This repository contains a collection of Machine Learning lab experiments conducted as part of the academic curriculum in 2025. The objective of these experiments is to provide hands-on experience with various supervised and unsupervised machine learning algorithms using Python and industry-standard libraries like Scikit-learn, Pandas, and Matplotlib.

Each experiment is designed to reinforce core ML concepts such as model training, evaluation, hyperparameter tuning, dimensionality reduction, and clustering. The labs also explore the impact of data preprocessing techniques such as outlier handling, feature scaling, and feature selection on model performance.

This repository serves both as a submission portfolio and as a practical reference for anyone learning or reviewing essential ML techniques.

To get a complete understanding of each experiment including:

Aim

Objectives

Dataset Description

Theoretical Overview

Code / Implementation

Output and Visualizations

👉 Please refer to the ML LAB PDF included in this repository. It is mapped according to the experiment numbers listed in the index file.