K Means Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Datasets/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>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>...</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   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",
       "..          ...     ...  ...                 ...                     ...\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset   #We can find no label in the dataset, so need to do clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:, [3,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "#Finding the optimal no. of clusters using Elbow method\n",
    "\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",
    "\n",
    "plt.plot(range(1,11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=5, init='k-means++', random_state=42)\n",
    "y_kmeans= kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0,\n",
       "       3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 1,\n",
       "       3, 0, 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,\n",
       "       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, 2, 4, 2, 1, 2, 4, 2, 4, 2,\n",
       "       1, 2, 4, 2, 4, 2, 4, 2, 4, 2, 1, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2,\n",
       "       4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2,\n",
       "       4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2,\n",
       "       4, 2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 39], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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.scatter(x[y_kmeans==0, 0], x[y_kmeans==0, 1], s=100, c='red', label='Cluster1')\n",
    "plt.scatter(x[y_kmeans==1, 0], x[y_kmeans==1, 1], s=100, c='blue', label='Cluster2')\n",
    "plt.scatter(x[y_kmeans==2, 0], x[y_kmeans==2, 1], s=100, c='green', label='Cluster3')\n",
    "plt.scatter(x[y_kmeans==3, 0], x[y_kmeans==3, 1], s=100, c='magenta', label='Cluster4')\n",
    "plt.scatter(x[y_kmeans==4, 0], x[y_kmeans==4, 1], s=100, c='cyan', label='Cluster5')\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=100, c='black', label='centroid')\n",
    "plt.title('Cluster for Customers')\n",
    "plt.xlabel('Annual income')\n",
    "plt.ylabel('Spending Scores')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x5f4f54f490>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(x[:,0],x[:,1])"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}