Regression-Algorithms-Implementation

Overview

This project implements various regression algorithms to provide a comprehensive understanding of regression techniques.

The following algorithms are covered:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression

Each algorithm is implemented in separate Jupyter notebooks, and corresponding datasets are provided for experimentation and learning.

Project Structure
/Regression-Algorithms-Implementation
    /datasets
        multiple_linear_regression.csv
        polynomial_regression.csv
        salary.csv
    Simple_Linear_Regression.ipynb
    Multiple_Linear_Regression.ipynb
    Polynomial_Regression.ipynb
    SVR.ipynb
    Decision_Tree_Regression.ipynb
    Random_Forest_Regression.ipynb
    README.md

To run the notebooks, you'll need the following Python packages:

Requirements

To run the notebooks, you'll need the following Python packages:

• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn

You can install the required packages using pip:

pip install numpy pandas matplotlib seaborn scikit-learn

How to Run

Clone this repository:

git clone https://github.com/yugeshsivakumar/Regression-Algorithms-Implementation.git

Navigate to the project directory:

cd Regression-Algorithms-Implementation

Contributing

If you'd like to contribute to this project, please fork the repository and create a pull request with your proposed changes.

License

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