Main Amazon user segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iIDXUkMElkDd"
   },
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hj40LjUilpix"
   },
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zApSnwUkl9eD"
   },
   "source": [
    "## Importing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\prasa\\Desktop\\DSA course\\Amazon user segmentation\\Amazon-user-segmentation-main\\Amazon.com cluster model.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Cus_ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301219</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>53667</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>301220</td>\n",
       "      <td>F</td>\n",
       "      <td>32</td>\n",
       "      <td>50420</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>301221</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>62896</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>301222</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>55306</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>301223</td>\n",
       "      <td>F</td>\n",
       "      <td>34</td>\n",
       "      <td>20634</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cus_ID Sex  Age  Income  Rating\n",
       "0  301219   M   23   53667     150\n",
       "1  301220   F   32   50420     106\n",
       "2  301221   M   23   62896      10\n",
       "3  301222   M   32   55306      22\n",
       "4  301223   F   34   20634     117"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(202, 5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cus_ID     int64\n",
       "Sex       object\n",
       "Age        int64\n",
       "Income     int64\n",
       "Rating     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Cus_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>301319.500000</td>\n",
       "      <td>31.663366</td>\n",
       "      <td>46520.727723</td>\n",
       "      <td>79.668317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>58.456537</td>\n",
       "      <td>8.898295</td>\n",
       "      <td>15035.827667</td>\n",
       "      <td>42.787150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>301219.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20159.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>301269.250000</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>33794.250000</td>\n",
       "      <td>43.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>301319.500000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>47619.500000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>301369.750000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>59778.750000</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>301420.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>69893.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Cus_ID         Age        Income      Rating\n",
       "count     202.000000  202.000000    202.000000  202.000000\n",
       "mean   301319.500000   31.663366  46520.727723   79.668317\n",
       "std        58.456537    8.898295  15035.827667   42.787150\n",
       "min    301219.000000   18.000000  20159.000000    3.000000\n",
       "25%    301269.250000   23.250000  33794.250000   43.250000\n",
       "50%    301319.500000   29.000000  47619.500000   79.000000\n",
       "75%    301369.750000   39.000000  59778.750000  117.000000\n",
       "max    301420.000000   56.000000  69893.000000  150.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XaG0s7WjmgPU"
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "## Handling Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cus_ID    0\n",
       "Sex       0\n",
       "Age       0\n",
       "Income    0\n",
       "Rating    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o0syRbsSmkoe"
   },
   "source": [
    "## Encoding Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Cus_ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301219</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>53667</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>301220</td>\n",
       "      <td>F</td>\n",
       "      <td>32</td>\n",
       "      <td>50420</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>301221</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>62896</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>301222</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>55306</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>301223</td>\n",
       "      <td>F</td>\n",
       "      <td>34</td>\n",
       "      <td>20634</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>301416</td>\n",
       "      <td>M</td>\n",
       "      <td>24</td>\n",
       "      <td>30710</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>301417</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>55340</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>301418</td>\n",
       "      <td>M</td>\n",
       "      <td>26</td>\n",
