Machine Learning -- Practical Implementations

A complete, hands-on repository of Machine Learning algorithms implemented from scratch and with libraries such as NumPy, Pandas, and Scikit-learn.
Each chapter includes explanations, step-by-step logic, and real-world datasets to help you learn ML by doing.

🚀 Features

• Fundamentals of Machine Learning implemented in Python\
• Beginner-friendly, readable, fully commented code\
• Real datasets for practical learning\
• Covers both theory and implementation\
• Easy to extend and contribute

📂 Repository Structure

Machine-Learning/
│
├── kNN/
│   ├── simple_knn.py
│   ├── dating_knn.py
│   └── digit_recognition_knn.py
│
├── Decision Tree/
│   ├── decision_tree.py
│   └── sklearn_decision_tree.py
│
├── Naive Bayes/
│   ├── bayes.py
│   ├── bayes-modify.py
│   └── nbc.py
│
├── Logistic/
│   ├── LogRegres.py
│   ├── LogRegres-gj.py
│   └── colicLogRegres.py
│
├── SVM/
│   ├── svm-simple.py
│   ├── svm-smo.py
│   ├── svmMLiA.py
│   └── svm-svc.py
│
├── AdaBoost/
│   ├── adaboost.py
│   ├── horse_adaboost.py
│   ├── sklearn_adaboost.py
│   └── ROC.py
│
├── Regression/
│   ├── regression_old.py
│   ├── abalone.py
│   ├── regression.py
│   └── lego.py
│
└── Regression Trees/
    └── regTrees.py

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🔧 Installation

git clone https://github.com/yourusername/Machine-Learning.git
cd Machine-Learning
pip install -r requirements.txt

▶️ How to Use

Run any algorithm directly:

python kNN/simple_knn.py
python Logistic/LogRegres.py
python SVM/svm-svc.py

📘 Chapters Overview

Chapter 2: K-Nearest Neighbors (KNN)

• Simple KNN classifier\
• Dating dataset classification\
• Digit recognition

Chapter 3: Decision Trees

• Loan prediction\
• Contact lenses classification

Chapter 4: Naive Bayes

• Comment filtering\
• Spam filtering\
• News classification

Chapter 5: Logistic Regression

• Logistic regression from scratch\
• Improved random gradient ascent\
• Horse mortality prediction

Chapter 6: Support Vector Machines (SVM)

• SMO algorithms\
• Nonlinear SVM\
• Scikit-learn SVC

Chapter 7: AdaBoost

• Decision stump AdaBoost\
• Hard dataset AdaBoost\
• ROC plotting

Chapter 8: Linear Regression

• Ordinary & Locally Weighted LR\
• Abalone age prediction\
• Stepwise regression\
• Lego price prediction

Chapter 9: Regression Trees

• CART regression tree\
• Tree pruning

🤝 Contributing

1. Fork the repository\
2. Create a new branch\
3. Commit your changes\
4. Submit a pull request

📄 License

MIT License

⭐ Support

If you find this useful, please consider starring ⭐ the repository!