCOSgAcAAAAAgBmYSwK+ql5YVddU1dVV9Zaqul9VPayqLq2qzw2vDx0b/9yquq6qPltVTxsrP7aqrhqG/X5V1TzWBwAAAABYTOecc05OP/30nHPOOfOuChvQmifgq+qQJL+c5LjuPibJpiSnJXlxksu6+8gklw3vU1VHDcOPTnJSkldV1aZhdq9OclaSI4fupDVcFQAAAABgwW3bti0333xztm3bNu+qsAHNqwmavZPsV1V7J7l/kluSnJzkjcPwNyZ5xtB/cpKLuvuu7r4+yXVJjq+qg5Ps390f6e5OcuHYNAAAAAAAMFd7r/UCu/vmqvqtJDcm+cckf9ndf1lVB3b3rcM4t1bVAcMkhyT56Ngstg5l3xz6l5bfR1WdldGV8vnu7/7u3bk6sKGJHZie+IHpiB2YnviB6YgdmM40sXPsr1w4yypN7UF33JlNSW684851V8crXnH6vKvALppHEzQPzeiq9iOSfFeSB1TVc1abZJmyXqX8voXdr+nu47r7uM2bN+9slWGPJXZgeuIHpiN2YHriB6YjdmA6Ygcms+ZXwCf5kSTXd/ftSVJVf5bkB5J8saoOHq5+PzjJbcP4W5McNjb9oRk1WbN16F9aDgAAAAAwkXv3ecC3vcLuNI8E/I1JTqiq+2fUBM1TkmxJ8o0kZyR5+fD6jmH8S5K8uap+J6Mr5o9Mcnl331NVd1bVCUk+luT0JP99TdcEAAAAAFho3zjyqfOuAhvYPNqA/1hVvTXJx5PcneQTSV6T5IFJLq6qMzNK0p86jH9NVV2c5NPD+Gd39z3D7J6b5IIk+yV5z9ABAAAAAMDczeMK+HT3S5K8ZEnxXRldDb/c+OclOW+Z8i1JjtntFQQAAAAAgF205g9hBQAAAACAPYEEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzIAEPAAAAAAAzMBECfiqelRVXVZVVw/vH1tV//dsqwYAAAAAAItr0ivg/yjJuUm+mSTd/akkp82qUgAAAAAAsOgmTcDfv7svX1J29+6uDAAAAAAAbBSTJuDvqKpHJukkqapTktw6s1oBAAAAAMCC23vC8c5O8pokj6mqm5Ncn+Q5M6sVAAAAAAAsuIkS8N39hSQ/UlUPSLJXd98522oBAAAAAMBim6gJmqp6flXtn+QfkvxuVX28qp467UKr6iFV9daq+kxVXVtVT6yqh1XVpVX1ueH1oWPjn1tV11XVZ6vqaWPlx1bVVcOw36+qmrZOAAAAAACwO03aBvzPd/fXkzw1yQFJfi7Jy3dhuf8tyXu7+zFJHpfk2iQvTnJZdx+Z5LLhfarqqCSnJTk6yUlJXlVVm4b5vDrJWUmOHLqTdqFOAAAAAACw20yagN9+ZfmPJ3lDd39yrGynDFfSPznJ65Kku/+5u7+a5OQkbxxGe2OSZwz9Jye5qLvv6u7rk1yX5PiqOjjJ/t39ke7uJBeOTQMAAAAAAHM1aQL+iqr6y4wS8O+rqgcluXfKZT4iye1J3lBVn6iq1w5tyx/Y3bcmyfB6wDD+IUluGpt+61B2yNC/tPw+quqsqtpSVVtuv/32KasNex6xA9MTPzAdsQPTEz8wHbED0xE7MJlJE/BnZtQkzBO6+x+S7JNRMzTT2DvJ9yd5dXd/X5JvDPNeyXJX2vcq5fct7H5Ndx/X3cdt3rx5Z+sLeyyxA9MTPzAdsQPTEz8wHbED0xE7MJm9Jxmpu++tquuTPKqq7reLy9yaZGt3f2x4/9aMEvBfrKqDu/vWoXmZ28bGP2xs+kOT3DKUH7pMOQAAAAAAzN1EV8BX1b9P8uEk70vysuH1pdMssLu3Jbmpqh49FD0lyaeTXJLkjKHsjCTvGPovSXJaVe1bVUdk9LDVy4dmau6sqhOqqpKcPjYNAAAAAADM1URXwCd5fpInJPlod/9QVT0mo0T8tJ6X5E1VtU+SL2TUnM1eSS6uqjOT3Jjk1CTp7muq6uKMkvR3Jzm7u+8Z5vPcJBck2S/Je4YOAAAAAADmbtIE/D919z9VVapq3+7+zNgV7Dutu69Mctwyg56ywvjnJTlvmfItSY6Zth4AAAAAADArkybgt1bVQ5L8eZJLq+or0d46AAAAAACsaNKHsD5z6H1pVX0gyYOTvHdmtQIAAAAAgAU36RXwqapNSQ5Mcv1QdFBGbbUDAAAAAABLTJSAr6rnJXlJki8muXco7iSPnVG9AAAAAABgoU16Bfzzkzy6u780y8oAAAAAAMBGsdeE492U5GuzrAgAAAAAAGwkq14BX1X/Yej9QpIPVtW7kty1fXh3/84M6wYAAAAAAAtrR03QPGh4vXHo9hk6AAAAAABgFasm4Lv7ZWtVEQAAAAAA2EgmagO+qi6tqoeMvX9oVb1vZrUCAAAAAIAFN+lDWDd391e3v+nuryQ5YCY1AgAAAACADWDSBPw9VfXd299U1fck6dlUCQAAAAAAFt+OHsK63a8l+Zuq+tDw/slJzppNlQAAAAAAYPHtMAFfVXsleXCS709yQpJK8sLuvmPGdQMAAAAAgIW1wwR8d99bVb/U3Rcneeca1AkAAAAAABbepG3AX1pVL6qqw6rqYdu7mdYMAAAAAAAW2KRtwP/88Hr2WFknecTurQ4AAAAAAGwMEyXgu/uIWVcEAAAAAAA2kokS8FV1+nLl3X3h7q0OAAAAAABsDJM2QfOEsf77JXlKko8nkYAHAAAAAIBlTNoEzfPG31fVg5P8z5nUCAAAAAAANoC9ppzuH5IcuTsrAgAAAAAAG8mkbcD/RZIe3u6V5KgkF8+qUgAAAAAAsOgmbQP+t8b6707yv7t7664suKo2JdmS5ObufnpVPSzJnyQ5PMkNSX6qu78yjHtukjOT3JPkl7v7fUP5sUkuSLJfkncneX53dwAAAAAAYM5WbYKmqu5XVS9IcmqSxyT5X939v3Y1+T54fpJrx96/OMll3X1kksuG96mqo5KcluToJCcledWQvE+SVyc5K6PmcI4chgMAAAAAwNztqA34NyY5LslVSX4syW/vjoVW1aFJ/m2S144Vnzwsb/tynzFWflF339Xd1ye5LsnxVXVwkv27+yPDVe8Xjk0DAAAAAABztaME/FHd/Zzu/sMkpyT5wd203N9Lck6Se8fKDuzuW5NkeD1gKD8kyU1j420dyg4Z+peW30dVnVVVW6pqy+23375bVgD2BGIHpid+YDpiB6YnfmA6YgemI3ZgMjtKwH9ze0933707FlhVT09yW3dfMekky5T1KuX3Lex+TXcf193Hbd68ecLFAmIHpid+YDpiB6YnfmA6YgemI3ZgMjt6COvjqurrQ38l2W94X0m6u/efYplPSvITVfXjSe6XZP+q+uMkX6yqg7v71qF5mduG8bcmOWxs+kOT3DKUH7pMOQAAAAAAzN2qV8B396bu3n/oHtTde4/1T5N8T3ef292HdvfhGT1c9f3d/ZwklyQ5YxjtjCTvGPovSXJaVe1bVUdk9LDVy4dmau6sqhOqqpKcPjYNAAAAAADM1Y6ugF9LL09ycVWdmeTGJKcmSXdfU1UXJ/l0kruTnN3d9wzTPDfJBUn2S/KeoQMAAAAAgLmbawK+uz+Y5IND/5eSPGWF8c5Lct4y5VuSHDO7GgIAAAAAwHR29BBWAAAAAABgChLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwAxLwAAAAAAAwA2uegK+qw6rqA1V1bVVdU1XPH8ofVlWXVtXnhteHjk1zblVdV1WfraqnjZUfW1VXDcN+v6pqrdcHAAAAAACWM48r4O9