This repository is designed to document and track my progress as I work with Linear Regression models. The goal is to store the evolution of my approach, from basic implementations to more advanced techniques, with a focus on parameter tuning, model evaluation, and real-world applications.
This repository serves as a journal for:
Tracking my personal development and understanding of linear regression. Saving and reviewing code that demonstrates the incremental improvements and challenges faced during the learning process. Providing a reference point for future projects that involve regression tasks.
Linear Regression Models: The repository includes various implementations of linear regression, from simple models to more complex ones that incorporate feature selection, regularization techniques, and model diagnostics.
Focus on key metrics such as Mean Squared Error (MSE), R-squared, and Adjusted R-squared to assess model performance and generalization to unseen data.
The files document the learning process, highlighting challenges, improvements, and key insights gained during experimentation with different datasets.
Python: Used to implement linear regression models. Scikit-learn: For building and evaluating the regression models. Pandas & NumPy: For dataset manipulation and feature engineering. Matplotlib & Seaborn: To visualize the regression results and residual plots.
This repository will continue to evolve as I explore more advanced topics, such as regularization techniques (Ridge, Lasso), polynomial regression, and handling multicollinearity. It will also include case studies from real-world datasets to demonstrate the practical applications of linear regression.