Skip to content

Instantly share code, notes, and snippets.

Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save saidmrad/de0fa54ce52f31d75aec7a25e7bd6725 to your computer and use it in GitHub Desktop.
Save saidmrad/de0fa54ce52f31d75aec7a25e7bd6725 to your computer and use it in GitHub Desktop.
Created on Skills Network Labs
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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
}
},
"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": 1,
"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.datasets.samples_generator 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",
"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": 2,
"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": 3,
"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": 4,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f8d03f61390>"
]
},
"execution_count": 4,
"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], 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": 5,
"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": 6,
"metadata": {
"button": 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,
"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": 7,
"metadata": {
"button": 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,
"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": 8,
"metadata": {
"button": false,
"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,
"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!\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": 9,
"metadata": {
"button": 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",
"\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": 13,
"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": [
"# write your code here\n",
"k_means4 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
"k_means4.fit(X)\n",
"\n",
"fig = plt.figure(figsize=(6, 4))\n",
"colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means4.labels_))))\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"for k, col in zip(range(len(k_means4.cluster_centers_)), colors):\n",
" my_members = (k_means4.labels_ == k)\n",
" cluster_center = k_means4.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"
]
},
{
"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",
"-->\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",
"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": 14,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-10-19 16:44:37-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/Cust_Segmentation.csv\n",
"Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 67.228.254.196\n",
"Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|67.228.254.196|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 33426 (33K) [text/csv]\n",
"Saving to: ‘Cust_Segmentation.csv’\n",
"\n",
"Cust_Segmentation.c 100%[===================>] 32.64K --.-KB/s in 0.02s \n",
"\n",
"2020-10-19 16:44:37 (1.53 MB/s) - ‘Cust_Segmentation.csv’ saved [33426/33426]\n",
"\n"
]
}
],
"source": [
"!wget -O Cust_Segmentation.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/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": 15,
"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",
" .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>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": 15,
"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": 16,
"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",
" .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>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",
" <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>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>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>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>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>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": 16,
"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": 17,
"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": 17,
"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": 18,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 0 0 2 1 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0\n",
" 0 0 1 0 1 0 2 0 1 0 0 0 1 1 0 0 1 1 0 0 0 1 0 1 0 1 1 0 0 1 0 0 0 1 1 1 0\n",
" 0 0 0 0 1 0 1 1 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0\n",
" 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0\n",
" 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0\n",
" 0 0 0 0 1 0 0 1 0 1 0 0 1 2 0 1 0 0 0 0 0 0 2 1 0 0 0 0 1 0 0 1 1 0 1 0 1\n",
" 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 1 0 0 0 0\n",
" 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0\n",
" 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0\n",
" 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 2 0 0 0 1 0 1 1 1 0\n",
" 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
" 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 2\n",
" 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 1 0 2 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 0\n",
" 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0\n",
" 0 1 1 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 2 0 2 0\n",
" 0 2 0 0 0 0 0 0 0 0 0 1 0 1 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1\n",
" 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1\n",
" 1 0 0 1 0 1 0 0 1 0 1 0 0 2 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0\n",
" 1 0 0 0 1 0 2 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0\n",
" 0 0 1 0 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0\n",
" 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1\n",
" 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]\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",
"We assign the labels to each row in dataframe.\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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",
" .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>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",
" <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>6.3</td>\n",
" <td>0</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>12.8</td>\n",
" <td>1</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>20.9</td>\n",
" <td>0</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>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>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 Clus_km \n",
"0 0.0 6.3 0 \n",
"1 0.0 12.8 1 \n",
"2 1.0 20.9 0 \n",
"3 0.0 6.3 0 \n",
"4 0.0 7.2 2 "
]
},
"execution_count": 19,
"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": 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",
" .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>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",
" <tr>\n",
" <th>Clus_km</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>432.006154</td>\n",
" <td>32.967692</td>\n",
" <td>1.613846</td>\n",
" <td>6.389231</td>\n",
" <td>31.204615</td>\n",
" <td>1.032711</td>\n",
" <td>2.108345</td>\n",
" <td>0.284658</td>\n",
" <td>10.095385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>403.780220</td>\n",
" <td>41.368132</td>\n",
" <td>1.961538</td>\n",
" <td>15.252747</td>\n",
" <td>84.076923</td>\n",
" <td>3.114412</td>\n",
" <td>5.770352</td>\n",
" <td>0.172414</td>\n",
" <td>10.725824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>410.166667</td>\n",
" <td>45.388889</td>\n",
" <td>2.666667</td>\n",
" <td>19.555556</td>\n",
" <td>227.166667</td>\n",
" <td>5.678444</td>\n",
" <td>10.907167</td>\n",
" <td>0.285714</td>\n",
" <td>7.322222</td>\n",
" </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",
"1 403.780220 41.368132 1.961538 15.252747 84.076923 \n",
"2 410.166667 45.388889 2.666667 19.555556 227.166667 \n",
"\n",
" Card Debt Other Debt Defaulted DebtIncomeRatio \n",
"Clus_km \n",
"0 1.032711 2.108345 0.284658 10.095385 \n",
"1 3.114412 5.770352 0.172414 10.725824 \n",
"2 5.678444 10.907167 0.285714 7.322222 "
]
},
"execution_count": 20,
"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": 21,
"metadata": {
"button": 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": {
"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": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f8d23074160>"
]
},
"execution_count": 22,
"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=\"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ---------- | --------------------- |\n",
"| 2020-08-04 | 0 | Nayef | Upload file to Gitlab |\n",
"| | | | |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<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>\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.11"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment