The goal of this exercise is to perform Exploratory Data Analysis (EDA) and Logistic Regression on the bank churn data to predict customer churn. The steps involved are loading the dataset, exploring the data, visualizing the data, conducting feature engineering, splitting the data into training and test sets, normalizing the data, fitting the logistic regression model, and evaluating the model performance. The performance of the model is evaluated using the confusion matrix and classification report. The end result of this exercise is to determine the most important factors that contribute to customer churn and to predict customer churn using logistic regression.