61. Customer Behaviour.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28b76ad4",
   "metadata": {},
   "source": [
    "# Customer Behaviour Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fe382e",
   "metadata": {},
   "source": [
    "## Importing Required Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39c63751",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec67c3a6",
   "metadata": {},
   "source": [
    "## Getting the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49a50ab1",
   "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",
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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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0  15624510    Male   19            19000          0\n",
       "1  15810944    Male   35            20000          0\n",
       "2  15668575  Female   26            43000          0\n",
       "3  15603246  Female   27            57000          0\n",
       "4  15804002    Male   19            76000          0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/proj61/Customer_Behaviour.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530645b1",
   "metadata": {},
   "source": [
    "## Cleaning dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d336b7cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User ID            0\n",
       "Gender             0\n",
       "Age                0\n",
       "EstimatedSalary    0\n",
       "Purchased          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9d6bcd",
   "metadata": {},
   "source": [
    "## Pre Processing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca38ae54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"User ID\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44670c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "df[\"Gender\"] = le.fit_transform(df[\"Gender\"])\n",
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a6f442d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 320)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(\"Purchased\", axis=1)\n",
    "y = df[\"Purchased\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "len(X_train), len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed9809b",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ca67d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.925"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rclf = RandomForestClassifier()\n",
    "rclf.fit(X_train.values, y_train.values)\n",
    "rclf.score(X_test.values, y_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3417983d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9125"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbclf = XGBClassifier().fit(X_train.values, y_train.values)\n",
    "\n",
    "xgbclf.score(X_test.values, y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65687d63",
   "metadata": {},
   "source": [
    "\n",
    "## Making Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e309f0d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rclf.predict([[1, 29, 84001]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6099666d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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