Book Image

The Data Analysis Workshop

By : Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev
Book Image

The Data Analysis Workshop

By: Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev

Overview of this book

Businesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you’ll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.

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Table of Contents (12 chapters)
Preface
About the Book
1
1. Bike Sharing Analysis
1. Bike Sharing Analysis
Introduction
Understanding the Data
Data Preprocessing
Hypothesis Tests
Analysis of Weather-Related Features
Time Series Analysis
ARIMA Models
Summary
Free Chapter
2
2. Absenteeism at Work
2. Absenteeism at Work
Introduction
Initial Data Analysis
Initial Analysis of the Reason for Absence
Analysis of Social Drinkers and Smokers
Body Mass Index
Age and Education Factors
Transportation Costs and Distance to Work Factors
Temporal Factors
Summary
3
3. Analyzing Bank Marketing Campaign Data
3. Analyzing Bank Marketing Campaign Data
Introduction
Initial Data Analysis
Impact of Numerical Features on the Outcome
Modeling the Relationship via Logistic Regression
Linear Regression
Logistic Regression
Summary
4
4. Tackling Company Bankruptcy
4. Tackling Company Bankruptcy
Introduction
Missing Value Analysis
Imputation of Missing Values
Splitting the Features
Feature Selection with Lasso
Summary
5
5. Analyzing the Online Shopper's Purchasing Intention
5. Analyzing the Online Shopper's Purchasing Intention
Introduction
Importing the Data
Exploratory Data Analysis
Clustering
Summary
6
6. Analysis of Credit Card Defaulters
6. Analysis of Credit Card Defaulters
Introduction
Importing the Data
Data Preprocessing
Exploratory Data Analysis
Correlation
Building a Profile of a High-Risk Customer
Summary
7
7. Analyzing the Heart Disease Dataset
7. Analyzing the Heart Disease Dataset
Introduction
Summary
8
8. Analyzing Online Retail II Dataset
8. Analyzing Online Retail II Dataset
Introduction
Data Cleaning
Data Preparation and Feature Engineering
Data Analysis
Summary
9
9. Analysis of the Energy Consumed by Appliances
9. Analysis of the Energy Consumed by Appliances
Introduction
Summary
10
10. Analyzing Air Quality
10. Analyzing Air Quality
Introduction
About the Dataset
Outliers
Missing Values
Heatmaps
Summary
Appendix
1. Bike Sharing Analysis
2. Absenteeism at Work
3. Analyzing Bank Marketing Campaign Data
4. Tackling Company Bankruptcy
5. Analyzing the Online Shopper's Purchasing Intention
6. Analysis of Credit Card Defaulters
7. Analyzing the Heart Disease Dataset
8. Analyzing Online Retail II Dataset
9. Analysis of the Energy Consumed by Appliances
10. Analyzing Air Quality

Feature Selection with Lasso

Feature selection is one of the most important steps to be performed before building any kind of machine learning model. In a dataset, not all the columns are going to have an impact on the dependent variable. If we include all the irrelevant features for model building, we'll end up building a model with bad performance. This gives rise to the need to perform feature selection. In this section, we will be performing feature selection using the lasso method.

Lasso regularization is a method of feature selection where the coefficients of irrelevant features are set to zero. By doing so, we remove the features that are insignificant and only the remaining significant features are included for further analysis.

Let's perform lasso regularization for our mean- and iterative-imputed DataFrames.

Lasso Regularization for Mean-Imputed DataFrames

Let's perform lasso regularization for the mean-imputed DataFrame 1.

As the first...

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