       "      <td>41269</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>301419</td>\n",
       "      <td>F</td>\n",
       "      <td>28</td>\n",
       "      <td>64338</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>301420</td>\n",
       "      <td>M</td>\n",
       "      <td>28</td>\n",
       "      <td>21533</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cus_ID Sex  Age  Income  Rating\n",
       "0    301219   M   23   53667     150\n",
       "1    301220   F   32   50420     106\n",
       "2    301221   M   23   62896      10\n",
       "3    301222   M   32   55306      22\n",
       "4    301223   F   34   20634     117\n",
       "..      ...  ..  ...     ...     ...\n",
       "197  301416   M   24   30710     115\n",
       "198  301417   F   25   55340      66\n",
       "199  301418   M   26   41269     142\n",
       "200  301419   F   28   64338      82\n",
       "201  301420   M   28   21533     144\n",
       "\n",
       "[202 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:, 3:5].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(202, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elbow method\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prasa\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i, init='k-means++',  random_state=21)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('elbow method')\n",
    "plt.xlabel('No of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans=KMeans(n_clusters=4, init='k-means++',  random_state=21)\n",
    "y_means=kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 1, 2, 0, 2, 3, 1, 0, 1, 3, 2, 0, 2, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       3, 0, 3, 1, 2, 2, 1, 3, 1, 0, 0, 1, 1, 0, 0, 2, 0, 1, 1, 2, 3, 2,\n",
       "       3, 3, 2, 2, 0, 0, 0, 2, 0, 2, 1, 0, 0, 0, 1, 1, 2, 2, 3, 0, 1, 3,\n",
       "       1, 0, 1, 3, 2, 3, 1, 2, 0, 0, 3, 2, 0, 2, 3, 1, 3, 0, 3, 2, 0, 1,\n",
       "       3, 3, 3, 1, 1, 0, 1, 0, 1, 2, 3, 3, 3, 2, 1, 3, 3, 1, 2, 1, 2, 3,\n",
       "       0, 2, 3, 1, 1, 1, 3, 3, 2, 1, 2, 1, 2, 3, 2, 2, 2, 2, 3, 1, 3, 3,\n",
       "       1, 0, 1, 3, 2, 2, 0, 1, 2, 2, 0, 3, 0, 2, 2, 2, 2, 0, 1, 3, 0, 1,\n",
       "       3, 3, 2, 0, 2, 2, 3, 0, 0, 2, 1, 0, 1, 3, 2, 2, 3, 0, 1, 3, 2, 1,\n",
       "       2, 0, 2, 1, 2, 3, 3, 0, 3, 3, 0, 2, 1, 3, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       2, 3, 1, 0])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y_means==0,0],X[y_means==0,1], s=100, c='red', label='Cluster1')\n",
    "plt.scatter(X[y_means==1,0],X[y_means==1,1], s=100, c='blue', label='Cluster2')\n",
    "plt.scatter(X[y_means==2,0],X[y_means==2,1], s=100, c='yellow', label='Cluster3')\n",
    "plt.scatter(X[y_means==3,0],X[y_means==3,1], s=100, c='green', label='Cluster4')\n",
    "#plt.scatter(kmeans.cluster_centers_[:0], kmeans.cluster_centers_[:1], s=100, c='black', label='Centroids')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids')\n",
    "\n",
    "plt.title('Cluster of amazon users')\n",
    "plt.xlabel('Annual income')\n",
    "plt.ylabel('Purchase rating')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for cluster 1 (Red): Mostly equal distributin & around 40% have very high tendency to buy\n",
    "\n",
    "for cluster 2(green) : Medium to high tendency to buy(~70%), people who have low tendency to buy are very less\n",
    "\n",
    "for  cluster 3(yellow): Mostly medium level of tendency to buy since number of points in higher and lower brackets are less\n",
    "\n",
    "for cluster 4(blue) : Almost equal distributtion but to some extent around 30% of the customers \n",
    "prefer not to buy. maybe due to the delay of arrival of the product etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Based on AGE and rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cus_ID', 'Sex', 'Age', 'Income', 'Rating'], dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_mapping = {'M': 1, 'F': 0}\n",
    "\n",
    "# Apply the mapping to the 'gender' column\n",
    "df['Sex'] = df['Sex'].map(gender_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Cus_ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301219</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>53667</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>301220</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>50420</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>301221</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>62896</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>301222</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>55306</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>301223</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>20634</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>301416</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>30710</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>301417</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>55340</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>301418</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>41269</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>301419</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>64338</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>301420</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>21533</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cus_ID  Sex  Age  Income  Rating\n",
