Customer Churn Prediction Using Machine Learning

Developed an end-to-end machine learning solution to predict customer churn using historical customer behavior and subscription data. The project focused on identifying customers who are most likely to leave a service, enabling businesses to improve retention strategies through data-driven decision-making. The workflow included data preprocessing, exploratory data analysis (EDA), feature engineering, model training, evaluation, and performance comparison. Multiple classification algorithms were tested to identify the most effective model for customer churn prediction. Responsibilities Collected and analyzed customer behavior data. Cleaned and preprocessed the dataset. Performed Exploratory Data Analysis (EDA). Engineered features to improve model performance. Built and compared multiple machine learning classification models. Evaluated models using Accuracy, Precision, Recall, F1-Score, and ROC-AUC. Selected the best-performing model based on evaluation metrics. Visualized customer behavior patterns and model performance. Documented the complete machine learning workflow.