K-Means Clustering Lecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the Mall Dataset with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Mall_Customers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, [3,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using the Elbow Method to Find the Optimal Number of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wcss = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters = i,init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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r6rJEZD8UNtKkZWWkcctXBnDvt49j/pptnH/P20xZqKeuiiQbhY00CyOO6cJL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\nF2P62s4JlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a591f42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
   "metadata": {},
   "source": [
    "# Applying K-Means to the Mall Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 4, 3,\n",
       "       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,\n",
       "       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, 2, 0, 2, 1, 2, 0, 2, 0, 2, 1, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 1, 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, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GIvfy37NAvlZti1U/IladCUt+YOIbSacmk5/oLghwTCNHOiuESZNaZ1JPmuSc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\nPRo4GZgi6fBOxDgY+JXtE4DXgG/k8dl5fASp59Jm4Oukb4O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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a5a0c16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1], s=100, c='red', label='Careful')\n",
    "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1], s=100, c='blue', label='Standard')\n",
    "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2,1], s=100, c='green', label='Target')\n",
    "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3,1], s=100, c='cyan', label='Careless')\n",
    "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4,1], s=100, c='magenta', label='Sensible')\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c='yellow', label='Centroids')\n",
    "plt.title('Clusters of Clients')\n",
    "plt.xlabel('Annual Income(k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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