This repository contains various machine learning algorithms implemented as part of the second semester coursework. The project focuses on understanding and applying machine learning concepts through practical examples and real-world datasets.
The repository includes Jupyter notebooks detailing the implementation of different machine learning algorithms. One of the key highlights of this project is the exploration of the Red Wine Quality dataset, where linear regression is used to predict wine quality based on various physicochemical properties.
Through this project, you can gain insights into the practical application of machine learning algorithms, understand the concept of underfitting and overfitting, and learn about feature engineering. This project serves as a valuable resource for anyone looking to delve deeper into the field of machine learning.