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Created on Skills Network Labs
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"cells": [
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"source": [
"<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
"\n",
"<h1><center>K-Means Clustering</center></h1>"
]
},
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"source": [
"## Introduction\n",
"\n",
"There are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered one of the simplest models amongst them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you will learn how to use k-Means for customer segmentation.\n",
"\n",
"Some real-world applications of k-means:\n",
"- Customer segmentation\n",
"- Understand what the visitors of a website are trying to accomplish\n",
"- Pattern recognition\n",
"- Machine learning\n",
"- Data compression\n",
"\n",
"\n",
"In this notebook we practice k-means clustering with 2 examples:\n",
"- k-means on a random generated dataset\n",
"- Using k-means for customer segmentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Table of contents</h1>\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
" <ul>\n",
" <li><a href=\"#random_generated_dataset\">k-Means on a randomly generated dataset</a></li>\n",
" <ol>\n",
" <li><a href=\"#setting_up_K_means\">Setting up K-Means</a></li>\n",
" <li><a href=\"#creating_visual_plot\">Creating the Visual Plot</a></li>\n",
" </ol>\n",
" <li><a href=\"#customer_segmentation_K_means\">Customer Segmentation with K-Means</a></li>\n",
" <ol>\n",
" <li><a href=\"#pre_processing\">Pre-processing</a></li>\n",
" <li><a href=\"#modeling\">Modeling</a></li>\n",
" <li><a href=\"#insights\">Insights</a></li>\n",
" </ol>\n",
" </ul>\n",
"</div>\n",
"<br>\n",
"<hr>"
]
},
{
"cell_type": "markdown",
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"source": [
"### Import libraries\n",
"Lets first import the required libraries.\n",
"Also run <b> %matplotlib inline </b> since we will be plotting in this section."
]
},
{
"cell_type": "code",
"execution_count": 1,
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"source": [
"import random \n",
"import numpy as np \n",
"import matplotlib.pyplot as plt \n",
"from sklearn.cluster import KMeans \n",
"from sklearn.datasets.samples_generator import make_blobs \n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"<h1 id=\"random_generated_dataset\">k-Means on a randomly generated dataset</h1>\n",
"Lets create our own dataset for this lab!\n"
]
},
{
"cell_type": "markdown",
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"source": [
"First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
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"outputs": [],
"source": [
"np.random.seed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n",
"<b> <u> Input </u> </b>\n",
"<ul>\n",
" <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
" <ul> <li> Value will be: 5000 </li> </ul>\n",
" <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
" <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n",
" <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n",
" <ul> <li> Value will be: 0.9 </li> </ul>\n",
"</ul>\n",
"<br>\n",
"<b> <u> Output </u> </b>\n",
"<ul>\n",
" <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n",
" <ul> <li> The generated samples. </li> </ul> \n",
" <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n",
" <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n",
"</ul>\n"
]
},
{
"cell_type": "code",
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"source": [
"X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
]
},
{
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"source": [
"Display the scatter plot of the randomly generated data."
]
},
{
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"execution_count": 4,
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"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f9cd228bd68>"
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"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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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], marker='.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"setting_up_K_means\">Setting up K-Means</h2>\n",
"Now that we have our random data, let's set up our K-Means Clustering."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The KMeans class has many parameters that can be used, but we will be using these three:\n",
"<ul>\n",
" <li> <b>init</b>: Initialization method of the centroids. </li>\n",
" <ul>\n",
" <li> Value will be: \"k-means++\" </li>\n",
" <li> k-means++: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.</li>\n",
" </ul>\n",
" <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
" <ul> <li> Value will be: 4 (since we have 4 centers)</li> </ul>\n",
" <li> <b>n_init</b>: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. </li>\n",
" <ul> <li> Value will be: 12 </li> </ul>\n",
"</ul>\n",
"\n",
