implementation of linear and polynomial regression
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• Data Science Foundations
  • Table of Contents
  • About The Project
  • Getting Started
    • Prerequisites
    • Dependencies
    • Installation
  • License
  • Contact

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

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

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

List of dependencies present/used in the project

• numpy
• pandas
• matplotlib

1. Clone the repository

git clone https://github.com/RikilG/Data-Science-Foundations.git
cd Data-Science-Foundations

2. Install/update dependencies

# if using anaconda, replace pip with conda
pip install numpy
pip install pandas
pip install matplotlib

3. 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

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

RikilG: Github | Site

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