This repository showcases my journey through various machine learning concepts and hands-on projects. Each project focuses on a different aspect of machine learning, ranging from foundational algorithms to advanced predictive modeling techniques. The goal is to build, explore, and explain machine learning concepts through well-documented examples and Jupyter notebooks. Contents: Polynomial Regression with Feature Engineering: Implemented a polynomial regression model to predict housing prices, exploring how feature engineering and polynomial features can model non-linear relationships effectively. Linear Regression and Feature Scaling: Examples demonstrating linear regression, feature scaling with Z-score normalization, and the impact of scaling on model performance. Real-World Housing Prices Analysis: A step-by-step project that uses mock housing data to illustrate the process of building and tuning a predictive model. Other ML Exercises: Including simple linear regression, multiple linear regression, and feature engineering experiments to understand the strengths and limitations of different approaches.
Each project includes: Jupyter Notebooks for code and results visualization. Detailed documentation and explanations of core machine learning techniques. Interactive plots and code that can be easily modified to explore further.
Feel free to explore, clone, or contribute. Let's learn machine learning together!