README.md

Projects In Machine Learning

Overview

This repository contains end-to-end machine learning notebook projects across multiple domains:

• Regression and tabular prediction
  • BoardGameReviewPredictor.ipynb
• Anomaly detection and fraud analytics
  • CreditCardFraudDetection.ipynb
• Natural language processing and text classification
  • IntroToNLP.ipynb
  • TextClassification.ipynb
• Unsupervised learning and dimensionality reduction
  • KMeans.ipynb
  • PrincipalComponentAnalysis.ipynb
• Computer vision
  • ObjectDetection.ipynb
  • ImageSuperResolution.ipynb

Detailed notebook-level documentation is available in NOTEBOOKS_CATALOG.md.

Startup Guide

1. Open this project folder in VS Code or JupyterLab.
2. Create and activate a Python environment.
3. Install core dependencies:

pip install numpy pandas matplotlib seaborn scikit-learn nltk torch torchvision opencv-python jupyter

4. Start Jupyter:

jupyter notebook

5. Open any notebook and run cells from top to bottom.
6. Dataset notes:
   • Some notebooks download data automatically (for example, MNIST/CIFAR through torchvision).
   • Some notebooks expect local datasets/assets to be available in the project folders.