The Data Analysis Workshop: Solve business problems with state-of-the-art data analysis models, developing expert data analysis skills along the way

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FM
Copyright
Table of Contents
Preface
Chapter 1: Bike Sharing Analysis
	Introduction
	Understanding the Data
	Data Preprocessing
		Exercise 1.01: Preprocessing Temporal and Weather Features
		Registered versus Casual Use Analysis
		Exercise 1.02: Analyzing Seasonal Impact on Rides
	Hypothesis Tests
		Exercise 1.03: Estimating Average Registered Rides
		Exercise 1.04: Hypothesis Testing on Registered Rides
	Analysis of Weather-Related Features
		Exercise 1.05: Evaluating the Difference between the Pearson and Spearman Correlations
			Correlation Matrix Plot
	Time Series Analysis
		Exercise 1.06: Time Series Decomposition in Trend, Seasonality, and Residual Components
	ARIMA Models
		Exercise 1.07: ACF and PACF Plots for Registered Rides
		Activity 1.01: Investigating the Impact of Weather Conditions on Rides
	Summary
Chapter 2: Absenteeism at Work
	Introduction
	Initial Data Analysis
		Exercise 2.01: Identifying Reasons for Absence
	Initial Analysis of the Reason for Absence
	Analysis of Social Drinkers and Smokers
		Exercise 2.02: Identifying Reasons of Absence with Higher Probability Among Drinkers and Smokers
		Exercise 2.03: Identifying the Probability of Being a Drinker/Smoker, Conditioned to Absence Reason
	Body Mass Index
	Age and Education Factors
		Exercise 2.04: Investigating the Impact of Age on Reason for Absence
		Exercise 2.05: Investigating the Impact of Education on Reason for Absence
	Transportation Costs and Distance to Work Factors
	Temporal Factors
		Exercise 2.06: Investigating Absence Hours, Based on the Day of the Week and the Month of the Year
		Activity 2.01: Analyzing the Service Time and Son Columns
	Summary
Chapter 3: Analyzing Bank Marketing Campaign Data
	Introduction
	Initial Data Analysis
		Exercise 3.01: Analyzing Distributions of Numerical Features in the Banking Dataset
		Exercise 3.02: Analyzing Distributions of Categorical Features in the Banking Dataset
	Impact of Numerical Features on the Outcome
		Exercise 3.03: Hypothesis Test of the Difference of Distributions in Numerical Features
	Modeling the Relationship via Logistic Regression
	Linear Regression
	Logistic Regression
		Exercise 3.04: Logistic Regression on the Full Marketing Campaign Data
		Activity 3.01: Creating a Leaner Logistic Regression Model
	Summary
Chapter 4: Tackling Company Bankruptcy
	Introduction
		Explanation of Some of the Important Features
		Importing the Data
		Exercise 4.01: Importing Data into DataFrames
		Pandas Profiling
			Running Pandas Profiling
			Pandas Profiling Report for DataFrame 1
			Pandas Profiling Report for DataFrame 2
	Missing Value Analysis
		Exercise 4.02: Performing Missing Value Analysis for the DataFrames
	Imputation of Missing Values
		Mean Imputation
		Exercise 4.03: Performing Mean Imputation on the DataFrames
		Iterative Imputation
		Exercise 4.04: Performing Iterative Imputation on the DataFrame
	Splitting the Features
	Feature Selection with Lasso
		Lasso Regularization for Mean-Imputed DataFrames
		Lasso Regularization for Iterative-Imputed DataFrames
		Activity 4.01: Feature Selection with Lasso
	Summary
Chapter 5: Analyzing the Online Shopper's Purchasing Intention
	Introduction
		Data Dictionary
	Importing the Data
	Exploratory Data Analysis
		Univariate Analysis
			Baseline Conversion Rate from the Revenue Column
			Visitor-Wise Distribution
			Traffic-Wise Distribution
		Exercise 5.01: Analyzing the Distribution of Customers Session on the Website
			Region-Wise Distribution
		Exercise 5.02: Analyzing the Browser and OS Distribution of Customers
			Administrative Pageview Distribution
			Information Pageview Distribution
			Special Day Session Distribution
		Bivariate Analysis
			Revenue Versus Visitor Type
			Revenue Versus Traffic Type
		Exercise 5.03: Analyzing the Relationship between Revenue and Other Variables
		Linear Relationships
			Bounce Rate versus Exit Rate
			Page Value versus Bounce Rate
			Page Value versus Exit Rate
			Impact of Administration Page Views and Administrative Pageview Duration on Revenue
			Impact of Information Page Views and Information Pageview Duration on Revenue
	Clustering
		Method to Find the Optimum Number of Clusters
		Exercise 5.04: Performing K-means Clustering for Informational Duration versus Bounce Rate
			Performing K-means Clustering for Informational Duration versus Exit Rate
		Activity 5.01: Performing K-means Clustering for Administrative Duration versus Bounce Rate and Administrative Duration versus Exit Rate
	Summary
Chapter 6: Analysis of Credit Card Defaulters
	Introduction
	Importing the Data
	Data Preprocessing
	Exploratory Data Analysis
		Univariate Analysis
		Bivariate Analysis
		Exercise 6.01: Evaluating the Relationship between the DEFAULT Column and the EDUCATION and MARRIAGE Columns
			PAY_1 versus DEFAULT
			Balance versus DEFAULT
		Exercise 6.02: Evaluating the Relationship between the AGE and DEFAULT Columns
	Correlation
		Activity 6.01: Evaluating the Correlation between Columns Using a Heatmap
	Building a Profile of a High-Risk Customer
	Summary
Chapter 7: Analyzing the Heart Disease Dataset
	Introduction
		Exercise 7.01: Loading and Understanding the Data
		Outliers
		Exercise 7.02: Checking for Outliers
		Activity 7.01: Checking for Outliers
		Exercise 7.03: Plotting the Distributions and Relationships Between Specific Features
		Activity 7.02: Plotting Distributions and Relationships between Columns with Respect to the Target Column
		Exercise 7.04: Plotting the Relationship between the Presence of Heart Disease and Maximum Recorded Heart Rate
		Activity 7.03: Plotting the Relationship between the Presence of Heart Disease and the Cholesterol Column
		Exercise 7.05: Observing Correlations with a Heatmap
	Summary
Chapter 8: Analysis of Credit Card Defaulters
	Introduction
	Data Cleaning
		Exercise 8.01: Loading and Cleaning Our Data
	Data Preparation and Feature Engineering
		Exercise 8.02: Preparing Our Data
	Data Analysis
		Exercise 8.03: Finding the Answers in Our Data
	Activity 8.01: Performing Data Analysis on the Online Retail II Dataset
	Summary
Chapter 9: Analysis of the Energy Consumed by Appliances
	Introduction
		Exercise 9.01: Taking a Closer Look at the Dataset
		Exercise 9.02: Analyzing the Light Energy Consumption Column
		Activity 9.01: Analyzing the Appliances Energy Consumption Column
		Exercise 9.03: Performing Feature Engineering
		Exercise 9.04: Visualizing the Dataset
		Activity 9.02: Observing the Trend between a_energy and day
		Exercise 9.05: Plotting Distributions of the Temperature Columns
		Activity 9.03: Plotting Distributions of the Humidity Columns
		Exercise 9.06: Plotting out_b, out_hum, visibility, and wind
	Summary
Chapter 10: Analyzing Air Quality
	Introduction
	About the Dataset
		Exercise 10.01: Concatenating Multiple DataFrames and Checking for Missing Values
	Outliers
		Exercise 10.02: Identifying Outliers
		Activity 10.01: Checking for Outliers
	Missing Values
		Exercise 10.03: Dealing with Missing Values
		Exercise 10.04: Observing the Concentration of PM25 and PM10 per Year
		Activity 10.02: Observing the Pollutant Concentration per Year
		Activity 10.03: Observing Pollutant Concentration per Month
	Heatmaps
		Exercise 10.05: Checking for Correlations between Features
	Summary
Appendix
Index