Customer Churn Prediction using Machine Learning

This project focuses on predicting customer churn using machine learning techniques. Customer churn prediction helps businesses identify customers who are likely to stop using their services. By analyzing customer behavior and historical data, machine learning models can predict which customers are at risk of leaving. Project workflow included: • Data cleaning and preprocessing • Exploratory Data Analysis (EDA) • Feature engineering • Training classification models • Evaluating model performance Algorithms used include Logistic Regression, Random Forest, and Decision Trees. Technologies used: Python Pandas NumPy Scikit-learn Matplotlib Seaborn This project demonstrates how machine learning can help businesses improve customer retention and make data-driven decisions.