Customer_Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Collection & Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the data from csv file to a Pandas DataFrame\n",
    "customer_data = pd.read_csv('Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first 5 rows in the DataFrame\n",
    "customer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the number of rows and columns\n",
    "customer_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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$)      200 non-null    int64 \n",
      " 4   Spending Score (1-100)  200 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#getting some information about the dataset\n",
    "customer_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Gender                    0\n",
       "Age                       0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for missing values\n",
    "customer_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing the Annual Income (k$) column and Spending Score (1-500) column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "X = customer_data.iloc[:,[3,4]].values\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choosing the number of squares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method : Within Cluster Sum of Squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#finding WCSS value for different number of clusters\n",
    "\n",
    "\n",
    "wcss=[]\n",
    "\n",
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters=i,init='k-means++',random_state = 42)\n",
    "    kmeans.fit(X)\n",
    "    \n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot as elbow graph\n",
    "\n",
    "sns.set()\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('Elbow Point Graph')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimum number of clusters = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the K-Means Clustering Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3\n",
      " 4 3 4 3 4 3 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 0 1 2 1 2 1 0 1 2 1 2 1 2 1 2 1 0 1 2 1 2 1\n",
      " 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
      " 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1]\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 0)\n",
    "\n",
    "#return a label for each data point based on the cluster it belongs to\n",
    "Y = kmeans.fit_predict(X)\n",
    "print(Y) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising all the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting all the clusters and their respective centroids\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.scatter(X[Y==0,0], X[Y==0,1],s=50, c='green', label='Cluster 1')\n",
    "plt.scatter(X[Y==1,0], X[Y==1,1],s=50, c='red', label='Cluster 1')\n",
    "plt.scatter(X[Y==2,0], X[Y==2,1],s=50, c='blue', label='Cluster 1')\n",
    "plt.scatter(X[Y==3,0], X[Y==3,1],s=50, c='purple', label='Cluster 1')\n",
    "plt.scatter(X[Y==4,0], X[Y==4,1],s=50, c='brown', label='Cluster 1')\n",
    "\n",
    "#plot the centroids\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=100, c='black', label='Centroids')\n",
    "\n",
    "plt.title('Customer Groups')\n",
    "plt.xlabel('Annual Income')\n",
    "plt.ylabel('Spending Score')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}