Logistic Regression is a variation of Linear Regression, used when the observed dependent variable, y, is categorical. It produces a formula that predicts the probability of the class label as a function of the independent variables.
About the dataset:
We will use a telecommunications dataset for predicting customer churn. This is a historical customer dataset where each row represents one customer. The data is relatively easy to understand, and you may uncover insights you can use immediately. Typically it is less expensive to keep customers than acquire new ones, so the focus of this analysis is to predict the customers who will stay with the company. This data set provides information to help you predict what behavior will help you to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.
Aim:To find accuracy using logistic regresseion.