Developed regression models to predict continuous values using structured datasets, with a focus on model evaluation, validation techniques, and understanding overfitting.

This project focuses on regression analysis using datasets such as the Boston Housing dataset to predict continuous values. The goal was to understand how machine learning models can be applied to real-world prediction problems. I implemented regression models and evaluated their performance using techniques such as K-fold cross-validation to ensure reliable results. The project also explored how model complexity affects performance, including identifying and reducing overfitting. This work helped build a strong foundation in model evaluation, validation strategies, and the practical challenges of working with real datasets.