Predicting bank term deposit subscriptions using Gradient Boosting, feature importance analysis, and customer segmentation for targeted marketing.

This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.

Implementation of Machine Learning Algorithms in python from scratch