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ML Mavericks – Machine Learning Coursework

Course: Machine Learning / Data Mining
Group Name: ML Mavericks
University: University of Surrey
Term: Spring 2025


📌 Project Overview

This project implements a complete Machine Learning / Data Mining pipeline using Python and Prolog. It covers multiple learning tasks across regression, classification, clustering, logic-based learning (ILP), and reinforcement learning.
We apply various machine learning algorithms to different datasets and evaluate their performance using appropriate metrics and visualizations.


🧪 Datasets Used

Dataset Type Purpose
Regression (Garment) Used for SVM, Decision Tree, MLP
Regression (Incident) Used for SVM, Decision Tree, MLP
Classification Used for SVM, Neural Network
Clustering Used for KMeans, Hierarchical
ILP Used Aleph on Census Dataset
Reinforcement Learning Used RL environments in Colab

🧠 Algorithms Implemented

  • Regression: SVM, Decision Tree, MLP
  • Classification: SVM, Neural Network
  • Clustering: KMeans, Hierarchical
  • Logic-Based Learning: ILP (Aleph)
  • Reinforcement Learning: Q-Learning, Deep Q-Learning

🗂️ Repository Structure

mlmavericks_coursework/
├── data/
│   ├── raw/              # Original datasets
│   └── processed/        # Cleaned and transformed datasets
├── media/                # RL video outputs
├── models/               # Saved models (.pkl/.pth)
├── notebooks/            # Jupyter notebooks organized by task
│   ├── classification/
│   ├── clustering/
│   ├── regression/
│   ├── reinforcement learning/
│   └── induction logic programming (ILP)/
├── src/                  # Utility scripts and functions
├── requirements.txt      # Environment dependencies
└── README.md             # Project overview

🚀 How to Run

Local (Jupyter):

  1. Install dependencies manually or use Anaconda.
  2. Open Jupyter Lab/Notebook.
  3. Navigate to the desired notebook and run all cells.

Google Colab (ILP and RL):

  1. Open notebooks in Colab.
  2. Install dependencies via !pip install ....
  3. Upload relevant data/model files if needed.

📤 Outputs

  • Evaluation Metrics: Notebooks output tables and scores.
  • Plots: Visualizations of model performance.
  • RL Videos: media/rl-video-episode-0.mp4
  • ILP Rules: Prolog outputs in ILP notebooks.

👨‍💻 Team Members

  • Ritwik Mishra
  • Shivasmi Sharma
  • Ishwari Niphade
  • Arpit Mahapatra
  • Suraj Borude

📄 License

This project is for educational purposes only.