🧠 Machine Learning Algorithms

From Scratch & Scikit-Learn

A complete educational journey through classical Machine Learning — built from scratch, line by line.

Understand the math. Build the code. Train the mind.

⭐ Star this repo • 🍴 Fork it • 📚 Documentation • 🚀 Get Started

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📖 Table of Contents

• 🎯 Overview
• ✨ Features
• 🏗️ Tech Stack
• 📂 Repository Structure
• 🚀 Quick Start
• 🧮 Algorithms Implemented
• 📊 Dataset Collection
• 📸 Visualizations
• 🎓 Learning Path
• 🤝 Contributing
• 👨‍💻 Author
• 📄 License
• ⭐ Support

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🎯 Overview

Welcome to Machine Learning Algorithms — your comprehensive playground for mastering classical ML! This repository features hand-crafted implementations of every major algorithm, from the ground up.

🌟 Why This Repository?

• Learn by Building: Every algorithm implemented from scratch using pure NumPy
• Compare & Contrast: Side-by-side comparisons with industry-standard Scikit-Learn
• Visual Learning: Beautiful plots and visualizations that bring theory to life
• Real-World Applications: Applied examples on diverse datasets
• Educational Focus: Clear documentation, math explanations, and code comments

💡 Perfect for students, developers, data scientists, and AI enthusiasts who want to truly understand Machine Learning from first principles.

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✨ Features

🔬 From Scratch Implementations Pure Python & NumPy implementations Step-by-step algorithm breakdown Mathematical intuition explained No black boxes — see every calculation	📈 Production Comparisons Scikit-Learn implementations Performance benchmarking Hyperparameter tuning examples Best practices demonstrated
🎨 Beautiful Visualizations Decision boundaries Loss function curves Feature importance plots Clustering visualizations	📚 Rich Documentation Jupyter Notebooks with explanations Code comments and docstrings Theory behind each algorithm Use case examples

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🏗️ Tech Stack

Category	Technologies
Language
Core Libraries
ML Framework
Visualization
Environment
Version Control

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📂 Repository Structure

Machine-learning-Algorithm/
│
├── 📁 Supervised Learning
│   ├── 📂 Regression
│   │   ├── LinearRegression/          # Simple & Multiple Linear Regression
│   │   ├── PolynomialRegression/      # Polynomial Regression
│   │   └── GradientDescent/           # Batch, Mini-Batch, Stochastic GD
│   │
│   ├── 📂 Classification
│   │   ├── LogisticRegression/        # Binary & Multi-class Classification
│   │   ├── KNN/                       # K-Nearest Neighbors
│   │   ├── NaiveBayes/                # Gaussian, Multinomial, Bernoulli
│   │   ├── SupportVectorMachines/     # SVM with Kernel Tricks
│   │   ├── DecisionTrees/             # CART Algorithm
│   │   └── NeuralNetworks/            # Perceptron & MLP
│   │
│   └── 📂 Ensemble Methods
│       ├── RandomForest/              # Random Forest Classifier & Regressor
│       ├── Bagging/                   # Bootstrap Aggregating
│       ├── AdaBoost/                  # Adaptive Boosting
│       ├── GradientBoosting/          # Gradient Boosting Machines
│       └── XGBoost/                   # Extreme Gradient Boosting
│
├── 📁 Unsupervised Learning
│   ├── 📂 Clustering
│   │   ├── K-Means-clustering/        # K-Means from Scratch
│   │   ├── HierarchicalClustering/    # Agglomerative & Divisive
│   │   └── DBSCAN/                    # Density-Based Clustering
│   │
│   └── 📂 Dimensionality Reduction
│       └── PCA/                       # Principal Component Analysis
│
├── 📁 DataSets/                       # Curated Real-World Datasets
│   ├── iris.csv                       # Classification Dataset
│   ├── heart.csv                      # Healthcare Dataset
│   ├── Social_Network_Ads.csv         # Marketing Dataset
│   ├── ipl-matches.csv                # Sports Analytics
│   ├── zomato.csv                     # Restaurant Data
│   └── student_clustering.csv         # Educational Data
│
├── 📁 Visualizations/                 # Plots & Charts
├── 📄 requirements.txt                # Python Dependencies
├── 📄 CONTRIBUTING.md                 # Contribution Guidelines
├── 📄 LICENSE                         # MIT License
└── 📄 README.md                       # You are here!