O8h+7+3uTnJDk7Ko6KsmLk1zW3UcmuWx4n2HYaUmOTnJSkldV1aZhXq9OclaSI4fupLVcEQAAAAAAWMmaJ+C7+9bu/vjQf2eSa5MckuTkJG8cRntjkmcM/Scnuai77+ru65Ncl+T4qjo4yf7d/ZHu7iQXjk0DAAAAAABzNdc24Kvq8CTfl+RjSQ7s7luTUZI+yQHDaIckuWlssq1D2SFD/9JyAAAAAACYu7kl4KvqgUneluQF3f311UZdpqxXKV9uWWdV1Zaq2nL77bfvfGVhDyV2YHriB6YjdmB64gemI3ZgOmIHJjOXBHxVfUdGyfc3dfefDcVfHJqVyfB621C+NclhY5MfmuSWofzQZcrvo7tf093Hdfdxmzdv3n0rAhuc2IHpiR+YjtiB6YkfmI7YgemIHZjMmifgq6qSvC7Jtd39O2ODLklyxtB/RpJ3jJWfVlX7VtURGT1s9fKhmZo7q+qEYZ6nj00DAAAAAABztfcclvmkJD+T5KqqunIo+7UkL09ycVWdmeTGJKcmSXdfU1UXJ/l0kruTnN3d9wzTPTfJBUn2S/KeoQMAAAAAgLlb8wR8d/9Nlm+/PUmessI05yU5b5nyLUmO2X21AwAAAACA3WNuD2EFAAAAAICNTAIeAAAAAABmQAIeAAAAAABmQAIeAAAAAABmQAIeAAAAAABmQAIeAAAAAOD/3979hfpd13Ecf73bLM2LgoRuLLSwwGIklRIFjaBaBFlpsF0EUbGQ9C4o6NLLuokQ+scg+mcUFGbFKiQiieYysRSloSyWN0kgJNZIPl3sNzuMlHM+O9999vn9Hg8Y2+/32/ntc77vPc/gfc75DRZgAQ8AAAAAAAuwgAcAAAAAgAVYwAMAAAAAwAIs4AEAAAAAYAEW8AAAAAAAsAALeAAAAAAAWIAFPAAAAAAALMACHgAAAAAAFmABDwAAAAAAC7CABwAAAACABVjAAwAAAADAAizgAQAAAABgARbwAAAAAACwAAt4AAAAAABYgAU8AAAAAAAswAIeAAAAAAAWYAEPAAAAAAALsIAHAAAAAIAFTL+Ar6oDVfVoVZ2oqs+NPg8AAAAAACSTL+Crak+SO5K8L8m1SQ5V1bVjTwUAAAAAAJMv4JNcn+REa+2x1trpJHcmuXHwmQAAAAAAINVaG32GblV1c5IDrbVPrm5/NMkNrbVbz/l9h5McXt18fZJHL+hBd9cVSZ4cfYgNN/sMnmytHdjOb1yzdpL5Zze7dbj+m9rPOsxudrPPYFPbSeaf3exmv/7bbidZu35mn906mH0Gm/pvz+xzWwezz2BT20nmn93s1uH6/99+Zl/AfyTJe89ZwF/fWrtt7MmWU1XHW2tvGX2OTWYG8zK7sVz/eZndeGYwL7Mby/Wfl9mNZwZzMrfxzGBeZjfWOl//2V+C5lSSV225fWWSJwadBQAAAAAAnjP7Av6+JNdU1dVV9eIkB5PcNfhMAAAAAACQvaMPcD5aa/+pqluTHE2yJ8mR1tpDg4+1tK+NPgBmMDGzG8v1n5fZjWcG8zK7sVz/eZndeGYwJ3MbzwzmZXZjre31n/o14AEAAAAA4GI1+0vQAAAAAADARckCHgAAAAAAFmABP4mqeraqHtjy46rRZ9okVdWq6ltbbu+tqr9X1d0jz8X26Gcc7cxNO+NoZ376GUc/c9POONqZm3bG0s/c9DPOprQz9X/CumGeaa29afQhNtjTSd5YVZe11p5J8u4kfxt8JrZPP+NoZ27aGUc789PPOPqZm3bG0c7ctDOWfuamn3E2oh1fAQ/b9/Mk71/9+lCS7w08C8xEO9BHO9BPP9BHO9BPP9Bn7duxgJ/HZVu+FeZHow+zoe5McrCqLk2yL8nvB5+H7dPPWNqZl3bG0s7c9DOWfualnbG0My/tjKefeelnrLVvx0vQzMO3wwzWWntw9Tpgh5L8bPBx2Bn9DKSdqWlnIO1MTz8D6Wdq2hlIO1PTzmD6mZp+BtqEdizgYWfuSvLFJPuTvGLsUWAq2oE+2oF++oE+2oF++oE+a92OBTzszJEkT7XW/lRV+wefBWaiHeijHeinH+ijHeinH+iz1u1YwMMOtNZOJfnS6HPAbLQDfbQD/fQDfbQD/fQDfda9nWqtjT4DAAAAAACsnReNPgAAAAAAAKwjC3gAAAAAAFiABTwAAAAAACzAAh4AAAAAABZgAQ8AAAAAAAuwgGdHquqVVfXdqnqsqv5QVb+rqg/twvPur6q7d+OMcDHSDvTRDvTTD/TRDvTTD/TRznqzgGfbqqqS/DjJb1prr2mtvTnJwSRXDjjL3gv9Z0Iv7UAf7UA//UAf7UA//UAf7aw/C3h24l1JTrfWvnL2jtbaydbal6tqT1V9oaruq6oHq+pTyXOfaft1Vf2wqh6pqu+sPrCkqg6s7vttkg+ffc6quryqjqye649VdePq/o9V1Q+q6idJfnFB33M4P9qBPtqBfvqBPtqBfvqBPtpZcz6rwU68Icn9z/PYJ5I81Vp7a1W9JMm9VXU22utWb/tEknuTvL2qjif5es58kDmR5PtbnuvzSe5prX28ql6e5FhV/Wr12NuS7Gut/WMX3y9Ymnagj3agn36gj3agn36gj3bWnAU83arqjiTvSHI6yckk+6rq5tXDL0tyzeqxY621U6u3eSDJVUn+meTx1tpfVvd/O8nh1du+J8kHquozq9uXJnn16te/9MGA2WkH+mgH+ukH+mgH+ukH+mhn/VjAsxMPJbnp7I3W2qer6ookx5P8NcltrbWjW9+gqvYn+feWu57N//7etef5cyrJTa21R895rhuSPH0e54dRtAN9tAP99AN9tAP99AN9tLPmvAY8O3FPkkur6pYt97109fPRJLdU1SVJUlWvq6rLX+C5HklydVW9dnX70JbHjia5bctrV123K6eHcbQDfbQD/fQDfbQD/fQDfbSz5izg2bbWWkvywSTvrKrHq+pYkm8m+WySbyR5OMn9VfXnJF/NC3yHRWvtXznzLTA/rTP/KcTJLQ/fnuSSJA+unuv2Bd4duGC0A320A/30A320A/30A320s/7qzIwBAAAAAIDd5CvgAQAAAABgARbwAAAAAACwAAt4AAAAAABYgAU8AAAAAAAswAIeAAAAAAAWYAEPAAAAAAALsIAHAAAAAIAF/BcaXK77LWxRzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1512x648 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age, city category followed by Gender and Purchase.\n",
    "\n",
    "g = sns.FacetGrid(combined, row = \"City_Category\", col = \"Age\") # Facetgrid Object\n",
    "g.map(sns.barplot, \"Gender\", \"Purchase\") # func, x, y\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d263192",
   "metadata": {},
   "source": [
    "* There are two age groups in the City Cat_A 26-35 and 18-25 for which the females have made lot of purchases.\n",
    "* We can say that Age Bracket 26-35 and 18-25 in A City are the potential buyers..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e3df6ab9",
   "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>Stay_In_Current_City</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>2.0</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>2.0</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>2.0</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>2.0</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>4.0</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  Stay_In_Current_City  \n",
       "0                 NaN                 NaN    8370.0                   2.0  \n",
       "1                 6.0                14.0   15200.0                   2.0  \n",
       "2                 NaN                 NaN    1422.0                   2.0  \n",
       "3                14.0                 NaN    1057.0                   2.0  \n",
       "4                 NaN                 NaN    7969.0                   4.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7fb9ba4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    783667.000000\n",
       "mean          5.366196\n",
       "std           3.878160\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           5.000000\n",
       "75%           8.000000\n",
       "max          20.000000\n",
       "Name: Product_Category_1, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Product_Category_1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "03f4c9cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Deploying Statistical Test to Check the relation between marital stats and product_cat1\n",
    "\n",
    "# Ho: That Prod Cat is Independent of the Gender\n",
    "# Ha: That Prod Cat is Dependent on the Gender\n",
    "\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Tabulate the Data\n",
    "tabulate = pd.crosstab(combined.Gender, combined.Product_Category_1)\n",
    "\n",
    "# Deploy the Test\n",
    "teststats, pvalue, df, exp_freq = stats.chi2_contingency(tabulate)\n",
    "\n",
    "# Print the Pvalue\n",
    "print(pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "d2f51845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Gender'>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tabulate.plot(kind =\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "9a422dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_3', ylabel='count'>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# With Product Category, Take Where the Gender == Male, Product Category median value\n",
    "\n",
    "sns.countplot(combined.Product_Category_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4672fb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    53621.000000\n",
       "mean        12.463475\n",
       "std          4.125628\n",
       "min          3.000000\n",
       "25%          9.000000\n",
       "50%         14.000000\n",
       "75%         16.000000\n",
       "max         18.000000\n",
       "Name: Product_Category_3, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.loc[combined.Gender==\"F\", \"Product_Category_3\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "46e8220a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Does City Category has any role in Product Category\n",
    "\n",
    "# Ho: That Prod Cat is Independent of the City Category\n",
    "# Ha: That Prod Cat is Dependent on the City Category\n",
    "\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Tabulate the Data\n",
    "tabulate = pd.crosstab(combined.City_Category, combined.Product_Category_1)\n",
    "\n",
    "# Deploy the Test\n",
    "teststats, pvalue, df, exp_freq = stats.chi2_contingency(tabulate)\n",
    "\n",
    "# Print the Pvalue\n",
