Machine Learning in C

Welcome to Machine Learning in C, a collection of hands-on mini-projects that demonstrate core machine learning concepts using the C programming language. This repository is designed for students, enthusiasts, and anyone interested in understanding how classic ML algorithms work under the hood, without relying on high-level libraries.

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📚 Project Overview

This repository contains three independent mini-projects, each focusing on a different machine learning paradigm:

1. K-Nearest Neighbors (KNN) – Supervised Learning (Classification)
2. K-Means Clustering – Unsupervised Learning (Clustering)
3. Tic-Tac-Toe with Q-Learning – Reinforcement Learning (Game AI)

Each project is self-contained, with its own code, dataset, and results. All implementations are written in C for maximum transparency and educational value.

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🗂️ Project Structure

ML_in_C/
├── K_means_ML/           # K-Means clustering implementation
│   ├── iris.data         # Iris dataset
│   ├── k_means_main.c    # Main C source file
│   ├── value_of_assignments_and_clusters.csv # Output results
│   └── README.md         # Project-specific instructions
│
├── KNN_ML/               # K-Nearest Neighbors implementation
│   ├── iris.data         # Iris dataset
│   ├── knn_algorithm_main.c # Main C source file
│   ├── results.csv       # Output results
│   └── README.md         # Project-specific instructions
│
├── Tic-Tac-Toe_ML/       # Tic-Tac-Toe with Q-Learning
│   ├── tic_tac_toe_main.c # Main C source file
│   ├── q_table.csv       # Q-table for the agent
│   ├── a.exe             # Compiled binary (example)
│   └── README.md         # Project-specific instructions
│
└── README.md             # (You are here)

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1️⃣ K-Nearest Neighbors (KNN)

• Type: Supervised Learning (Classification)
• Dataset: Iris Dataset
• Goal: Classify iris flowers into three species based on sepal/petal measurements.
• Features:
  • Reads and normalizes the Iris dataset
  • Implements the KNN algorithm from scratch
  • Outputs predictions and accuracy to results.csv
• Learning Objectives:
  • Understand distance metrics and nearest neighbor search
  • Learn about data normalization and supervised learning

How to Run:

1. Navigate to KNN_ML/.
2. Compile: gcc knn_algorithm_main.c -o knn
3. Run: ./knn (or knn.exe on Windows)

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2️⃣ K-Means Clustering

• Type: Unsupervised Learning (Clustering)
• Dataset: Iris Dataset
• Goal: Cluster iris data into groups without using species labels.
• Features:
  • Random centroid initialization
  • Iterative assignment and centroid update
  • Outputs cluster assignments to value_of_assignments_and_clusters.csv
• Learning Objectives:
  • Understand unsupervised learning and clustering
  • Learn about centroid-based algorithms and convergence

How to Run:

1. Navigate to K_means_ML/.
2. Compile: gcc k_means_main.c -o kmeans
3. Run: ./kmeans (or kmeans.exe on Windows)

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3️⃣ Tic-Tac-Toe with Q-Learning

• Type: Reinforcement Learning (Game AI)
• Goal: Train an AI agent to play Tic-Tac-Toe using Q-Learning.
• Features:
  • Implements Q-Learning from scratch
  • Agent learns by playing games and updating a Q-table
  • Q-table can be saved/loaded (q_table.csv)
  • Play against the AI and watch it improve
• Learning Objectives:
  • Understand reinforcement learning concepts (states, actions, rewards)
  • Learn about Q-tables and epsilon-greedy policies

How to Run:

1. Navigate to Tic-Tac-Toe_ML/.
2. Compile: gcc tic_tac_toe_main.c -o ttt
3. Run: ./ttt (or ttt.exe on Windows)

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📦 Requirements

• GCC or any C99-compatible compiler
• No external libraries required (standard C only)
• (Optional) Python or spreadsheet software to view CSV results

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🧑‍💻 How to Contribute

Contributions are welcome! Feel free to open issues or submit pull requests for improvements, bug fixes, or new ML mini-projects in C.

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📜 License

This project is open-source and available under the MIT License.

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🙏 Credits

• UCI Machine Learning Repository – Iris Dataset
• Inspired by classic ML textbooks and C programming tutorials.

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🚀 Start Learning ML with C!

Explore each folder, read the code, and experiment with the algorithms. This project is a great way to demystify machine learning by building it from scratch in a low-level language.