{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " # Hands On KMeans Clustering - II" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CustomerIDGenreAgeAnnual Income (k$)Spending Score (1-100)
01Male191539
12Male211581
23Female20166
34Female231677
45Female311740
\n", "
" ], "text/plain": [ " CustomerID Genre Age Annual Income (k$) 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" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('Mall_Customers.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### What data says:\n", "There is big mall in specific city that contains information of it's clients.The clients that subscribe to membership card.When the client subscribe to the card they provide information such as their Gender,Age,anda Annual income.Since they have this card they use it buy all sorts of in the mall and therefore the mall has the purchase history each of it's client member and that's how they obtained last column i.e Spending Score.The spending score is decided on number of criteria such as member income, number of times per week they showed up in mall,and ofcourse the amount of dollars they spent in a year.Based on these parameters spending score ranges (1-100).The closer the score to 1, less spend made by client vise versa.\n", "\n", "Now mall company wants to segregate their clients based on two features: Annual Income and Spending Score\n", "\n", "This is a typical clustering problem. Lets help poor guys with newly acquired knowledge of K-means clustering." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(200, 5)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "X = df.iloc[:,[3,4]].values " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating the Clusters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cluster import KMeans" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Using the elbow method to find optimum number of clusters" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "wcss = []" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(1,11):\n", " km = KMeans(n_clusters=i,init = 'k-means++',max_iter=300,n_init=10,random_state=0)\n", " km.fit(X)\n", " wcss.append(km.inertia_)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "As you've noticed, I have set initialization to \"k-means++\".This is to avoid random initialization trap.\n", "#### What is random initialization trap?\n", "Let's say we have scatter plot which looks something like this…\n", "" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "If we choose K=3 clusters… we will hope the random initialization would lead us to...following this 3 clusters\n", " \n", " " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "But what if we had a bad random initialization?\n", "Green line seperating data points and assigning to each clusters.\n", "This is converged and final model.Look how different clusters came out to be when we initialize with diffrent set of points.That is the curse of random initialization and remedy to that is \"[K-means++](https://en.wikipedia.org/wiki/K-means%2B%2B)\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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sOXje0eEIIcohq4Vj0aJF9ohD2NEjdb0JD/Jm0e4zpGXlOTocIUQ5Y7VwbN++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\n9XmiUXVHhyZEmWS3wpGbmws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ERETc8ZiicpKL46JC69ChA4GBgebrAk5OTly5coXCwkLS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\nTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style('darkgrid')\n", "plt.plot(range(1,11),wcss)\n", "plt.title('The Elbow Method')\n", "plt.xlabel('Number of Clusters')\n", "plt.ylabel('Within Cluster Sum Of Squares');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As shown in graph , elbow is formed at number 5 on X-axis. That is the optimal number of clusters for this dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "Alternative to elbow method is.. \n", "\n", "#### Silhouette Analysis\n", "\n", "$$\\text{silhouette score}=\\frac{p-q}{max(p,q)}$$\n", "\n", "Where,\n", "\n", "$p$ is the mean distance to the points in the nearest cluster that the data point is not a part of\n", "\n", "$q$ is the mean intra-cluster distance to all the points in its own cluster.\n", "\n", "* The value of the silhouette score range lies between -1 to 1. \n", "\n", "* A score closer to 1 indicates that the data point is very similar to other data points in the cluster, \n", "\n", "* A score closer to -1 indicates that the data point is not similar to the data points in its cluster." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import silhouette_score" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sse_= []\n", "for k in range(2,8):\n", " km = KMeans(n_clusters=k,init = 'k-means++',max_iter=300,n_init=10,random_state=0).fit(X)\n", " sse_.append([k, silhouette_score(X, km.labels_)])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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UajVGoxGNRsOJEyf49ttv+eijj/j4448tr/H19WXMmDE8/vjj7N27l/Hjx7Nu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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style('darkgrid')\n", "plt.plot(pd.DataFrame(sse_)[0], pd.DataFrame(sse_)[1])\n", "plt.title('Silhouette Analysis')\n", "plt.xlabel('Number of Clusters')\n", "plt.ylabel('Silhouette Scores');" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Applying K-means to mall dataset" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "km = KMeans(n_clusters=5,init = 'k-means++',max_iter=300,n_init=10,random_state=0) # setting default values for max_iter and n_init\n", "y_means = km.fit_predict(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the Clusters" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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Y039F+xsM8NJL8Mwz2mueptpMJnjooUMYDO5OAKaA5DL3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\nMaNIEZHa8jeRBRuXlopEFxVdsa5809FS4GVifz9HEXGGlpa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Dy8vLc2Joljpx4kRMfA6n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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plotting scatter plot of clusters along with their highlighted clusters.\n", "sns.set_style('whitegrid')\n", "plt.figure(figsize=(10,6))\n", "plt.scatter(X[y_means==0,0],X[y_means==0,1],s=50,c='red',label='Cluster 1',marker='*') #X[y_means==0,0] for x-coordinates for cluter1,X[y_means==0,1] for y-coordinates ,s for size of datapoint\n", "plt.scatter(X[y_means==1,0],X[y_means==1,1],s=50,c='blue',label='Cluster 2',marker='*')\n", "plt.scatter(X[y_means==2,0],X[y_means==2,1],s=50,c='green',label='Cluster 3',marker='*')\n", "plt.scatter(X[y_means==3,0],X[y_means==3,1],s=50,c='brown',label='Cluster 4',marker='*')\n", "plt.scatter(X[y_means==4,0],X[y_means==4,1],s=50,c='magenta',label='Cluster 5',marker='*')\n", "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],s=250,c='yellow',label='Centroids') #Centroids are highlighted with bigger size\n", "\n", "plt.title('Clusters of Clients')\n", "plt.xlabel('Annual Income (k$)')\n", "plt.ylabel('Spending Score (1-100)')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Cluster 1 - In this cluster, clients earn High income but don't spend much money.We could call these clients as \"__Careful__\"\n", "- Cluster 2 - In this cluster, clients earn average income and have average spending score.Let's call them \"__Standard__\"\n", "- Cluster 3 - In this cluster, clients earn High income and have high spending score.This is main potential customer base for mall's marketing campaigns and it would be very insightful for the mall to understand what kind of product are brought by these set of clients.So we would call this cluster as \" __Target__\"\n", "- Cluster 4 - In this cluster, clients earn low income but high spending score.Let's call these clients \"__Careless__\"\n", "- Cluster 5 - In this cluster, clients earn low income and have low spending score.Lets' name them \"__Sensible__\"\n", "\n", "#### We will make these changes in our code..." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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SusdUiaXWW/6FK/mo8LNXjjCl7s7++w+MvFHwPa6qXrSW0px7NMnjVZ2EUHpb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\niEgFTLqIiIiIVPD/AXfyH0mqCFaoAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style('darkgrid')\n", "plt.figure(figsize=(10,6))\n", "plt.scatter(X[y_means==0,0],X[y_means==0,1],s=50,c='red',label='Careful')\n", "plt.scatter(X[y_means==1,0],X[y_means==1,1],s=50,c='blue',label='Standard')\n", "plt.scatter(X[y_means==2,0],X[y_means==2,1],s=50,c='green',label='Target')\n", "plt.scatter(X[y_means==3,0],X[y_means==3,1],s=50,c='brown',label='Careless')\n", "plt.scatter(X[y_means==4,0],X[y_means==4,1],s=50,c='magenta',label='Sensible')\n", "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],s=250,c='yellow',label='Centroids')\n", "\n", "plt.title('Clusters of Clients')\n", "plt.xlabel('Annual Income (k$)')\n", "plt.ylabel('Spending Score (1-100)')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Note__ : This code is for two dimentional clustering meaning data with just two variables! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*** \n", "\n", "[MLXTEND](http://rasbt.github.io/mlxtend/)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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1sAyjH+DdQTn4ukoxUnA8SKuYQJEm2UUn3rt3IlrU6vFE2tG5TXPMGF/TrPaR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\niEgbmECFGZPJjOk3PYg/TZ8pPWJjLLC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T4XTizeWbkZwQCwC4dnB39O/aTuWo9KWozIqC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\n9emnnyo1NVVlZWUaPny4lS8BAADgOEtXoPbt26f29nbt3r1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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# library supported by Sebastian Raschka for visualizing clusters and many other fuctions\n", "# linear boudries Visualization \n", "from mlxtend.plotting import plot_decision_regions\n", "plt.figure(figsize=(10,6))\n", "plot_decision_regions(X,y_means,clf=km);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }