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):
- Install dependencies manually or use Anaconda.
- Open Jupyter Lab/Notebook.
- Navigate to the desired notebook and run all cells.
Google Colab (ILP and RL):
- Open notebooks in Colab.
- Install dependencies via
!pip install ...
. - 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.