    "print(pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "43eab298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='City_Category'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tabulate.plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "7de6cbd4",
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Category</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>F</th>\n",
       "      <td>33582.0</td>\n",
       "      <td>10.124710</td>\n",
       "      <td>4.798571</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>106543.0</td>\n",
       "      <td>9.895713</td>\n",
       "      <td>5.092870</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>F</th>\n",
       "      <td>55225.0</td>\n",
       "      <td>9.994006</td>\n",
       "      <td>4.853629</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>169990.0</td>\n",
       "      <td>9.768433</td>\n",
       "      <td>5.148369</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">C</th>\n",
       "      <th>F</th>\n",
       "      <td>42014.0</td>\n",
       "      <td>9.923502</td>\n",
       "      <td>4.950752</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>130331.0</td>\n",
       "      <td>9.740852</td>\n",
       "      <td>5.215808</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count       mean       std  min  25%   50%   75%  \\\n",
       "City_Category Gender                                                        \n",
       "A             F        33582.0  10.124710  4.798571  2.0  6.0  11.0  14.0   \n",
       "              M       106543.0   9.895713  5.092870  2.0  5.0   9.0  15.0   \n",
       "B             F        55225.0   9.994006  4.853629  2.0  5.0  10.0  14.0   \n",
       "              M       169990.0   9.768433  5.148369  2.0  5.0   9.0  15.0   \n",
       "C             F        42014.0   9.923502  4.950752  2.0  5.0  10.0  14.0   \n",
       "              M       130331.0   9.740852  5.215808  2.0  5.0   9.0  15.0   \n",
       "\n",
       "                       max  \n",
       "City_Category Gender        \n",
       "A             F       18.0  \n",
       "              M       18.0  \n",
       "B             F       18.0  \n",
       "              M       18.0  \n",
       "C             F       18.0  \n",
       "              M       18.0  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.groupby([\"City_Category\", \"Gender\"])[\"Product_Category_2\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "c95c7e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute the Missing Values in Prod_Cat_2\n",
    "\n",
    "# City Condition\n",
    "ca = combined.City_Category==\"A\"\n",
    "cb = combined.City_Category==\"B\"\n",
    "cc = combined.City_Category==\"C\"\n",
    "\n",
    "\n",
    "# Gender\n",
    "c_m = combined.Gender==\"M\"\n",
    "c_f = combined.Gender==\"F\"\n",
    "\n",
    "combined.loc[(ca) & (c_m) &(combined[\"Product_Category_2\"].isnull()), \n",
    "             \"Product_Category_2\"] = 9.0\n",
    "\n",
    "combined.loc[(ca) & (c_f) &(combined[\"Product_Category_2\"].isnull()), \n",
    "             \"Product_Category_2\"]=11.0\n",
    "\n",
    "combined.loc[(cb) & (c_m) &(combined[\"Product_Category_2\"].isnull()),\n",
    "             \"Product_Category_2\"]=9.0\n",
    "\n",
    "combined.loc[(cb) & (c_f) &(combined[\"Product_Category_2\"].isnull()),\n",
    "             \"Product_Category_2\"]=10.0\n",
    "\n",
    "combined.loc[(cc) & (c_m) &(combined[\"Product_Category_2\"].isnull()),\n",
    "             \"Product_Category_2\"]=9.0\n",
    "combined.loc[(cc) & (c_f) &(combined[\"Product_Category_2\"].isnull()),\n",
    "             \"Product_Category_2\"]=10.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "f05694f8",
   "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",
       "Product_Category_3            545809\n",
