Welcome to the Machine Learning Algorithms Code Snippets Repository! This repository aims to provide basic code snippets for some of the most widely used ML algorithms, including Supervised Learning, Unsupervised Learning, and Recommender Systems. Whether you are a beginner or an experienced data scientist, these code snippets will be a handy reference to get you started implementing various ML algorithms. - Supervised Learning - Unsupervised Learning - Recommender Systems

The repository covers the following categories of ML algorithms:

1. Linear Regression
2. Logistic Regression
3. Random Forests
4. XGboost

1. K-Means Clustering
2. Anomaly Detection with IsolationForest

1. Collaborative Filtering with Surprise Library

To use this repository and access the code snippets, you have two options:

1. Clone the repository:
2. Browse the repository directly on GitHub to view the code snippets online.
3. Dataset folder contains the dataset required for the Training and testing
4. Make sure the files in the Dataset folder and the .ipynb code file are in the same folder

I would greatly appreciate your contributions to this repository. If you want to add code snippets for more ML algorithms, improve existing code, fix bugs, or enhance the documentation, please follow the standard GitHub workflow:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with descriptive commit messages.
4. Push the changes to your fork.
5. Submit a pull request to the original repository.

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