"Initialize KMeans with these parameters, where the output parameter is called <b>k_means</b>."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"k_means = KMeans(init = \"k-means++\", n_clusters = 4, n_init = 12)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now let's fit the KMeans model with the feature matrix we created above, <b> X </b>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_means.fit(X)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now let's grab the labels for each point in the model using KMeans' <b> .labels\\_ </b> attribute and save it as <b> k_means_labels </b> "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 3, 3, ..., 1, 0, 0], dtype=int32)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_means_labels = k_means.labels_\n",
"k_means_labels"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also get the coordinates of the cluster centers using KMeans' <b> .cluster&#95;centers&#95; </b> and save it as <b> k_means_cluster_centers </b>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-2.03743147, -0.99782524],\n",
" [ 3.97334234, 3.98758687],\n",
" [ 0.96900523, 0.98370298],\n",
" [ 1.99741008, -3.01666822]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_means_cluster_centers = k_means.cluster_centers_\n",
"k_means_cluster_centers"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"creating_visual_plot\">Creating the Visual Plot</h2>\n",
"So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Please read through the code and comments to understand how to plot the model."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Initialize the plot with the specified dimensions.\n",
"fig = plt.figure(figsize=(6, 4))\n",
"\n",
"# Colors uses a color map, which will produce an array of colors based on\n",
"# the number of labels there are. We use set(k_means_labels) to get the\n",
"# unique labels.\n",
"colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
"\n",
"# Create a plot\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"\n",
"# For loop that plots the data points and centroids.\n",
"# k will range from 0-3, which will match the possible clusters that each\n",
"# data point is in.\n",
"for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
"\n",
" # Create a list of all data points, where the data poitns that are \n",
" # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
" # labeled as false.\n",
" my_members = (k_means_labels == k)\n",
" \n",
" # Define the centroid, or cluster center.\n",
" cluster_center = k_means_cluster_centers[k]\n",
" \n",
" # Plots the datapoints with color col.\n",
" ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
" \n",
" # Plots the centroids with specified color, but with a darker outline\n",
" ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6)\n",
"\n",
"# Title of the plot\n",
"ax.set_title('KMeans')\n",
"\n",
"# Remove x-axis ticks\n",
"ax.set_xticks(())\n",
"\n",
"# Remove y-axis ticks\n",
"ax.set_yticks(())\n",
"\n",
"# Show the plot\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Practice\n",
"Try to cluster the above dataset into 3 clusters. \n",
"Notice: do not generate data again, use the same dataset as above."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 2 2 ... 1 0 0]\n",
"[[-0.80700142 -0.17035769]\n",
" [ 3.70310165 3.69627302]\n",
" [ 2.02030226 -2.94614399]]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# write your code here\n",
"k_means = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
"k_means.fit(X)\n",
"labels = k_means.labels_\n",
"cluster_center = k_means.cluster_centers_\n",
"print(labels)\n",
"print(cluster_center)\n",
"# Initialize the plot with the specified dimensions.\n",
"fig = plt.figure(figsize=(6, 4))\n",
"\n",
"# Colors uses a color map, which will produce an array of colors based on\n",
"# the number of labels there are. We use set(k_means_labels) to get the\n",
"# unique labels.\n",
"colors = plt.cm.Spectral(np.linspace(0, 1, len(set(labels))))\n",
"\n",
"# Create a plot\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"\n",
"# For loop that plots the data points and centroids.\n",
"# k will range from 0-3, which will match the possible clusters that each\n",
"# data point is in.\n",
"for k, col in zip(range(len([[4,4], [-2, -1], [2, -3]])), colors):\n",
"\n",
" # Create a list of all data points, where the data poitns that are \n",
" # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
" # labeled as false.\n",
" my_members = (labels == k)\n",
" \n",
" # Define the centroid, or cluster center.\n",
" cluster_center_k = cluster_center[k]\n",
" \n",
" # Plots the datapoints with color col.\n",
" ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
" \n",
" # Plots the centroids with specified color, but with a darker outline\n",
" ax.plot(cluster_center_k[0], cluster_center_k[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6)\n",
"\n",
"# Title of the plot\n",
"ax.set_title('KMeans')\n",
"\n",
"# Remove x-axis ticks\n",
"ax.set_xticks(())\n",
"\n",
"# Remove y-axis ticks\n",
"ax.set_yticks(())\n",
"\n",
"# Show the plot\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Your answer is below:\n",
"\n",
"k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
"k_means3.fit(X)\n",
"fig = plt.figure(figsize=(6, 4))\n",
"colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
" my_members = (k_means3.labels_ == k)\n",
" cluster_center = k_means3.cluster_centers_[k]\n",
" ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
" ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6)\n",
"plt.show()\n",
"\n",
"\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h1 id=\"customer_segmentation_K_means\">Customer Segmentation with K-Means</h1>\n",
"Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data.\n",
"Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.\n",
"\n",
"Lets download the dataset. To download the data, we will use **`!wget`** to download it from IBM Object Storage. \n",
"__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-05-19 06:44:58-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv\n",
"Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
"Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 34276 (33K) [text/csv]\n",
"Saving to: ‘Cust_Segmentation.csv’\n",
"\n",
"Cust_Segmentation.c 100%[===================>] 33.47K --.-KB/s in 0.02s \n",
"\n",
"2020-05-19 06:44:59 (1.52 MB/s) - ‘Cust_Segmentation.csv’ saved [34276/34276]\n",
"\n"
]
}
],
"source": [
"!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Load Data From CSV File \n",
"Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" 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>Customer Id</th>\n",
" <th>Age</th>\n",
" <th>Edu</th>\n",
" <th>Years Employed</th>\n",
" <th>Income</th>\n",
" <th>Card Debt</th>\n",
" <th>Other Debt</th>\n",
" <th>Defaulted</th>\n",
" <th>Address</th>\n",
" <th>DebtIncomeRatio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>41</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>0.124</td>\n",
" <td>1.073</td>\n",
" <td>0.0</td>\n",
" <td>NBA001</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>26</td>\n",
" <td>100</td>\n",
" <td>4.582</td>\n",
" <td>8.218</td>\n",
" <td>0.0</td>\n",
" <td>NBA021</td>\n",
" <td>12.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>33</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>57</td>\n",
" <td>6.111</td>\n",
" <td>5.802</td>\n",
" <td>1.0</td>\n",
" <td>NBA013</td>\n",
" <td>20.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>19</td>\n",
" <td>0.681</td>\n",
" <td>0.516</td>\n",
" <td>0.0</td>\n",
" <td>NBA009</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>253</td>\n",
" <td>9.308</td>\n",
" <td>8.908</td>\n",
" <td>0.0</td>\n",
" <td>NBA008</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Customer Id Age Edu Years Employed Income Card Debt Other Debt \\\n",
"0 1 41 2 6 19 0.124 1.073 \n",
"1 2 47 1 26 100 4.582 8.218 \n",
"2 3 33 2 10 57 6.111 5.802 \n",
"3 4 29 2 4 19 0.681 0.516 \n",
"4 5 47 1 31 253 9.308 8.908 \n",
"\n",
" Defaulted Address DebtIncomeRatio \n",
"0 0.0 NBA001 6.3 \n",
"1 0.0 NBA021 12.8 \n",
"2 1.0 NBA013 20.9 \n",
"3 0.0 NBA009 6.3 \n",
"4 0.0 NBA008 7.2 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"cust_df = pd.read_csv(\"Cust_Segmentation.csv\")\n",
"cust_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2 id=\"pre_processing\">Pre-processing</h2"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"As you can see, __Address__ in this dataset is a categorical variable. k-means algorithm isn't directly applicable to categorical variables because Euclidean distance function isn't really meaningful for discrete variables. So, lets drop this feature and run clustering."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[41. , 2. , 6. , ..., 1.073, 0. , 6.3 ],\n",
" [47. , 1. , 26. , ..., 8.218, 0. , 12.8 ],\n",
" [33. , 2. , 10. , ..., 5.802, 1. , 20.9 ],\n",
" ...,\n",
" [25. , 4. , 0. , ..., 3.21 , 1. , 33.4 ],\n",
" [32. , 1. , 12. , ..., 0.696, 0. , 2.9 ],\n",
" [52. , 1. , 16. , ..., 3.638, 0. , 8.6 ]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = cust_df.drop('Address', axis=1)\n",
"df.values[:,1:]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Normalizing over the standard deviation\n",
"Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that helps mathematical-based algorithms to interpret features with different magnitudes and distributions equally. We use __StandardScaler()__ to normalize our dataset."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
"outputs_hidden": false
},
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.74291541, 0.31212243, -0.37878978, ..., -0.59048916,\n",
" -0.52379654, -0.57652509],\n",
" [ 1.48949049, -0.76634938, 2.5737211 , ..., 1.51296181,\n",
" -0.52379654, 0.39138677],\n",
" [-0.25251804, 0.31212243, 0.2117124 , ..., 0.80170393,\n",
" 1.90913822, 1.59755385],\n",
" ...,\n",
" [-1.24795149, 2.46906604, -1.26454304, ..., 0.03863257,\n",
" 1.90913822, 3.45892281],\n",
" [-0.37694723, -0.76634938, 0.50696349, ..., -0.70147601,\n",
" -0.52379654, -1.08281745],\n",
" [ 2.1116364 , -0.76634938, 1.09746566, ..., 0.16463355,\n",
" -0.52379654, -0.2340332 ]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"X = df.values[:,1:]\n",
"X = np.nan_to_num(X)\n",
"Clus_dataSet = StandardScaler().fit_transform(X)\n",