       "Purchase                      233599\n",
       "Stay_In_Current_City               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "fb163804",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputate the missing value in catergory 2\n",
    "ca=combined.City_Category=='A'\n",
    "cb=combined.City_Category=='B'\n",
    "cc=combined.City_Category=='C'\n",
    "\n",
    "# Gender\n",
    "c_m=combined.Gender=='M'\n",
    "c_f=combined.Gender=='F'\n",
    "\n",
    "combined.loc[(ca) & (c_m) &(combined[\"Product_Category_3\"].isnull()), \n",
    "             \"Product_Category_3\"] = 15.0\n",
    "\n",
    "combined.loc[(ca) & (c_f) &(combined[\"Product_Category_3\"].isnull()), \n",
    "             \"Product_Category_3\"]=14.0\n",
    "\n",
    "combined.loc[(cb) & (c_m) &(combined[\"Product_Category_3\"].isnull()),\n",
    "             \"Product_Category_3\"]=15.0\n",
    "\n",
    "combined.loc[(cb) & (c_f)&(combined[\"Product_Category_3\"].isnull()),\n",
    "             \"Product_Category_3\"]=14.0\n",
    "\n",
    "combined.loc[(cc) & (c_m)&(combined[\"Product_Category_3\"].isnull()),\n",
    "             \"Product_Category_3\"]=15.0\n",
    "\n",
    "combined.loc[(cc) & (c_f)&(combined[\"Product_Category_3\"].isnull()),\n",
    "             \"Product_Category_3\"]=14.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "bbb35c73",
   "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",
       "Product_Category_3                 0\n",
       "Purchase                      233599\n",
       "Stay_In_Current_City               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "271ada42",
   "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>Stay_In_Current_City</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>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>2.0</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>2.0</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>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>2.0</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>14.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>2.0</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>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>4.0</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  Stay_In_Current_City  \n",
       "0                11.0                14.0    8370.0                   2.0  \n",
       "1                 6.0                14.0   15200.0                   2.0  \n",
       "2                11.0                14.0    1422.0                   2.0  \n",
       "3                14.0                14.0    1057.0                   2.0  \n",
       "4                 9.0                15.0    7969.0                   4.0  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "f6f6700b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Top5 Products\n",
    "prod = combined.Product_ID.value_counts().index[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b86a10db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def products(x):\n",
    "    if x in prod:\n",
    "        return(\"Top5\")\n",
    "    else:\n",
    "        return(\"Others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "0bb8196b",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Prod_ID\"] = combined.Product_ID.apply(products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "4e8ed4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Prod_ID', ylabel='Purchase'>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = \"Prod_ID\", y = \"Purchase\", data = combined)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b42a1a",
   "metadata": {},
   "source": [
    "### Dropping the Unnecessary Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "f1d38226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
