Customer Churn Prediction using Machine Learning

I developed a comprehensive Customer Churn Prediction system as an end-to-end Data Science project, focusing on extracting insights from customer data and building robust predictive models. The workflow began with an in-depth Exploratory Data Analysis (EDA) to identify patterns, correlations, and key factors influencing customer churn. This included handling missing values, detecting outliers, and analyzing feature distributions to better understand the dataset. Next, I performed data preprocessing and feature engineering, including encoding categorical variables, scaling numerical features, and selecting the most impactful features to improve model performance. I implemented and compared multiple machine learning models such as: Logistic Regression (baseline model) Random Forest (for capturing non-linear relationships) LightGBM (for high-performance gradient boosting) To ensure model reliability, I applied Stratified Cross-Validation and evaluated performance using multiple metrics including Accuracy, Precision, Recall, and F1-score. I further enhanced the model using Hyperparameter Tuning techniques (Grid Search / Random Search) to achieve optimal performance. In addition, I provided clear and insightful data visualizations using libraries like Matplotlib and Seaborn to illustrate trends, feature importance, and model performance in an understandable way. The final output includes a well-structured and reproducible pipeline that takes raw data as input and produces accurate churn predictions, making it suitable for real-world business applications.