K-Means Clustering (Mall_Customers).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d5cb0772",
   "metadata": {},
   "source": [
    "Clustering is example of unsupervised learnining in which We’re not trying to predict an outcome here. The goal is to look for structures in the data. In other words, we’re “dividing” the dataset into groups wherein members have some similarities or proximities. For example, each ecommerce customer might belong to a particular group (e.g. given their income and spending level). If we have gathered enough data points, it’s likely there are aggregates.\n",
    "At first the data points will seem scattered (no pattern at all). But once we apply a Clustering algorithm, the data will somehow make sense because we’ll be able to easily visualize the groups or clusters. Aside from discovering the natural groupings, Clustering algorithms may also reveal outliers for Anomaly Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "983244a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "343b2c0c",
   "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>Genre</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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  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",
       "5           6  Female   22                  17                      76\n",
       "6           7  Female   35                  18                       6\n",
       "7           8  Female   23                  18                      94\n",
       "8           9    Male   64                  19                       3\n",
       "9          10  Female   30                  19                      72"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('Mall_Customers.csv') \n",
    "dataset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7670ebb1",
   "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   Genre                   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.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b1c4d9",
   "metadata": {},
   "source": [
    "In this example we’re more interested in grouping the Customers according to their Annual Income and Spending Score.\n",
    "1. MediumAnnualIncome,MediumSpendingScore \n",
    "2. HighAnnualIncome,LowSpendingScore\n",
    "3. LowAnnualIncome,LowSpendingScore\n",
    "4. LowAnnualIncome,HighSpendingScore\n",
    "5. HighAnnualIncome,HighSpendingScore\n",
    "* Group 2 (High Annual Income, Low Spending Score). If there’s a sizable number of customers that fall under this group, it could mean a huge opportunity for the mall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f7b8f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, [3, 4]].values # selecting the column of income and spending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef967dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42) \n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_) \n",
    "plt.plot(range(1, 11), wcss) \n",
    "plt.title('The Elbow Method') \n",
    "plt.xlabel('Number of clusters') \n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c74fa21",
   "metadata": {},
   "source": [
    "Notice that the “elbow” points at 5 (number of clusters).\n",
    "After determining the optimal number of clusters, we can then proceed with applying K-Means to the dataset and then performing data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29cb5d16",
   "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": [
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = kmeans.fit_predict(X) \n",
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1') \n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of customers') \n",
    "plt.xlabel('Annual Income (k$)') \n",
    "plt.ylabel('Spending Score (1-100)') \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37cba671",
   "metadata": {},
   "source": [
    "There we have it. We have 5 clusters and Cluster #2 (blue points, High Annual Income and Low Spending Score) is significant enough. It might be worthwhile for the marketing department to focus on that group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa7a9e1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}