🤖 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.

---

Machine Learning Algorithum

Link:Machine Learning From Scratch

---

🎯 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.

---

🧠 Algorithms Included

Each folder includes:

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

---

🔹 Supervised Learning

📈 Regression

• ✅ Simple Linear Regression
• ✅ Polynomial Linear Regression
• ✅ Multiple Linear Regression
• ✅ Lasso Regression
• ✅ Ridge Regression
• ✅ Random Forest Regression
• ✅ XGBoost Regressor (Scikit-learn version only)
• ✅ Decision Tree Regressor
• ✅ LightGBMRegressor
• ✅ CastboostRegression
• ✅ ElasticNet Regression
• ✅ BayesianRidge Regression
• ✅ Gradient Boosting Regression
• ✅ Support Vector Machine (SVM) Regression
• ✅ K-Nearest Neighbors (KNN) Regression
• ✅ Huber Regression
• ✅ Orthogonal Matching Pursuit (OMP)

🔍 Classification

• ✅ Logistic Regression
• ✅ Bernoulli Naive Bayes
• ✅ Multinomial Naive Bayes
• ✅ Naive Bayes Classification
• ✅ Decision Tree Classification
• ✅ K-Nearest Neighbors (KNN)
• ✅ Support Vector Machine (SVM)
• ✅ Random Forest Classifier (Scikit-learn only)

---

🔹 Unsupervised Learning

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

---

---

📘 Requirements

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

Install dependencies:

pip install numpy pandas matplotlib seaborn scikit-learn

---

💼 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

---

🙌 Author

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

---

⭐️ 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!