ML_Algorithm_from_scratch

This repository contains implementations of fundamental Machine Learning algorithms from scratch using only Python and core libraries like NumPy , Pandas and Matplotlib.
The goal is to understand the inner workings of ML models by building them step-by-step without relying on high-level frameworks such as scikit-learn or TensorFlow.

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🚀 Implemented Algorithms

• Linear Regression

• Logistic Regression

• Decision Tree

• k-Nearest Neighbors (k-NN)

• Naive Bayes

• DBSCAN

• K Means

• Support Vector Machine

• Random Forest

(More algorithms can be added later!)

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📂 Project Structure

ML_Algorithm_from_scratch-main/

│── ML/

│ ├── Linear_regression/ # Implementation of Linear Regression

│ ├── Logistic_regression/ # Implementation of Logistic Regression

│ ├── KNN/ # Implementation of k-Nearest Neighbors

│ ├── Decision_Tree/ # Implementation of Decision Tree

│ ├── Random_Forest/ # Implementation of Random Forest

│ ├── K_Means/ # Implementation of K means clustering

│ ├── DBSCAN/ # Implementation of DBSCAN

│ ├── Naive_Bayes/ # Implementation of Naive Bayes

│ ├── SVM/ # Implementation of Support Vector Machine

│── README.md # Project documentation

yaml Copy code

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⚡ Installation

Clone the repo:

git clone https://github.com/yourusername/ML_Algorithm_from_scratch.git
cd ML_Algorithm_from_scratch

🧑‍💻 Usage

Run a specific algorithm example:

bash Copy code python ML/Linear_regression/linear_regression.py Or explore all algorithm folders individually.

🎯 Learning Objectives

Strengthen intuition behind machine learning algorithms.

Understand how training, optimization, and evaluation work internally.

Gain confidence to implement and debug ML models without black-box libraries.

🤝 Contributing

Contributions are welcome!

Fork the repo

Create a new branch (feature-new-algo)

Commit your changes

Submit a Pull Request 🎉

📜 License

This project is licensed under the MIT License.