Machine Learning Algorithms

This repository contains implementations of popular Machine Learning algorithms using Python and Jupyter Notebook. These notebooks are useful for students, beginners, and anyone who wants to understand the basic concepts of Machine Learning through practical examples.

📚 Algorithms Included

• ✅ Linear Regression
• ✅ Logistic Regression
• ✅ Decision Tree
• ✅ Random Forest
• ✅ Support Vector Machine (SVM)
• ✅ AdaBoost Classifier
• ✅ Multilayer Perceptron (MLP) Classifier
• ✅ K-Means Clustering
• ✅ K-Modes Clustering
• ✅ Hierarchical Clustering

🛠️ Technologies Used

• Python 3
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Scikit-learn

📁 Repository Structure

Machine-Learning-Algorithms/
│── AdaBoost Classifier.ipynb
│── Decision Tree.ipynb
│── Hierarchical Clustering.ipynb
│── K-Means Clustering.ipynb
│── K-Mode Clustering.ipynb
│── Linear Regression.ipynb
│── Logistic Regression.ipynb
│── Multilayer Perception Classifier.ipynb
│── Random Forest.ipynb
│── SVM.ipynb
│── README.md

🚀 How to Run

1. Clone the repository

git clone https://github.com/Tuhin092005/Machine-Learning-Algorithms.git

2. Open the project folder

cd Machine-Learning-Algorithms

3. Install the required libraries

pip install numpy pandas matplotlib scikit-learn

4. Launch Jupyter Notebook

jupyter notebook

5. Open any notebook and run the cells.

🎯 Features

• Beginner-friendly implementations
• Well-structured Jupyter notebooks
• Uses Scikit-learn library
• Easy to understand and modify
• Covers both Supervised and Unsupervised Learning algorithms

📖 Learning Objectives

This repository helps you learn:

• Data preprocessing
• Model training
• Model prediction
• Model evaluation
• Classification
• Regression
• Clustering

🤝 Contributions

Contributions are welcome. Feel free to fork this repository, improve the notebooks, and submit a pull request.

⭐ Support

If you found this repository useful, please consider giving it a ⭐ on GitHub.

👨‍💻 Author

Tuhin Maji

GitHub: https://github.com/Tuhin092005