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‎Ch 8 Clustering Analysis/Chaper 8 Excercises.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"## Hands-On Data Preprocessing in Python\n",
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"Learn how to effectively prepare data for successful data analytics\n",
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" \n",
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" AUTHOR: Dr. Roy Jafari \n",
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"\n",
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"### Chapter 8: Clustering Analysis\n",
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"#### Excercises"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Excercise 1\n",
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"In your own words, answer the following two questions. Use at most 200 words, to answer each question.\n",
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"\n",
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" a.\tWhat is the difference between Classification and Prediction?\n",
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" b.\tWhat is the difference between Classification and Clustering?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Excercise 2\n",
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"Consider Figure 8.6 regarding the necessity of normalization before performing Clustering analysis. With this new appreciation you developed in this chapter, would you like to change your answer to the first exercise question from the previous chapter?\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Excercise 3\n",
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"In this chapter, we used WH Report_preprocessed.csv to form meaningful clusters of countries only using 2019 data. In this exercise, we want to use the data of all the years 2010-2019. Perform the following steps to do this."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" a.\tUse a .pivot() function to restructure the data so each combination of year and happiness index has a column. In other words, the data of the year is recorded in the long format and we would like to change that into the wide format. We will not need the columns population and Continent. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" b.\t Normalize pvt_df and assign into Xs."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" c.\tUse K-means and Xs to find 3 clusters among the data objects. Report the members of each cluster. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" d.\tUse a heatmap to perform centroid analysis. As the number of columns is many for this clustering, you may have to resize the heatmap bigger so you can use it for analysis. Make sure you’ve given a name to each cluster. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Excercise 4\n",
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"For this exercise we will be using the dataset Mall_Customers.xlsx to form 4 meaningful clusters of customers. The following steps will help you to do this correctly. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" a.\tRead the data into the pandas DataFrame customer_df."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" b.\tSet CustomerID as the index of customer_df and binary code the column Gender."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" c.\tClean the columns names by using respectively Gender, Age, Annual_income, Spending_score."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" d.\tNormalize customer_df and load it to the variable Xs."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" e.\tUse K-means and Xs to find 4 clusters among the data objects. Report the members of each cluster."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "markdown",
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"f.\tUse a heatmap to perform centroid analysis. Make sure you’ve given a name to each cluster."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" g.\tWhy did we binary code the attribute Gender in b?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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