"Clus_dataSet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2 id=\"modeling\">Modeling</h2>"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"In our example (if we didn't have access to the k-means algorithm), it would be the same as guessing that each customer group would have certain age, income, education, etc, with multiple tests and experiments. However, using the K-means clustering we can do all this process much easier.\n",
"\n",
"Lets apply k-means on our dataset, and take look at cluster labels."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
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" 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2]\n"
]
}
],
"source": [
"clusterNum = 3\n",
"k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n",
"k_means.fit(X)\n",
"labels = k_means.labels_\n",
"print(labels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"insights\">Insights</h2>\n",
"We assign the labels to each row in dataframe."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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},
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"data": {
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" <th>Age</th>\n",
" <th>Edu</th>\n",
" <th>Years Employed</th>\n",
" <th>Income</th>\n",
" <th>Card Debt</th>\n",
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" <tr>\n",
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" <td>0.0</td>\n",
" <td>6.3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>253</td>\n",
" <td>9.308</td>\n",
" <td>8.908</td>\n",
" <td>0.0</td>\n",
" <td>7.2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Customer Id Age Edu Years Employed Income Card Debt Other Debt \\\n",
"0 1 41 2 6 19 0.124 1.073 \n",
"1 2 47 1 26 100 4.582 8.218 \n",
"2 3 33 2 10 57 6.111 5.802 \n",
"3 4 29 2 4 19 0.681 0.516 \n",
"4 5 47 1 31 253 9.308 8.908 \n",
"\n",
" Defaulted DebtIncomeRatio Clus_km \n",
"0 0.0 6.3 0 \n",
"1 0.0 12.8 2 \n",
"2 1.0 20.9 0 \n",
"3 0.0 6.3 0 \n",
"4 0.0 7.2 1 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Clus_km\"] = labels\n",
"df.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily check the centroid values by averaging the features in each cluster."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"jupyter": {
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},
"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"data": {
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"<div>\n",
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" <th>Customer Id</th>\n",
" <th>Age</th>\n",
" <th>Edu</th>\n",
" <th>Years Employed</th>\n",
" <th>Income</th>\n",
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" <th>Other Debt</th>\n",
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" <td>10.724590</td>\n",
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"</table>\n",
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],
"text/plain": [
" Customer Id Age Edu Years Employed Income \\\n",
"Clus_km \n",
"0 432.468413 32.964561 1.614792 6.374422 31.164869 \n",
"1 410.166667 45.388889 2.666667 19.555556 227.166667 \n",
"2 402.295082 41.333333 1.956284 15.256831 83.928962 \n",
"\n",
" Card Debt Other Debt Defaulted DebtIncomeRatio \n",
"Clus_km \n",
"0 1.032541 2.104133 0.285185 10.094761 \n",
"1 5.678444 10.907167 0.285714 7.322222 \n",
"2 3.103639 5.765279 0.171233 10.724590 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('Clus_km').mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, lets look at the distribution of customers based on their age and income:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
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"deletable": true,
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}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"area = np.pi * ( X[:, 1])**2 \n",
"plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)\n",
"plt.xlabel('Age', fontsize=18)\n",
"plt.ylabel('Income', fontsize=16)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f9cef2aa5f8>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.mplot3d import Axes3D \n",
"fig = plt.figure(1, figsize=(8, 6))\n",
"plt.clf()\n",
"ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
"\n",
"plt.cla()\n",
"# plt.ylabel('Age', fontsize=18)\n",
"# plt.xlabel('Income', fontsize=16)\n",
"# plt.zlabel('Education', fontsize=16)\n",
"ax.set_xlabel('Education')\n",
"ax.set_ylabel('Age')\n",
"ax.set_zlabel('Income')\n",
"\n",
"ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each cluster are similar to each other demographically.\n",
"Now we can create a profile for each group, considering the common characteristics of each cluster. \n",
"For example, the 3 clusters can be:\n",
"\n",
"- AFFLUENT, EDUCATED AND OLD AGED\n",
"- MIDDLE AGED AND MIDDLE INCOME\n",
"- YOUNG AND LOW INCOME"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2>Want to learn more?</h2>\n",
"\n",
"IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
"\n",
"Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
"\n",
"<h3>Thanks for completing this lesson!</h3>\n",
"\n",
"<h4>Author: <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
"<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
"\n",
"<hr>\n",
"\n",
"<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
]
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