ABSTRACT
Logistic regression is a supervised classifier and is one of the simplest and most commonly used Machine Learning algorithms that is used to model a dependent variable in binary classification. Logistic regression uses the sigmoid function to convert the linear regression model into a classifier. This chapter will provide a detailed understanding of logistic regression and its implementation in Python using the Scikit-Learn library. The importance of scaling the features and the different types of scaling is also presented. The logistic regression project in this chapter is used for classifying heart disease and non-heart disease occurrences. Moreover, a classifier is built for the identification of malignant versus benign cells using the derived image features. Sklearn’s LogisticRegression() object is used to implement the classifier.
