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Commit c7225fec authored by Mishra, Ritwik (PG/T - Comp Sci & Elec Eng)'s avatar Mishra, Ritwik (PG/T - Comp Sci & Elec Eng)
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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.
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## 🧪 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.
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