CustomerSegmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a1b0064-4f30-4af2-bbc1-1a4b8934f243",
   "metadata": {},
   "source": [
    "# Customer Segmentation: KMeans Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda4fa42-306c-436c-901a-67045827c9f5",
   "metadata": {},
   "source": [
    "In this project, we will be trying to develop an end-to-end data science application. The aim of the project is to perform customer segmentation using Mall customers data with the following attributes:\n",
    "\n",
    "* CustomerID - Unique ID assigned to customer.\n",
    "* Gender - Gender of customer.\n",
    "* Age - Age of Customer.\n",
    "* Annual Income (k$)  - Annual income of cutomer.\n",
    "* Spending Score (1-100) - Score Assigned to cutomer based on bahavior and spending nature.\n",
    "\n",
    "We will be doing segmentation using **Spending Score** and **Annual Income**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b59ddbb1-f8a5-4463-a089-1c4de29ebb2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"OMP_NUM_THREADS\"]='1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "16835a03-7e4b-4972-8411-8f2eeb2d7680",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "3bf4d186-5ede-4c73-ae34-e7eedcc09a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'MallCustomers.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "a2f1b1e6-4947-4662-b6c0-db38c1e784de",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d4ba3497-1b07-4c9d-a501-c3a817f23efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "e070a191-5902-4398-ab68-8f34875003c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   CustomerID              200 non-null    int64 \n",
      " 1   Gender                  200 non-null    object\n",
      " 2   Age                     200 non-null    int64 \n",
      " 3   Annual Income (k$)      200 non-null    int64 \n",
      " 4   Spending Score (1-100)  200 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "aa7c323e-7057-4e93-b83f-54cd14e54b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
       "count  200.000000  200.000000          200.000000              200.000000\n",
       "mean   100.500000   38.850000           60.560000               50.200000\n",
       "std     57.879185   13.969007           26.264721               25.823522\n",
       "min      1.000000   18.000000           15.000000                1.000000\n",
       "25%     50.750000   28.750000           41.500000               34.750000\n",
       "50%    100.500000   36.000000           61.500000               50.000000\n",
       "75%    150.250000   49.000000           78.000000               73.000000\n",
       "max    200.000000   70.000000          137.000000               99.000000"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6e2b5eff-3cb6-49b0-ae25-97438bfb87e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def spending_group(x):\n",
    "    if  x <=33:\n",
    "        return 'low'\n",
    "    elif x > 33 and x <= 66:\n",
    "        return 'moderate'\n",
    "    else:\n",
    "        return 'high'\n",
    "    \n",
    "def income_group(x):\n",
    "    if  x <=52:\n",
    "        return 'low income'\n",
    "    elif x > 52 and x <= 156:\n",
    "        return 'middle income'\n",
    "    else:\n",
    "        return 'upper income'\n",
    "    \n",
    "def age_group(x):\n",
    "    if x >= 18 and x <=35:\n",
    "        return 'young adult'\n",
    "    elif x >= 36 and x <= 55:\n",
    "        return 'middle aged'\n",
    "    else:\n",
    "        return 'old adult'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "aff4c6a3-f222-4453-b012-412a83c5784e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age Group'] = df.Age.apply(age_group)\n",
    "df['Income Group']=df['Annual Income (k$)'].apply(income_group)\n",
    "df['Spending Group']=df['Spending Score (1-100)'].apply(spending_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "273e6dba-f328-4617-bd8f-1ba7d9372dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>Spending Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>young adult</td>\n",
       "      <td>middle income</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "      <td>middle aged</td>\n",
       "      <td>middle income</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "      <td>young adult</td>\n",
       "      <td>middle income</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "      <td>young adult</td>\n",
       "      <td>middle income</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "      <td>young adult</td>\n",
       "      <td>middle income</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "195         196  Female   35                 120                      79   \n",
       "196         197  Female   45                 126                      28   \n",
       "197         198    Male   32                 126                      74   \n",
       "198         199    Male   32                 137                      18   \n",
       "199         200    Male   30                 137                      83   \n",
       "\n",
       "       Age Group   Income Group Spending Group  \n",
