Machine Learning Practice

📌 Overview

This repository consolidates various Machine Learning projects, covering key concepts such as Natural Language Processing (NLP), Regression Models, Support Vector Machines (SVM), Clustering, Anomaly Detection, and Ensemble Learning. The goal is to provide well-structured implementations, visualizations, and insights into different machine learning techniques.

📁 Repository Structure

Machine-Learning-Practice/
│── Regression-and-SVM-Models/    # Regression models & SVM implementations
│── Scratch/                      # ML algorithms implemented from scratch
│── ensemble-learning/            # Ensemble learning techniques
│── EDA/                          # Exploratory Data Analysis projects
│── clustering-and-anomaly-detection/ # Clustering & anomaly detection models
│── README.md                     # Project documentation (this file)

📚 Included Topics

✅ Regression Models: Linear Regression, Lasso, Ridge, ElasticNet, Logistic Regression

✅ Support Vector Machines (SVM): Classification and Regression

✅ Clustering & Anomaly Detection: K-Means, DBSCAN, Hierarchical Clustering, Isolation Forest

✅ Ensemble Learning: Random Forest, Gradient Boosting, AdaBoost, Stacking

✅ Exploratory Data Analysis (EDA): Data cleaning, visualization, feature engineering

✅ Scratch Implementations: Custom implementations of ML algorithms

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⚙️ Installation & Setup

To use this repository, follow these steps:

git clone https://github.com/ayushcodes13/Machine-Learning-Practice.git
cd Machine-Learning-Practice
pip install -r requirements.txt 

🚀 How to Run

Each project has its own Jupyter Notebook (.ipynb) or Python script (.py). You can run them using:

jupyter notebook  
python script.py  

📌 Contribution Guidelines

Contributions are welcome! If you want to add new ML projects or improve existing ones:

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

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