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🚀 Quick Start

🔧 Prerequisites

• Python 3.9 or higher
• pip package manager
• Git

📥 Installation

1️⃣ Clone the Repository

git clone https://github.com/Nitin-Prata/Machine-learning-Algorithm.git
cd Machine-learning-Algorithm

2️⃣ Create Virtual Environment

Windows (PowerShell)

python -m venv .venv
.\.venv\Scripts\Activate.ps1

macOS / Linux

python3 -m venv .venv
source .venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Launch Jupyter Notebook

jupyter notebook

Your browser will open automatically at http://localhost:8888

🎯 First Steps

1. Navigate to any algorithm folder (e.g., LinearRegression/)
2. Open the Jupyter Notebook (.ipynb file)
3. Run cells sequentially to see the implementation
4. Experiment with parameters and datasets
5. Compare scratch implementation with Scikit-Learn

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🧮 Algorithms Implemented

📊 Regression Algorithms (5)

Algorithm	From Scratch	Scikit-Learn	Notebook
Linear Regression	✅	✅	📓
Multiple Linear Regression	✅	✅	📓
Polynomial Regression	✅	✅	📓
Ridge Regression	✅	✅	📓
Lasso Regression	✅	✅	📓

🎯 Classification Algorithms (8)

Algorithm	From Scratch	Scikit-Learn	Notebook
Logistic Regression	✅	✅	📓
K-Nearest Neighbors (KNN)	✅	✅	📓
Naive Bayes	✅	✅	📓
Support Vector Machine (SVM)	✅	✅	📓
Decision Trees	✅	✅	📓
Random Forest	✅	✅	📓
Neural Networks (MLP)	✅	✅	📓
Softmax Regression	✅	✅	📓

🌳 Ensemble Methods (5)

Algorithm	From Scratch	Scikit-Learn	Notebook
Random Forest	✅	✅	📓
Bagging	✅	✅	📓
AdaBoost	✅	✅	📓
Gradient Boosting	✅	✅	📓
XGBoost	✅	✅	📓

🔍 Clustering Algorithms (3)

Algorithm	From Scratch	Scikit-Learn	Notebook
K-Means Clustering	✅	✅	📓
Hierarchical Clustering	✅	✅	📓
DBSCAN	✅	✅	📓

📉 Dimensionality Reduction (1)

Algorithm	From Scratch	Scikit-Learn	Notebook
Principal Component Analysis (PCA)	✅	✅	📓

⚡ Optimization Algorithms (3)

Algorithm	From Scratch	Notebook
Batch Gradient Descent	✅	📓
Mini-Batch Gradient Descent	✅	📓
Stochastic Gradient Descent	✅	📓

📊 Algorithm Summary

Category	Count	Implementation Status
Regression	5	✅ Complete
Classification	8	✅ Complete
Ensemble Methods	5	✅ Complete
Clustering	3	✅ Complete
Dimensionality Reduction	1	✅ Complete
Optimization	3	✅ Complete
TOTAL	25	✅ Complete

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📊 Dataset Collection

All datasets are curated, cleaned, and ready to use in the /DataSets folder.

Dataset	Size	Features	Use Case	Domain
iris.csv	150	4	Multi-class Classification	Botany
heart.csv	303	13	Binary Classification	Healthcare
Social_Network_Ads.csv	400	4	Marketing Classification	Business
ipl-matches.csv	756	18	Regression & Analysis	Sports
zomato.csv	9551	21	Clustering	Food Industry
student_clustering.csv	2000	7	Clustering	Education

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📸 Visualizations

🎨 Sample Outputs

Decision boundaries, loss curves, clustering plots, and feature importance visualizations are included in each notebook.

