#### 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.
Preface
1. Bike Sharing Analysis
Free Chapter
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

# Logistic Regression

Logistic regression is very similar to the linear regression technique we introduced in the previous section, with the only difference that the target variable, `Y`, assumes only values in a discrete set; say, for simplicity {0, 1}. If we were to approach such a problem as a logistic regression problem, the output of the right-hand side of the equation in Figure 3.17 could easily go way beyond the values 0 and 1. Furthermore, even by limiting the output, it will still be able to assume all the values in the interval [0, 1]. For this reason, the idea behind logistic regression is to model the probability of the target variable Y, to assume one of the values (say 1). In this case, all the values between 0 and 1 will be reasonable.

With `p`, let's denote the probability of the target variable, `Y`, being equal to 1 when it's given a specific feature `x`:

Figure 3.32: Definition of p

Let's also define the `logit` function:

...