Regression and Classification Modeling Project

Applied various supervised learning techniques on real-world datasets to explore regression and classification problems. Implemented Simple, Multiple, and Polynomial Regression models to predict continuous variables, and evaluated them using metrics like R² and MSE. Also built a Logistic Regression model for binary classification tasks, evaluated using confusion matrices. The entire process included data cleaning, preprocessing, EDA, feature selection, and performance comparison using Python libraries like scikit-learn, pandas, matplotlib, and seaborn.