       "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
       "       'Product_Category_2', 'Product_Category_3', 'Purchase',\n",
       "       'Stay_In_Current_City', 'Prod_ID'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "9394eeba",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = combined.drop(['User_ID', 'Product_ID', 'Stay_In_Current_City_Years'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "b36d0cd1",
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</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>Stay_In_Current_City</th>\n",
       "      <th>Prod_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</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>2.0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender   Age  Occupation City_Category Marital_Status  Product_Category_1  \\\n",
       "0      F  0-17          10             A              0                   3   \n",
       "1      F  0-17          10             A              0                   1   \n",
       "2      F  0-17          10             A              0                  12   \n",
       "3      F  0-17          10             A              0                  12   \n",
       "4      M    55          16             C              0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  Stay_In_Current_City  \\\n",
       "0                11.0                14.0    8370.0                   2.0   \n",
       "1                 6.0                14.0   15200.0                   2.0   \n",
       "2                11.0                14.0    1422.0                   2.0   \n",
       "3                14.0                14.0    1057.0                   2.0   \n",
       "4                 9.0                15.0    7969.0                   4.0   \n",
       "\n",
       "  Prod_ID  \n",
       "0  Others  \n",
       "1  Others  \n",
       "2  Others  \n",
       "3  Others  \n",
       "4  Others  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "f1a9c4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.Age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "5214e494",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "new[\"Age_Encoded\"] = le.fit_transform(new.Age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "48e426ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying pd.get_dummies\n",
    "\n",
    "dummydata = pd.get_dummies(new.drop(\"Age\", axis=1), drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "e612826e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(550068, 12) (233599, 12)\n"
     ]
    }
   ],
   "source": [
    "# Split the Data in newtrain and newtest\n",
    "\n",
    "newtrain = dummydata.loc[0:train.shape[0]-1, :]\n",
    "newtest = dummydata.loc[train.shape[0]:, :]\n",
    "\n",
    "print(newtrain.shape, newtest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a7e564c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((550068, 12), (233599, 11))"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "3c46ff3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the Purchase variable from new test and define the X and y\n",
    "\n",
    "newtest = newtest.drop(\"Purchase\", axis = 1)\n",
    "X = newtrain.drop(\"Purchase\", axis = 1)\n",
    "y = newtrain.Purchase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d90ba24",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "39d8e285",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import VotingRegressor, BaggingRegressor, RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
    "#import lightgbm as lgbm\n",
    "\n",
    "# Models\n",
    "lr = LinearRegression()\n",
    "dtree = DecisionTreeRegressor()\n",
    "rf = RandomForestRegressor()\n",
    "gbm = GradientBoostingRegressor()\n",
    "ada = AdaBoostRegressor()\n",
