• This repository contains Jupyter Notebook files that demonstrate various machine learning algorithms and techniques.

• These notebooks serve as a resource for learning and implementing different algorithms in the field of data science.

• Introduction
• Algorithms
• Usage
• Contributing
• License

• Data science is a rapidly growing field that encompasses various techniques and algorithms for extracting insights and making predictions from data.

• This repository aims to provide a comprehensive collection of Jupyter Notebook files that illustrate the implementation of different machine learning algorithms.

• Each notebook is designed to be self-contained and focuses on a specific algorithm.

• It includes step-by-step explanations and code examples to facilitate understanding and implementation.

The repository includes the following Machine Learning Notebooks :

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forest
5. Support Vector Machines (SVM)
6. K-Nearest Neighbors (KNN)
7. K-Means Clustering
8. K-Fold Cross Validation
9. Naive Bayes
10. Principal Component Analysis (PCA)
11. Hyper Perimeter Tuning
12. Bagging Ensemble
13. Saving Model to a File

Each Algorithm has its dedicated Jupyter Notebook that provides an overview, theoretical background, and practical implementation of the algorithm.

• To use the Notebooks in this Repository, you need to have Jupyter Notebook installed on your local machine.

• You can clone this repository using the following command:

git clone https://github.com/TheMrityunjayPathak/MachineLearning.git

• Once cloned, navigate to the specific Algorithm's Notebook and open it using Jupyter Notebook.

• Run the code cells sequentially to understand the algorithm's implementation and observe the outputs.

• Feel free to modify the Notebooks, experiment with different Datasets, and adapt the code to your specific use cases.

Contributions to this repository are Welcome! If you would like to add a new Algorithm or improve the existing notebooks, please follow these steps :

• Fork the Repository.

• Create a new branch for your Contribution.

• Make your changes and enhancements.

• Test the notebooks and ensure they run successfully.

• Submit a pull request explaining the changes you have made.

Your contributions will be reviewed, and upon approval, they will be merged into the main repository.

This repository is licensed under the MIT License. You are free to use, modify, and distribute the code in this repository for personal and commercial purposes.