Bank Marketing Campaign Prediction
Dug into bank marketing campaign data to figure out which customers are actually worth calling. Built predictive models that cut through demographics, financial history, and past campaign interactions to identify high-conversion prospects — so marketing teams stop wasting calls on people who were never going to say yes.
Python
Machine Learning
Scikit-learn
Feature Engineering
Data Analysis
Jupyter Notebook
This AI-powered marketing prediction system represents a comprehensive approach to leveraging machine learning for optimizing bank marketing campaigns. By implementing advanced predictive models including Random Forest, Gradient Boosting, and Neural Networks, the system automatically identifies the most promising prospects and predicts campaign success rates. The solution includes automated feature engineering, hyperparameter optimization, and real-time prediction capabilities, enabling data-driven marketing decisions that significantly improve conversion rates and ROI.