Customer Churn Prediction Model Using Machine Learning

Overview Developed a machine learning model to predict customer churn based on behavioral and transactional data. The goal was to identify customers likely to leave and provide actionable insights. Dataset Structured dataset with customer demographics and usage patterns Included features such as tenure, activity, and engagement metrics What Was Done Data cleaning and preprocessing (handled missing values & duplicates) Exploratory Data Analysis (EDA) to identify key patterns Feature selection to improve model performance Built classification models (Logistic Regression, Random Forest) Tuned hyperparameters for better accuracy Results Achieved high prediction accuracy Identified key factors influencing churn Delivered a model ready for real-world use Tools & Technologies Python, Pandas, NumPy, Scikit-learn, Matplotlib