13-Customer-Segmentation/Customer_Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bfa58b59",
   "metadata": {},
   "source": [
    "# Customer Segmentaion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f12960",
   "metadata": {},
   "source": [
    "### Importing all dependecies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c66dc1f",
   "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",
   "id": "2d072d6c",
   "metadata": {},
   "source": [
    "### Data Collectin and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40ba62f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading data\n",
    "customer_data = pd.read_csv(\"data/Mall_Customers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6f0a80d",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show dataset\n",
    "customer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b435fd21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "customer_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee4ac23e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Gender                    0\n",
       "Age                       0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values\n",
    "customer_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d8d6394",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   CustomerID              200 non-null    int64 \n",
      " 1   Gender                  200 non-null    object\n",
      " 2   Age                     200 non-null    int64 \n",
      " 3   Annual Income (k$)      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": [
    "# dataset information\n",
    "customer_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "402a7a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# features\n",
    "X = customer_data.iloc[:, [3, 4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4f5733c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c8549bf",
   "metadata": {},
   "source": [
    "### WCSS value for different clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e20ab142",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TanveerKader\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: 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"
     ]
    }
   ],
   "source": [
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    km = KMeans(n_clusters = i, init = \"k-means++\", random_state = 42)\n",
    "    km.fit(X)\n",
    "    \n",
    "    wcss.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53330cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot 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",
   "id": "4303785f",
   "metadata": {},
   "source": [
    "### Model Training with KMeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63eb53d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[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 4\n",
      " 3 4 3 4 3 4 1 4 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 2 0 2 1 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 1 2 0 2 0 2\n",
      " 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0\n",
      " 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2]\n"
     ]
    }
   ],
   "source": [
    "# initiate model\n",
    "kmeans = KMeans(n_clusters = 5, init = \"k-means++\", random_state = 0)\n",
    "\n",
    "# assign label for each cluster\n",
    "Y = kmeans.fit_predict(X)\n",
    "\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be782a97",
   "metadata": {},
   "source": [
    "### Visualising the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc4b349b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot clusters\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 = \"cyan\", label = \"Cluster 2\" )\n",
    "plt.scatter(X[Y == 2, 0], X[Y == 2, 1], s = 50, c = \"yellow\", label = \"Cluster 3\" )\n",
    "plt.scatter(X[Y == 3, 0], X[Y == 3, 1], s = 50, c = \"blue\", label = \"Cluster 4\" )\n",
    "plt.scatter(X[Y == 4, 0], X[Y == 4, 1], s = 50, c = \"violet\", label = \"Cluster 5\" )\n",
    "\n",
    "# plot centroids\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 100, c = \"red\", label = \"Centroids\")\n",
    "\n",
    "plt.title(\"Customer Groups\")\n",
    "plt.xlabel(\"Annual Income\")\n",
    "plt.ylabel(\"Spending Score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08513c5c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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