       "195  young adult  middle income           high  \n",
       "196  middle aged  middle income            low  \n",
       "197  young adult  middle income           high  \n",
       "198  young adult  middle income            low  \n",
       "199  young adult  middle income           high  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b95144-0b3d-4b93-a8af-b98d889fe0c0",
   "metadata": {},
   "source": [
    "## Exploring gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "47a0fcb1-28e4-44f8-a40e-d6119aa7004c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n"
     ]
    }
   ],
   "source": [
    "print(sns.color_palette().as_hex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "9f4d5a9f-ac0d-4191-8332-2183dbf6f0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,axes = plt.subplots(1,2,figsize=(12, 5))\n",
    "sns.countplot(data=df, x='Gender', ax=axes[0],palette=['#ff7f0e','#1f77b4'])\n",
    "axes[1].pie(df.Gender.value_counts(),explode=[0,0.03],labels=['Female','Male'],autopct=\"%.0f%%\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65314be1-9108-4ca2-8efd-23ed68c20468",
   "metadata": {},
   "source": [
    "We see that most customers are females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "5db4146a-5bae-4c00-a290-369ded64b385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x='Gender',y='Spending Score (1-100)',palette=['#ff7f0e','#1f77b4']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "25f7c536-0393-4b3a-a6f3-9bde730af0d6",
   "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>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>51.526786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>48.511364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Spending Score (1-100)\n",
       "Gender                        \n",
       "Female               51.526786\n",
       "Male                 48.511364"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot = df.pivot_table(index='Gender',values=['Spending Score (1-100)'],aggfunc='mean')\n",
    "pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2b76fa-1a95-4659-992b-3f380aad9a5c",
   "metadata": {},
   "source": [
    "We can see that females on average spend more than males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "18c99008-8805-47ae-8a71-a3ed0247c84d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df,x='Age',y='Spending Score (1-100)',hue='Income Group');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fade9a7e-b151-4ba9-95d7-afe006830e3b",
   "metadata": {},
   "source": [
    "## Exploring Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "589858b2-6c37-4503-8355-b7def1cf6ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df,x='Age Group',y='Annual Income (k$)',palette=['#2ca02c','#ff7f0e','#1f77b4']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "83a130f6-d3a2-4617-afa1-5abf4c1a14d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,axes = plt.subplots(1,2,figsize=(12, 5))\n",
    "sns.barplot(data=df,x='Age Group',y='Spending Score (1-100)',ax=axes[0],palette=['#2ca02c','#ff7f0e','#1f77b4']);\n",
    "pivot = df.pivot_table(index='Age Group',values=['Spending Score (1-100)'],aggfunc='mean')\n",
    "pivot.plot(kind='pie',subplots=True,autopct=\"%.0f%%\",ax=axes[1],explode=[0,0,0.07])\n",
    "plt.ylabel('')\n",
    "plt.legend([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15eb850d-40f7-46b3-9b0f-71fc069904c2",
   "metadata": {},
   "source": [
    "Young Adults on averge spend more than any age group."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edda0501-27f9-46aa-8d4b-028875daf58b",
   "metadata": {},
   "source": [
    "## Exploring Spending Score and Annual Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "86601bbe-910c-45b0-9f90-0992fd5781e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df,x='Spending Score (1-100)',y='Annual Income (k$)')\n",
    "plt.title(\"Scatterplot of Annual income and Spending Score\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f9dd81-3023-4c39-92d6-4d86bf32b8dc",
   "metadata": {},
   "source": [
    "We can see that there some grouped data points follow a pattern. The data form 'clusters'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5df473-f472-46dd-af55-1be990d875e1",
   "metadata": {},
   "source": [
    "## Split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b1d6f6-d737-4dda-adf8-0a3c5a589524",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "091e42ed-6505-4995-8742-f0a83619c121",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "b515c055-0e29-4e0c-8031-468775c38049",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Annual Income (k$)','Spending Score (1-100)']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45a7cbd-7c8f-4d5f-be09-b6c95fd33a2d",
   "metadata": {},
   "source": [
    "We will be doing segmentation using **Spending Score** and **Annual Income**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "2ce7856f-44f8-4db2-9d60-6d2d2890c3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_means = ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47270c9c-e5c7-4340-95cf-ce5e6e848329",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c0011fa1-2c7f-4600-8eab-2b4c33f6f7f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,Xtest = train_test_split(X_means , test_size=0.2, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02b12b7-359e-4066-b826-c0394de354e7",
