Machine learning project predicting bank term deposit subscriptions using customer demographics and campaign data. Features multiple ML models (Random Forest, Logistic Regression, Decision Tree), SMOTE for class balancing, and interactive Streamlit app for real-time predictions.

This project analyzes the Bank Marketing dataset to predict whether a client will subscribe to a term deposit.The workflow includes data loading, preprocessing, EDA, feature engineering, and building machine learning models to classify client responses.The goal is to build a predictive model to assist the bank in potential clients more effectively.

End-to-end ML project predicting term deposit subscriptions using bank marketing data.