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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
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"source": [
"<center>\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# K-Means Clustering\n",
"\n",
"Estimated time needed: **25** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"- Use scikit-learn's K-Means Clustering to cluster data\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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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",
"\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",
"In this notebook we practice k-means clustering with 2 examples:\n",
"\n",
"- k-means on a random generated dataset\n",
"- Using k-means for customer segmentation\n"
]
},
{
"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>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
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},
"source": [
"### Import libraries\n",
"\n",
"Lets first import the required libraries.\n",
"Also run <b> %matplotlib inline </b> since we will be plotting in this section.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import random \n",
"import numpy as np \n",
"import matplotlib.pyplot as plt \n",
"from sklearn.cluster import KMeans \n",
"from sklearn import datasets\n",
"#from sklearn.datasets.samples_generator import make_blobs\n",
"# Replaced by sklearn.datasets\n",
"from sklearn.datasets import make_blobs\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h1 id=\"random_generated_dataset\">k-Means on a randomly generated dataset</h1>\n",
"\n",
"Lets create our own dataset for this lab!\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"np.random.seed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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",
"\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",
"execution_count": 9,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Display the scatter plot of the randomly generated data.\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f95f78cf1f0>"
]
},
"execution_count": 10,
"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,
"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.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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",
"\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>.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"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,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"KMeans(n_clusters=4, n_init=12)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_means.fit(X)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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> \n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 1, 1, ..., 3, 2, 2], dtype=int32)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_means_labels = k_means.labels_\n",
"k_means_labels"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also get the coordinates of the cluster centers using KMeans' <b> .cluster_centers_ </b> and save it as <b> k_means_cluster_centers </b>\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.96900523, 0.98370298],\n",
" [ 1.99741008, -3.01666822],\n",
" [-2.03743147, -0.99782524],\n",
" [ 3.97334234, 3.98758687]])"
]
},
"execution_count": 14,
"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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"creating_visual_plot\">Creating the Visual Plot</h2>\n",
"\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!\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Please read through the code and comments to understand how to plot the model.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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",
"\n",
"Try to cluster the above dataset into 3 clusters. \n",
"Notice: do not generate data again, use the same dataset as above.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"k_means = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
"k_means.fit(X)\n",
"k_means_labels = k_means.labels_\n",
"k_means_labels\n",
"k_means_cluster_centers = k_means.cluster_centers_\n",
"k_means_cluster_centers\n",
"\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(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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\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",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h1 id=\"customer_segmentation_K_means\">Customer Segmentation with K-Means</h1>\n",
"\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)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2021-03-10 16:19:17-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%204/data/Cust_Segmentation.csv\n",
"Résolution de cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)… 169.63.118.104\n",
"Connexion à cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443… connecté.\n",
"requête HTTP transmise, en attente de la réponse… 200 OK\n",
"Taille : 33426 (33K) [text/csv]\n",
"Enregistre : «Cust_Segmentation.csv»\n",
"\n",
"Cust_Segmentation.c 100%[===================>] 32,64K --.-KB/s ds 0,02s \n",
"\n",
"2021-03-10 16:19:18 (1,73 MB/s) - «Cust_Segmentation.csv» enregistré [33426/33426]\n",
"\n"
]
}
],
"source": [
"!wget -O Cust_Segmentation.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%204/data/Cust_Segmentation.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Load Data From CSV File\n",
"\n",
"Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv.\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Customer Id</th>\n",
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" <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",
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" </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": 18,
"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\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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.\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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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>DebtIncomeRatio</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
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" <td>20.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
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" <td>8.908</td>\n",
" <td>0.0</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 DebtIncomeRatio \n",
"0 0.0 6.3 \n",
"1 0.0 12.8 \n",
"2 1.0 20.9 \n",
"3 0.0 6.3 \n",
"4 0.0 7.2 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = cust_df.drop('Address', axis=1)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Normalizing over the standard deviation\n",
"\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.\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": 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": 21,
"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>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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.\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"insights\">Insights</h2>\n",
"\n",
"We assign the labels to each row in dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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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>DebtIncomeRatio</th>\n",
" <th>Clus_km</th>\n",
" </tr>\n",
" </thead>\n",
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" <th>2</th>\n",
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" <th>3</th>\n",
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" <td>19</td>\n",
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" <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,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily check the centroid values by averaging the features in each cluster.\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <th></th>\n",
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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",
" <th>Other Debt</th>\n",
" <th>Defaulted</th>\n",
" <th>DebtIncomeRatio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Clus_km</th>\n",
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" <td>5.678444</td>\n",
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" <td>7.322222</td>\n",
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" <th>2</th>\n",
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" <td>41.368132</td>\n",
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" <td>3.114412</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Customer Id Age Edu Years Employed Income \\\n",
"Clus_km \n",
"0 432.006154 32.967692 1.613846 6.389231 31.204615 \n",
"1 410.166667 45.388889 2.666667 19.555556 227.166667 \n",
"2 403.780220 41.368132 1.961538 15.252747 84.076923 \n",
"\n",
" Card Debt Other Debt Defaulted DebtIncomeRatio \n",
"Clus_km \n",
"0 1.032711 2.108345 0.284658 10.095385 \n",
"1 5.678444 10.907167 0.285714 7.322222 \n",
"2 3.114412 5.770352 0.172414 10.725824 "
]
},
"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:\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 0x7f95f4193b50>"
]
},
"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,
"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\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"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=\"https://www.ibm.com/analytics/spss-statistics-software\">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://www.ibm.com/cloud/watson-studio\">Watson Studio</a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"## Author\n",
"\n",
"Saeed Aghabozorgi\n",
"\n",
"### Other Contributors\n",
"\n",
"<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ---------- | ---------------------------------- |\n",
"| 2020-11-03 | 2.1 | Lakshmi | Updated URL of csv |\n",
"| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
"| | | | |\n",
"| | | | |\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
]
}
],
"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": "3.9.1"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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