       "0    301219    1   23   53667     150\n",
       "1    301220    0   32   50420     106\n",
       "2    301221    1   23   62896      10\n",
       "3    301222    1   32   55306      22\n",
       "4    301223    0   34   20634     117\n",
       "..      ...  ...  ...     ...     ...\n",
       "197  301416    1   24   30710     115\n",
       "198  301417    0   25   55340      66\n",
       "199  301418    1   26   41269     142\n",
       "200  301419    0   28   64338      82\n",
       "201  301420    1   28   21533     144\n",
       "\n",
       "[202 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = df.iloc[:, [2, 4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 23, 150],\n",
       "       [ 32, 106],\n",
       "       [ 23,  10],\n",
       "       [ 32,  22],\n",
       "       [ 34, 117],\n",
       "       [ 43,   6],\n",
       "       [ 43, 123],\n",
       "       [ 22, 133],\n",
       "       [ 34, 108],\n",
       "       [ 23,  62],\n",
       "       [ 29, 125],\n",
       "       [ 28,  73],\n",
       "       [ 27,  44],\n",
       "       [ 25,  66],\n",
       "       [ 26,  30],\n",
       "       [ 38,  38],\n",
       "       [ 23, 128],\n",
       "       [ 21, 130],\n",
       "       [ 21,  59],\n",
       "       [ 22,  42],\n",
       "       [ 20,   3],\n",
       "       [ 31,  30],\n",
       "       [ 32,  57],\n",
       "       [ 33,  97],\n",
       "       [ 30,  75],\n",
       "       [ 39,  16],\n",
       "       [ 37,   9],\n",
       "       [ 35, 131],\n",
       "       [ 30, 149],\n",
       "       [ 31,  48],\n",
       "       [ 29,  76],\n",
       "       [ 28, 105],\n",
       "       [ 35,  76],\n",
       "       [ 22,  54],\n",
       "       [ 20, 145],\n",
       "       [ 23, 144],\n",
       "       [ 21, 122],\n",
       "       [ 27,  71],\n",
       "       [ 28, 121],\n",
       "       [ 23, 132],\n",
       "       [ 29,  14],\n",
       "       [ 36,  34],\n",
       "       [ 39, 145],\n",
       "       [ 31,  54],\n",
       "       [ 37,  84],\n",
       "       [ 39,  61],\n",
       "       [ 20,  70],\n",
       "       [ 29, 148],\n",
       "       [ 35,  56],\n",
       "       [ 23,  84],\n",
       "       [ 34,  48],\n",
       "       [ 34,   9],\n",
       "       [ 23,  16],\n",
       "       [ 34,  55],\n",
       "       [ 32,  73],\n",
       "       [ 23, 120],\n",
       "       [ 19,  59],\n",
       "       [ 19,  41],\n",
       "       [ 23,   9],\n",
       "       [ 24, 121],\n",
       "       [ 25, 101],\n",
       "       [ 26, 128],\n",
       "       [ 27,   4],\n",
       "       [ 28,  27],\n",
       "       [ 29,  12],\n",
       "       [ 20, 130],\n",
       "       [ 39, 135],\n",
       "       [ 38,  97],\n",
       "       [ 34,  69],\n",
       "       [ 35,  47],\n",
       "       [ 36,  51],\n",
       "       [ 37, 101],\n",
       "       [ 38,  62],\n",
       "       [ 32, 113],\n",
       "       [ 32, 146],\n",
       "       [ 20,  18],\n",
       "       [ 27, 129],\n",
       "       [ 39, 113],\n",
       "       [ 25,  30],\n",
       "       [ 26, 128],\n",
       "       [ 27, 116],\n",
       "       [ 28,  30],\n",
       "       [ 26,  81],\n",
       "       [ 25, 149],\n",
       "       [ 26, 117],\n",
       "       [ 27,  23],\n",
       "       [ 28,  42],\n",
       "       [ 25,  80],\n",
       "       [ 27,  57],\n",
       "       [ 28, 136],\n",
       "       [ 29,  17],\n",
       "       [ 24, 105],\n",
       "       [ 23,  21],\n",
       "       [ 29, 128],\n",
       "       [ 29,  80],\n",
       "       [ 29,  97],\n",
       "       [ 29,  29],\n",
       "       [ 23, 108],\n",
       "       [ 23, 120],\n",
       "       [ 23, 142],\n",
       "       [ 23,  31],\n",
       "       [ 23, 124],\n",
       "       [ 23,  66],\n",
       "       [ 23,  48],\n",
       "       [ 23, 141],\n",
       "       [ 33, 113],\n",
       "       [ 43,  80],\n",
       "       [ 42, 136],\n",
       "       [ 45,  53],\n",
       "       [ 46, 103],\n",
       "       [ 47,  19],\n",
       "       [ 45, 122],\n",
       "       [ 34, 131],\n",
       "       [ 43,  51],\n",
       "       [ 34,  27],\n",
       "       [ 43,  46],\n",
       "       [ 45,  34],\n",
       "       [ 54, 115],\n",
       "       [ 45, 110],\n",
       "       [ 54, 118],\n",
       "       [ 56,  91],\n",
       "       [ 45,  20],\n",
       "       [ 34,  79],\n",
       "       [ 43,  80],\n",
       "       [ 34, 128],\n",
       "       [ 43, 106],\n",
       "       [ 34, 146],\n",
