aadi1011/Supervised-Machine-Learning-Modelling

A repository with the complete overview of Supervised Machine Learning and the implementation of various models based on multiple datasets.

Supervised Machine Learning Modelling

This repository provides a comprehensive overview of Supervised Machine Learning and implements various models based on multiple datasets. Additionally, the models have been deployed on Streamlit.

Streamlit Deployment

You can access the deployed models using the following link:

Streamlit Deployment

Note: Incase the app is down due to streamlit inactivity, please click on the button to restart the app

Machine Learning Models

The repository includes the implementation of the following machine learning models:

• Simple Linear Regression
• Multiple Linear Regression
• Support Vector Machine
  • Support Vector Regression
  • Support Vector Classifier
• K-Nearest Neighbours
  • KNN Classifier
  • KNN Regressor
• Random Forest
• Decision Tree

Datasets Used

The repository uses multiple datasets, which can be found in the following directory:

Datasets Directory

Usage

To explore the deployed models, visit the following site:

Streamlit Deployment

Here are a couple of screenshots from the deployed application:

Contributing

We welcome contributions to this repository! To contribute, follow these steps:

1. Fork the repository by clicking on the "Fork" button on the top right corner of this page.

2. Clone the forked repository to your local machine using the following command in your terminal or command prompt:

   git clone https://github.com/your-username/Supervised-Machine-Learning-Modelling.git
   

3. Create a new branch for your changes:

   cd Supervised-Machine-Learning-Modelling
   git checkout -b feature/your-feature-name
   

4. Make your desired changes to the code, datasets, or documentation.

5. Commit your changes with a descriptive commit message:

   git add .
   git commit -m "Add your commit message here"
   

6. Push your changes to your forked repository:

    git push origin feature/your-feature-name
   

7. Finally, open a pull request from your forked repository to the original repository. Provide a clear description of your changes and submit the pull request.

For more information, visit the CONTRIBUTIONS file.

Hacktoberfest 2023

We are participating in the Hacktoberfest 2023. You may fork this repository, and make your submissions.

Notebooks/Model Reference

Any reference to as how the model was built with the parameters and tuning can be viewed in the notebooks folder. I wanted to make project completely open source and free as a good education platform for everyone to get a hands-on demo on how supervised machine learning models are created and easily deployed with streamlit

---

We appreciate your contributions and will review your pull request as soon as possible.

If you use this repository for your personal work, we kindly ask for a small acknowledgement in your comments!

Thank you for your interest in contributing to this project!