This repository contains implementations of various simple regression models.
Overview In this project, we implement and compare several regression models including: Decision Tree Gradient Boosting K-Nearest Neighbors (KNN) Lasso Regression Linear Regression Random Forest (RF) Ridge Regression Stacking and Voting Regression (Combining multiple models of choice) Support Vector Regression (SVR) Extreme Gradient Boosting (XGBoost)
Directory Structure data/: Contains raw and processed datasets. models/: This directory contains model-specific notebooks for each type of regression model. knn/: K-Nearest Neighbors implementation. rf/: Random Forest implementation. svr/: Support Vector Regression implementation. xgb/: XGBoost implementation. dt/: Decision Tree Implementation requirements.txt: List of dependencies. README.md: Project overview and instructions.