🤖 Machine Learning From Scratch

Welcome to Machine Learning From Scratch — a comprehensive, beginner-friendly, and professional repository containing classic Machine Learning algorithms implemented in two ways:

✅ From Scratch (Pure Python) — for complete understanding of the math and logic
✅ With Scikit-learn — for production-ready and fast implementation

📌 This project includes only Machine Learning algorithms (no deep learning), with clean code, real datasets, and Jupyter notebooks for practice.

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Machine Learning Algorithum

Link:Machine Learning From Scratch

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🎯 Project Mission

To help students, developers, and AI enthusiasts learn Machine Learning the right way — by implementing core algorithms manually and then applying the same models using Scikit-learn for practical use.

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🧠 Algorithms Included

Each folder includes:

• Python code (from scratch)
• Scikit-learn version
• Dataset(s)
• Jupyter notebook(s)

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🔹 Supervised Learning

📈 Regression

1. ✅ Simple Linear Regression
2. ✅ Polynomial Linear Regression
3. ✅ Multiple Linear Regression
4. ✅ Lasso Regression
5. ✅ Ridge Regression
6. ✅ Random Forest Regression
7. ✅ XGBoost Regressor (Scikit-learn version only)
8. ✅ Decision Tree Regressor
9. ✅ LightGBM Regressor
10. ✅ CatBoost Regression
11. ✅ ElasticNet Regression
12. ✅ Bayesian Ridge Regression
13. ✅ Gradient Boosting Regression
14. ✅ Support Vector Machine (SVM) Regression
15. ✅ K-Nearest Neighbors (KNN) Regression
16. ✅ Huber Regression
17. ✅ Orthogonal Matching Pursuit (OMP)
18. ✅ Theil-Sen Regression
19. ✅ Quantile Regression
20. ✅ Tweedie Regression
21. ✅ Principal Component Regression (PCR)
22. ✅ Gamma Regression
23. ✅ AdaBoost Regression
24. ✅ Stepwise Regression
    • i) Forward Stepwise
    • ii) Backward Stepwise

🔍 Classification

25. ✅ Logistic Regression
26. ✅ Random Forest Classification
27. ✅ Decision Tree Classification
28. ✅ LightGBM Classification
29. ✅ K-Nearest Neighbors Classification (KNN)
30. ✅ Support Vector Machine Classification (SVM)
31. ✅ Gaussian Naive Bayes Classification
32. ✅ Bernoulli Naive Bayes Classification
33. ✅ Multinomial Naive Bayes Classification
34. ✅ AdaBoost Classification
35. ✅ CatBoost Classification
36. ✅ XGBoost Classification
37. ✅ Gradient Boosting Classification
38. ✅ Extra Tree Classification
39. ✅ Linear Discriminant Analysis (LDA)

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🔹 Unsupervised Learning

• ✅ K-Means Clustering
• ✅ Hierarchical Clustering
• ✅ DBSCAN
• ✅ PCA (Principal Component Analysis)
• ✅ Apriori Algorithm (Association Rule Mining)
• ✅ Frequent Pattern Growth (FP-Growth)

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📘 Requirements

• Python 3.x
• NumPy, Pandas, Matplotlib, Seaborn
• Scikit-learn (for sklearn versions only)

Install dependencies:

pip install numpy pandas matplotlib seaborn scikit-learn

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💼 Use Cases

This repository is perfect for:

• Students learning ML theory and code
• Preparing for interviews and exams
• Building data science portfolios
• Training models with real datasets

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🙌 Author

Zohaib Sattar
📧 Email: zabizubi86@gmail.com 🔗 LinkedIn: Zohaib Sattar

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⭐️ Support the Project

If you find this project helpful, please ⭐️ star the repo and share it with your network. It motivates further open-source contributions!