Machine Learning Labs

Machine Learning laboratory assignments and practical implementations using Python, NumPy, Pandas, Scikit-learn, and other ML libraries.

This repository contains my Machine Learning laboratory assignments completed during my B.Tech Computer Science and Engineering program.

Technologies Used

• Python
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• Google Colab
• Jupyter Notebook

Lab List

Lab No.	Experiment	Folder
Lab 01	NumPy and Pandas Basics	Lab-01-NumPy-Pandas
Lab 02	Feature Engineering	Lab-02-Feature-Engineering
Lab 03	Linear Regression	Lab-03-Linear-Regression
Lab 04	Decision Tree Learning	Lab-04-Decision-Tree-Learning
Lab 05	K-Nearest Neighbors	Lab-05-K-Nearest-Neighbors
Lab 06	Linear Regression	Lab-06-Linear-Regression
Lab 07	Statistical Analysis of Housing Dataset	Lab-07-Statistical-Analysis
Lab 08	Principal Component Analysis (PCA)	Lab-08-Principal-Component-Analysis

Repository Contents

Each lab folder contains the relevant Jupyter Notebook, dataset files, and README documentation. PDF reports are included where applicable.

Lab 08: Principal Component Analysis (PCA)

Objective: Apply PCA to the Housing dataset and reduce dimensionality while preserving at least 90% of the total variance.

Dataset: Housing.csv

Results:

• Original dataset: 545 rows and 13 columns.
• Principal components retained: 11.
• Total variance preserved: 92.64%.
• Variance-retention requirement: At least 90%, successfully achieved.

Files:

• Lab_08_Principal_Component_Analysis.ipynb: PCA implementation and outputs.
• Lab-08-Principal-Component-Analysis - Colab.pdf: PDF export of the notebook.
• Housing.csv: Original dataset.
• Housing_PCA_Result.csv: Reduced dataset generated by PCA.
• README.md: Lab-specific documentation.

Tools

• Python
• Google Colab
• Git and GitHub