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🎓 Learning Path

🔰 Beginner Track (Weeks 1-4)

1. Week 1-2: Linear & Logistic Regression

   • Start with LinearRegression/
   • Move to LogisticRegression/
   • Understand cost functions and gradient descent

2. Week 3: Classification Basics

   • Explore KNN/
   • Study NaiveBayes/
   • Practice on iris dataset

3. Week 4: Tree-Based Methods

   • Learn DecisionTrees/
   • Build intuition with visualizations

🚀 Intermediate Track (Weeks 5-8)

4. Week 5-6: Advanced Classification

   • Master SupportVectorMachines/
   • Understand kernel tricks
   • Implement NeuralNetworks/

5. Week 7: Ensemble Methods

   • Study RandomForest/
   • Compare with Bagging/
   • Understand bootstrap aggregating

6. Week 8: Clustering

   • Implement K-Means-clustering/
   • Explore HierarchicalClustering/
   • Try DBSCAN/

⚡ Advanced Track (Weeks 9-12)

7. Week 9-10: Boosting Algorithms

   • Deep dive into AdaBoost/
   • Master GradientBoosting/
   • Optimize with XGBoost/

8. Week 11: Dimensionality Reduction

   • Understand PCA/
   • Apply to high-dimensional data

9. Week 12: Optimization Techniques

   • Compare gradient descent variants
   • Implement custom optimizers
   • Hyperparameter tuning

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🎯 Key Learning Outcomes

After completing this repository, you will:

• ✅ Understand the mathematical foundations of ML algorithms
• ✅ Implement algorithms from scratch using NumPy
• ✅ Debug and optimize ML code effectively
• ✅ Compare custom implementations with Scikit-Learn
• ✅ Visualize model behavior and decision boundaries
• ✅ Apply algorithms to real-world datasets
• ✅ Choose the right algorithm for specific problems
• ✅ Tune hyperparameters for optimal performance

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🤝 Contributing

Contributions are always welcome! Here's how you can help:

🌟 Ways to Contribute

• 🐛 Report bugs and issues
• 💡 Suggest new algorithms to implement
• 📝 Improve documentation
• 🎨 Add visualizations
• 📊 Contribute new datasets
• ✨ Optimize existing code
• 🧪 Add unit tests

📋 Contribution Guidelines

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please read CONTRIBUTING.md for detailed guidelines.

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👨‍💻 Author

Nitin Pratap Singh

🎓 B.Tech in Computer Science (AI) | India 🇮🇳

💼 Machine Learning, AI Education & Open Source

"Learn the math. Build the code. Train the mind." 🧠

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2024 Nitin Pratap Singh

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files...

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⭐ Support This Project

If you find this repository helpful, please consider:

Action	Why?
⭐ Star this repository	Show appreciation & help others discover it
🍴 Fork it	Create your own version & experiment
👀 Watch	Get notified of updates
💬 Share	Help the ML community learn
🐛 Report Issues	Help improve the project
🤝 Contribute	Make it even better

🎯 Repository Stats

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🙏 Acknowledgments

Special thanks to:

• Andrew Ng for his legendary Machine Learning course that inspired this project
• CampusX for their exceptional ML tutorials and educational content
• Scikit-Learn team for the amazing library
• NumPy contributors for the numerical computing foundation
• The open-source community for inspiration
• You for taking the time to explore this repository!

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📚 Additional Resources

📖 Recommended Reading

• Pattern Recognition and Machine Learning by Christopher Bishop
• The Elements of Statistical Learning by Hastie, Tibshirani, Friedman
• Machine Learning Yearning by Andrew Ng

🎥 Video Courses

• Machine Learning by Stanford
• Fast.ai Practical Deep Learning
• StatQuest with Josh Starmer

🌐 Online Resources

• Scikit-Learn Documentation
• Kaggle Learn
• Machine Learning Mastery

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💭 Final Thoughts

Machine Learning is not just about using libraries — it's about understanding the principles that make those libraries work. This repository bridges the gap between theory and practice, empowering you to not just use ML, but to truly understand it.

🚀 Start Your ML Journey Today!

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Made with ❤️ and ☕ by Nitin Pratap Singh

Happy Learning! 🎓🚀