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Contains basic implementation of Gradient Descent for Polynomial Regression

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RikilG/Data-Science-Foundations

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Data Science Foundations

Data Science Foundations

implementation of linear and polynomial regression
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About The Project

This project implements Polynomial regression using the method of Gradient Descent and Normal Equations for user given dataset and degree(use 1 for linear regression). For testing the model, this dataset is used

Built With Python

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

Things you need to use the project and how to install them:

  • python3
# skip this if you already have python > 3.4 installed
sudo apt install python python3

Dependencies

List of dependencies present/used in the project

Installation

  1. Clone the repository
git clone https://github.com/RikilG/Data-Science-Foundations.git
cd Data-Science-Foundations
  1. Install/update dependencies
# if using anaconda, replace pip with conda
pip install numpy
pip install pandas
pip install matplotlib
  1. Run the project
  • set the degree
  • set the method to
    • L1GD(Lasso Regression)
    • L2GD(Ridge Regression)
    • GD (Gradient Descent)
    • SGD (Stocastic Gradient Descent)
    • NE (Normal Equations)
  • Then run the project using:
python main.py

License

Distributed under the MIT License. See LICENSE for more information.

Contact

RikilG: Github | Site

Project Link: https://github.com/RikilG/Data-Science-Foundations

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Contains basic implementation of Gradient Descent for Polynomial Regression

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