Customer Churn Prediction & Analysis for XYZ Bank | Machine Learning

In this project, I analyzed customer data from XYZ Bank to predict customer churn using data analysis and machine learning techniques. The project includes:

Exploratory Data Analysis (EDA): Cleaning the dataset, visualizing key trends, and identifying factors influencing customer churn.

Feature Grouping: Categorizing features into meaningful groups to understand their relationships with churn.

Machine Learning Model: Training a Logistic Regression model, achieving an accuracy of 99.85% in predicting customer churn.

This project provides valuable insights into customer retention and helps identify high-risk customers to improve retention strategies.