       "       [ 43,  43],\n",
       "       [ 43,  48],\n",
       "       [ 43, 117],\n",
       "       [ 43,  79],\n",
       "       [ 43,  52],\n",
       "       [ 43,  17],\n",
       "       [ 43,  86],\n",
       "       [ 43,  57],\n",
       "       [ 43,  88],\n",
       "       [ 43,  19],\n",
       "       [ 23,  67],\n",
       "       [ 23, 119],\n",
       "       [ 23,  36],\n",
       "       [ 23,  41],\n",
       "       [ 23, 120],\n",
       "       [ 23,  58],\n",
       "       [ 23,  72],\n",
       "       [ 23, 102],\n",
       "       [ 23,  23],\n",
       "       [ 32, 100],\n",
       "       [ 23,  51],\n",
       "       [ 32,  49],\n",
       "       [ 32, 144],\n",
       "       [ 23, 104],\n",
       "       [ 23,  31],\n",
       "       [ 23, 103],\n",
       "       [ 48,  21],\n",
       "       [ 48,  69],\n",
       "       [ 47,  55],\n",
       "       [ 47, 115],\n",
       "       [ 46, 146],\n",
       "       [ 46,  41],\n",
       "       [ 46,  37],\n",
       "       [ 46, 103],\n",
       "       [ 47,  80],\n",
       "       [ 48,  72],\n",
       "       [ 44, 112],\n",
       "       [ 23,  47],\n",
       "       [ 32,  34],\n",
       "       [ 33, 122],\n",
       "       [ 45,  13],\n",
       "       [ 45,  58],\n",
       "       [ 45,  54],\n",
       "       [ 45,  86],\n",
       "       [ 45, 104],\n",
       "       [ 45,  68],\n",
       "       [ 45, 112],\n",
       "       [ 45,  29],\n",
       "       [ 29,  27],\n",
       "       [ 18, 143],\n",
       "       [ 27,  18],\n",
       "       [ 26,  32],\n",
       "       [ 25, 144],\n",
       "       [ 24, 132],\n",
       "       [ 25, 109],\n",
       "       [ 23, 148],\n",
       "       [ 25, 107],\n",
       "       [ 26,  76],\n",
       "       [ 27, 136],\n",
       "       [ 28,  26],\n",
       "       [ 29,  71],\n",
       "       [ 20, 112],\n",
       "       [ 29,  73],\n",
       "       [ 28, 108],\n",
       "       [ 27, 143],\n",
       "       [ 26,  38],\n",
       "       [ 25,  93],\n",
       "       [ 24,   9],\n",
       "       [ 21,  80],\n",
       "       [ 23, 144],\n",
       "       [ 24, 115],\n",
       "       [ 25,  66],\n",
       "       [ 26, 142],\n",
       "       [ 28,  82],\n",
       "       [ 28, 144]], dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prasa\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss1=[]\n",
    "\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i, init='k-means++',  random_state=21)\n",
    "    kmeans.fit(X1)\n",
    "    wcss1.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,11),wcss1)\n",
    "plt.title('elbow method')\n",
    "plt.xlabel('No of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans=KMeans(n_clusters=4, init='k-means++',  random_state=42)\n",
    "y1=kmeans.fit_predict(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2, 2, 1, 2, 1, 3, 1, 0, 3, 0, 2, 0, 2, 2, 3, 3, 0, 2, 2, 2,\n",
       "       0, 1, 0, 2, 2, 3, 3, 0, 0, 1, 0, 0, 3, 3, 3, 0, 1, 3, 2, 2, 3, 0,\n",
       "       0, 0, 0, 3, 0, 0, 0, 2, 2, 0, 0, 1, 0, 2, 2, 1, 1, 3, 2, 2, 2, 3,\n",
       "       3, 1, 0, 0, 0, 1, 0, 1, 3, 2, 3, 1, 2, 3, 1, 2, 0, 3, 1, 2, 2, 0,\n",
       "       0, 3, 2, 1, 2, 3, 0, 1, 2, 1, 1, 3, 2, 3, 0, 0, 3, 1, 0, 3, 0, 1,\n",
       "       2, 1, 3, 0, 2, 0, 2, 1, 1, 1, 1, 2, 0, 0, 3, 1, 3, 2, 0, 1, 0, 0,\n",
       "       2, 0, 0, 1, 2, 0, 1, 2, 2, 1, 0, 0, 1, 2, 1, 0, 0, 3, 1, 2, 1, 2,\n",
       "       0, 0, 1, 3, 2, 2, 1, 0, 0, 1, 0, 2, 1, 2, 0, 0, 0, 1, 0, 1, 2, 2,\n",
       "       3, 2, 2, 3, 3, 1, 3, 1, 0, 3, 2, 0, 1, 0, 1, 3, 2, 1, 2, 0, 3, 1,\n",
       "       0, 3, 0, 3])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X1[y1==0,0],X1[y1==0,1], s=100, c='red', label='Cluster1')\n",
    "plt.scatter(X1[y1==1,0],X1[y1==1,1], s=100, c='blue', label='Cluster2')\n",
    "plt.scatter(X1[y1==2,0],X1[y1==2,1], s=100, c='yellow', label='Cluster3')\n",
    "plt.scatter(X1[y1==3,0],X1[y1==3,1], s=100, c='green', label='Cluster4')\n",
    "#plt.scatter(kmeans.cluster_centers_[:0], kmeans.cluster_centers_[:1], s=100, c='black', label='Centroids')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=100, c='black', label='Centroids')\n",
    "\n",
    "plt.title('Cluster of amazon users')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Purchase rating')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age = (20-30)\n",
    "\n",
    "1)Around 20 % of the people from age bracket (20-30) have very high tendency to buy\n",
    "\n",
    "2) Around 25% have high tendency to buy \n",
    "\n",
    "3) Around 30% have medium tendency to buy\n",
    "\n",
    "4) Around 25% have low tendency to buy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the customes aged 40-50 have a medium to low tendency to buy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMojhScvfhNEf0PB0SFUBgo",
   "name": "Data Preprocessing Template.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}