   "metadata": {},
   "source": [
    "### Elbow Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8cc6bf-6379-4469-8b6d-f99c9cd29320",
   "metadata": {},
   "source": [
    "To find the correct number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ba7b6bd6-affa-43aa-a06a-523a8d37ee81",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = []\n",
    "for i in range(1,11):\n",
    "    model = KMeans(n_clusters=i,n_init=i,init='k-means++',random_state=42)\n",
    "    model.fit(X_train)\n",
    "    inertia.append(model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "f918149e-1272-478f-9de0-f30f91f467d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = [i for i in range(1,11)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "a767c1a8-6246-49ed-aa8b-f623b593f3df",
   "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.plot(clusters, inertia, 'x--')\n",
    "plt.title(\"Clusters vs Inertia\")\n",
    "plt.xlabel(\"Clusters\")\n",
    "plt.ylabel(\"Inertia\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "9595639d-9bec-4fc4-8b64-4abc5cab4a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.40344930706913934,\n",
       " 0.45404862424077985,\n",
       " 0.47721891230990243,\n",
       " 0.5509842461121202,\n",
       " 0.5408225711237818,\n",
       " 0.536669787130034,\n",
       " 0.4509937596508946,\n",
       " 0.4566345256771399,\n",
       " 0.4335415087980149]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_scores = []\n",
    "\n",
    "for i in range(2,11):\n",
    "    model = KMeans(n_clusters=i, n_init='auto', init='k-means++', random_state=42)\n",
    "    model.fit(X_train)\n",
    "    score = silhouette_score(X_train, model.labels_)\n",
    "    silhouette_scores.append(score)\n",
    "\n",
    "silhouette_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "02c1dbbe-adb7-445e-861c-b6cdc95e3f07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=[i for i in range(2,11)], y=silhouette_scores)\n",
    "plt.title(\"Clusters vs Silhoutte Score\")\n",
    "plt.xlabel(\"Clusters\")\n",
    "plt.ylabel(\"Silhoutte Score\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb367007-3809-49d9-aa5f-46208a65c1b7",
   "metadata": {},
   "source": [
    "From the elbow plot and silhoutte score we can see that the optimum number of clusters is **5**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66d2a9c-817f-4372-8f65-467d085ff92f",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "75734ad4-31ee-40f8-b602-fc1bfa570c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=5, n_init='auto', init='k-means++', random_state=42)\n",
    "model.fit(X_means)\n",
    "df['Labels'] = model.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "d6d12a2e-7afd-4e22-8cf3-4339b1681bac",
   "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>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>Spending Group</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>young adult</td>\n",
       "      <td>low income</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>young adult</td>\n",
       "      <td>low income</td>\n",
       "      <td>high</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>young adult</td>\n",
       "      <td>low income</td>\n",
       "      <td>low</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>young adult</td>\n",
       "      <td>low income</td>\n",
       "      <td>high</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>young adult</td>\n",
       "      <td>low income</td>\n",
       "      <td>moderate</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "0           1    Male   19                  15                      39   \n",
       "1           2    Male   21                  15                      81   \n",
       "2           3  Female   20                  16                       6   \n",
       "3           4  Female   23                  16                      77   \n",
       "4           5  Female   31                  17                      40   \n",
       "\n",
       "     Age Group Income Group Spending Group  Labels  \n",
       "0  young adult   low income       moderate       2  \n",
       "1  young adult   low income           high       3  \n",
       "2  young adult   low income            low       2  \n",
       "3  young adult   low income           high       3  \n",
       "4  young adult   low income       moderate       2  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "ff1cea89-af4d-46cc-8edc-91800ec38ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df,\n",
    "                x=df['Spending Score (1-100)'], \n",
    "                y=df['Annual Income (k$)'], \n",
    "                hue='Labels',\n",
    "                palette=sns.color_palette()[:5]\n",
    "               )\n",
    "plt.legend(title='Cluster',bbox_to_anchor=(1,0.5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5fde42f-a551-49c8-9c65-4d75ae65a822",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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