Machine Learning Algorithms 🤖

Welcome to the Machine Learning Algorithms repository! This project showcases various machine learning algorithms implemented from scratch. It is a comprehensive guide for anyone looking to understand the inner workings of different ML algorithms 📊

Table of Contents

• Introduction
• Algorithms Implemented
• Prerequisites
• Usage
• Contributing
• Contact

Introduction 📘

Machine learning is a powerful tool that enables systems to learn and make decisions without explicit programming. This repository aims to demystify the concepts behind machine learning by providing clear and concise implementations of various algorithms.

Algorithms Implemented 📚

Here's a list of the machine learning algorithms you will find in this repository:

• Linear Regression 📈
• Logistic Regression 📉
• Support Vector Machines (SVM) 🛡️
• K-Nearest Neighbors (KNN) 👥
• Decision Trees Classifier & Regressor🌳
• Random Forests - Bagging Algorithm 🌲🌲
• Adaboost - Boosting Algorithm 📚
• K-Means Clustering 🔶

Prerequisites 🛠️

• Python 3.x
• Jupyter Notebook
• Required libraries: numpy, pandas, scikit-learn, matplotlib, seaborn

Usage 🚀

Each algorithm comes with its own script and a corresponding Jupyter notebook to demonstrate its usage. For example, to run the Linear Regression algorithm:

• Open the Jupyter notebook: jupyter notebook linear_regression.ipynb

Feel free to explore and modify the scripts and notebooks to understand each algorithm better

Contributing 🤝

Contributions are welcome! If you have any improvements or new algorithms to add, please follow these steps:

Contact 📬

If you have any questions or suggestions, feel free to reach out:

Garvit Jain: garvitjain-02

Happy Coding! 😊