    "bag = BaggingRegressor()\n",
    "xgb_r = xgb.XGBRegressor()\n",
    "#lgbr = lgbm.LGBMRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "59f7d633",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear Regression\n",
    "\n",
    "pred_lr = lr.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_lr, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"LRMODEL.csv\", index=False) # 4687.193511838603"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "b6bc96dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mukulchauhan\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "d4c60830",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision Tree Model\n",
    "\n",
    "pred_tree = dtree.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_tree, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"TREEMODEL.csv\", index=False) # 3256.1745641335124"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "a19ab711",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Model\n",
    "\n",
    "pred_rf = rf.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_rf, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"RFMODEL.csv\", index=False) # 3008.490038248746"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "9dacc803",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bagging Regressor\n",
    "\n",
    "pred_bag = bag.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_bag, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"BAGMODEL.csv\", index=False) # 3046.0216137657508"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "df612385",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ADABOOST Regressor\n",
    "\n",
    "pred_ada = ada.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_ada, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"ADAMODEL.csv\", index=False) # 3822 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "8b18f0ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GBM Regressor\n",
    "\n",
    "pred_gbm = gbm.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_gbm, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"GBMMODEL.csv\", index=False) # 2980"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "3990172a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# XGBM\n",
    "\n",
    "pred_xgbr = xgb_r.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_xgbr, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"XGBMODEL.csv\", index=False) # 2866"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55b4120",
   "metadata": {},
   "source": [
    "#### Tuning the XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "bab7948c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = [{\"learning_rate\":[0.1, 0.2, 0.01, 0.3, 0.5],\n",
    "          \"max_depth\":[4, 5, 6, 8]}]\n",
    "\n",
    "xgb_r = xgb.XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "7a02d1d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.3, 'max_depth': 8}\n"
     ]
    }
   ],
   "source": [
    "# ...sshhh the machine is learning...\n",
    "grid = GridSearchCV(estimator=xgb_r, param_grid=params, cv = 5)\n",
    "\n",
    "# Fitting the Grid Search Model\n",
    "grid.fit(X, y)\n",
    "\n",
    "#Printing the best parameters..\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "8fc45811",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets Build a Tuned Model\n",
    "xgb_r = xgb.XGBRegressor(learning_rate = 0.3, max_depth = 8)\n",
    "\n",
    "pred_xgbr = xgb_r.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_xgbr, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"XGBTunedMODEL.csv\", index=False) # 2838"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "4f05eae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = xgb_r.feature_importances_, \n",
