From d61f076442f222f8cc40bdd94876d7b958053980 Mon Sep 17 00:00:00 2001
From: "Mishra, Ritwik (PG/T - Comp Sci & Elec Eng)" <rm02120@surrey.ac.uk>
Date: Wed, 28 May 2025 14:33:34 +0000
Subject: [PATCH] Delete README.md

---
 README.md | 131 ------------------------------------------------------
 1 file changed, 131 deletions(-)
 delete mode 100644 README.md

diff --git a/README.md b/README.md
deleted file mode 100644
index 8f12fce..0000000
--- a/README.md
+++ /dev/null
@@ -1,131 +0,0 @@
-
-# 🤖 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 four different datasets and evaluate their performance using appropriate metrics and visualisations.
-
----
-
-## 🧪 Datasets Used
-
-| Dataset Type        | Purpose                          |
-|---------------------|----------------------------------|
-| Regression (Small)  | Used for SVM & Perceptron        |
-| Regression (Large)  | Used for Decision Trees & MLP    |
-| Classification      | Used for SVM & Neural Networks   |
-| Clustering          | Used for KNN & Hierarchical      |
-
----
-
-## 🧠 Algorithms Implemented
-
-| Category                | Algorithms                              |
-|--------------------------|------------------------------------------|
-| **Regression**           | SVM, Decision Tree, Perceptron, MLP     |
-| **Classification**       | SVM, Neural Network                     |
-| **Clustering**           | KNN Clustering, Hierarchical Clustering |
-| **Logic-Based Learning** | Aleph ILP, FOIL ILP (in Prolog)         |
-| **Reinforcement Learning** | Q-Learning, Deep Q-Learning (DQL)    |
-
----
-
-## 🗂️ Project Structure
-
-```
-ml_mavericks/
-├── data/                  # Contains all datasets (.csv)
-├── notebooks/             # EDA and initial experiments in Jupyter
-├── models/                # Saved ML models (optional)
-├── outputs/               # Metrics, plots, ILP & RL outputs
-├── src/                   # Modular Python code
-│   ├── preprocess.py
-│   ├── visualize.py
-│   ├── regression_models.py
-│   ├── classification_models.py
-│   ├── clustering_models.py
-│   ├── logic_learning_aleph.pl
-│   ├── logic_learning_foil.pl
-│   ├── reinforcement_q_learning.py
-│   ├── reinforcement_dql.py
-│   └── evaluate.py
-├── main.py                # Main pipeline controller
-└── requirements.txt       # Python dependencies
-```
-
----
-
-## 🚀 How to Run
-
-### 1. Install Dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### 2. Run the Pipeline
-
-Example for regression task using SVM:
-
-```bash
-python main.py --task regression --model svm --dataset regression_small
-```
-
----
-
-## 📤 View Outputs
-
-- Evaluation metrics: `/outputs/evaluation_results.csv`
-- Visualisations: `/outputs/plots/`
-- ILP rule output: `/outputs/logic_output/`
-- Reinforcement learning logs: `/outputs/reinforcement_logs/`
-
----
-
-## 🖼️ Visualisations
-
-The pipeline outputs various visual aids:
-- Confusion matrices
-- Regression error plots
-- Clustering dendrograms
-- RL training reward curves
-
----
-
-## 📘 Tools & Technologies
-
-- **Python 3.x**
-- Libraries: `scikit-learn`, `matplotlib`, `seaborn`, `gym`, `stable-baselines3`
-- **SWI-Prolog** for ILP (Aleph & FOIL)
-
----
-
-## 👨‍💻 Team Members
-
-**Group Name:** ML Mavericks  
-- Ritwik Mishra
-- Shivasmi Sharma  
-- Ishwari Niphade
-- Arpit Mahapatra
-- Suraj Borude
-
----
-
-## 📌 Project Status
-
-✅ In development – submitted as coursework for ML module Spring 2025.
-
----
-
-## 📄 License
-
-This project is for educational use only.
-- 
GitLab