A project focused on predicting bank term deposit subscriptions using classification models. The repository contains data analysis, feature engineering, and model implementation steps aimed at maximizing prediction accuracy.

"Bank Marketing Term Deposit Prediction: A classification project using machine learning algorithms to predict whether a customer will subscribe to a term deposit based on demographic and financial data. Aim: Develop a model to improve marketing strategies and increase subscription rates."

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Bank Term Deposit Subscription Prediction | ML Classification Project

ML project using Random Forest for classification (term deposit subscription) and regression (balance prediction) on bank marketing data. Includes preprocessing, feature selection, model tuning, and joblib model saving.

A Term Deposit Subscription Prediction Project is a classification problem that aims to predict whether a customer will subscribe to a term deposit based on their demographic, behavioral, and financial information. This type of project is typically built using machine learning models to help financial institutions improve their marketing campaigns.

A machine learning project that uses classification and clustering to optimize bank marketing campaigns. The system predicts term deposit subscriptions and segments customers for targeted outreach using models like Random Forest, XGBoost, and K-Means. Includes real-time prediction via a Streamlit app.