    "                  columns = [\"Feat_imp\"], index = xgb_r.feature_names_in_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "77576170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sort_values(by = \"Feat_imp\", ascending = False,kind = \"quicksort\").plot(kind = \"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "8c175e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting Top2 Features\n",
    "subset = X.loc[:, [\"Product_Category_1\", \"Prod_ID_Top5\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "3654916d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets Build a Tuned Model on Subset of the Data\n",
    "xgb_r = xgb.XGBRegressor(learning_rate = 0.3, max_depth = 8)\n",
    "\n",
    "pred_xgbr = xgb_r.fit(subset, y).predict(newtest.loc[:, [\"Product_Category_1\", \n",
    "                                                         \"Prod_ID_Top5\"]])\n",
    "solution = pd.DataFrame({\"Purchase\":pred_xgbr, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"XGBSubsetMODEL.csv\", index=False) # 3026"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024bc54e",
   "metadata": {},
   "source": [
    "#### Cross Validation Model - XGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "167e62ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import KFold\n",
    "\n",
    "kfold = KFold(n_splits = 5, shuffle = True)\n",
    "\n",
    "xgbr = xgb.XGBRegressor(learning_rate = 0.3, max_depth = 8)\n",
    "pred_model = pd.DataFrame()\n",
    "\n",
    "n = 10\n",
    "for i in range(n):\n",
    "    folds = next(kfold.split(X)) # Splitting the data in even groups...\n",
    "    xtrain = X.iloc[folds[0]]\n",
    "    ytrain = y.iloc[folds[0]]\n",
    "    pred_model[i] = xgbr.fit(xtrain, ytrain).predict(newtest)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "d53cb561",
   "metadata": {},
   "outputs": [],
   "source": [
    "XGB_cv = pred_model.mean(axis=1)\n",
    "\n",
    "solution = pd.DataFrame({\"Purchase\":XGB_cv, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"XGB_CV25.csv\", index=False) # 2831"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "2aac9574",
   "metadata": {},
   "outputs": [],
   "source": [
    "import catboost as cb\n",
    "\n",
    "cbr = cb.CatBoostRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "eab15c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.11096\n",
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      "988:\tlearn: 2843.5142554\ttotal: 27.7s\tremaining: 308ms\n",
      "989:\tlearn: 2843.4343688\ttotal: 27.7s\tremaining: 280ms\n",
      "990:\tlearn: 2843.3709702\ttotal: 27.7s\tremaining: 252ms\n",
      "991:\tlearn: 2843.3241588\ttotal: 27.7s\tremaining: 224ms\n",
      "992:\tlearn: 2843.2959439\ttotal: 27.8s\tremaining: 196ms\n",
      "993:\tlearn: 2843.2636023\ttotal: 27.8s\tremaining: 168ms\n",
      "994:\tlearn: 2843.2428300\ttotal: 27.8s\tremaining: 140ms\n",
      "995:\tlearn: 2843.2034440\ttotal: 27.9s\tremaining: 112ms\n",
      "996:\tlearn: 2843.1740329\ttotal: 27.9s\tremaining: 83.9ms\n",
      "997:\tlearn: 2843.1578071\ttotal: 27.9s\tremaining: 56ms\n",
      "998:\tlearn: 2843.1211391\ttotal: 28s\tremaining: 28ms\n",
      "999:\tlearn: 2843.0855459\ttotal: 28s\tremaining: 0us\n"
     ]
    }
   ],
   "source": [
    "pred_cbr = cbr.fit(X, y).predict(newtest)\n",
    "solution = pd.DataFrame({\"Purchase\":pred_cbr, \n",
    "                         \"User_ID\":test.User_ID, \"Product_ID\": test.Product_ID})\n",
    "solution.to_csv(\"Catboost.csv\", index=False) # 2870"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8dbb2af",
   "metadata": {},
   "source": [
    "Conclusion\n",
    "\n",
    "**The Best Model is XGB with CV =10 and Learning Rate = 0.3 and Max Depth = 8.**"
   ]
  }
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