This project involves building a robust machine learning model to predict customer churn for a telecommunications company. The workflow includes comprehensive data cleaning, exploratory data analysis (EDA), one-hot encoding for categorical features, and standard scaling for numerical features. Multiple models, including Logistic Regression and Random Forest, were trained and evaluated. The Random Forest model was further optimized using GridSearchCV to find the best hyperparameters, resulting in improved accuracy. The final tuned model is saved using pickle and deployed as an interactive web application with Streamlit for real-time predictions.
