Customer Segmentation using K means Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77e6814c-b422-4538-8a77-aeb0328b25b4",
   "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",
       "    </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",
       "    <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>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\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",
       "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",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "data=pd.read_csv(\"Mall_Customers.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f7f1efd-1954-4aa4-9b76-4505079a4e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "purchase = data[['Spending Score (1-100)']]\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "inertia = []\n",
    "\n",
    "for k in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(purchase)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(range(1, 11), inertia, marker='o', linestyle='--')\n",
    "plt.title('Elbow Curve')\n",
    "plt.xlabel('Number of Clusters (K)')\n",
    "plt.ylabel('Inertia (Within-Cluster Sum of Squares)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b98c03a-66cc-4694-ae28-e1d64d7c6ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 2\n",
    "\n",
    "kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "data['Cluster'] = kmeans.fit_predict(purchase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4f80741-ee04-464b-9d2b-3ebc8467048c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7524\\1862964829.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cluster_data['Cluster'] = kmeans.fit_predict(cluster_data_scaled)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_7524\\1862964829.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cluster_data['Cluster Name'] = cluster_data['Cluster']\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "cluster_data = data[['Spending Score (1-100)', 'Annual Income (k$)']]\n",
    "\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "cluster_data_scaled = scaler.fit_transform(cluster_data)\n",
    "\n",
    "kmeans = KMeans(n_clusters=2, random_state=42)\n",
    "cluster_data['Cluster'] = kmeans.fit_predict(cluster_data_scaled)\n",
    "\n",
    "cluster_data['Cluster Name'] = cluster_data['Cluster']\n",
    "    \n",
    "plt.figure(figsize=(10, 6))\n",
    "colors = [ 'pink', 'blue']\n",
    "for i, cluster in enumerate(cluster_data['Cluster'].unique()):\n",
    "    subset = cluster_data[cluster_data['Cluster'] == cluster]\n",
    "    plt.scatter(subset['Spending Score (1-100)'], subset['Annual Income (k$)'], label=f'Cluster {cluster}: {subset[\"Cluster Name\"].iloc[0]}', color=colors[i])\n",
    "\n",
    "plt.xlabel('Purchase History')\n",
    "plt.ylabel('Annual Income (k$)')\n",
    "plt.legend()\n",
    "plt.title('Purchase History vs. Annual Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bc0a69c-ae90-457f-b03f-b410993f1c6f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}