https://github.com/xgagandeep/linear-regression-from-scratch-with-gradient-descent

This project demonstrates the implementation of linear regression using gradient descent.
https://github.com/xgagandeep/linear-regression-from-scratch-with-gradient-descent

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This project demonstrates the implementation of linear regression using gradient descent.

• Host: GitHub
• URL: https://github.com/xgagandeep/linear-regression-from-scratch-with-gradient-descent
• Owner: xgagandeep
• Created: 2024-09-11T16:51:42.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-11T17:08:46.000Z (2 months ago)
• Last Synced: 2024-09-12T02:30:32.146Z (2 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 238 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Project: Linear Regression with Gradient Descent

**Date:** 2020  

**Language:** Python  

**Libraries:** NumPy, Pandas, Matplotlib  

**Type:** Linear Regression Implementation

## Description

This project demonstrates the implementation of linear regression using gradient descent. The goal is to fit a linear model to the training data and make predictions on test data. The code covers data normalization, hypothesis computation, error calculation, gradient computation, and visualization of the results.

## Features

- **Data Loading:** Reads training and test datasets from CSV files.

- **Data Normalization:** Normalizes feature values to standardize the range.

- **Linear Regression:** Implements hypothesis function, error function, and gradient descent for model training.

- **Visualization:** Plots data points, regression lines, and error convergence.

- **Prediction:** Makes predictions on test data and saves the results to a CSV file.

- **Interactive Visualization:** Displays interactive plots of the regression process.

## Files

- `Linear_X_Train.csv`: Training features dataset.

- `Linear_Y_Train.csv`: Training target values dataset.

- `Linear_X_Test.csv`: Test features dataset.

- `output.csv`: Output file with predictions for the test dataset.

## Installation

To run this project, you need Python and the required libraries installed. Follow these steps:

1. **Clone the repository:**

   ```bash

   git clone https://github.com/xgagandeep/Linear-Regression-from-scratch-with-gradient-descent.git

   ```

2. **Navigate to the project directory:**

   ```bash

   cd Linear Regression from scratch with gradient descent

   ```

3. **Install the required libraries:**

   ```bash

   pip install numpy pandas matplotlib

   ```

4. **Prepare Data Files:**

   Make sure to place `Linear_X_Train.csv`, `Linear_Y_Train.csv`, and `Linear_X_Test.csv` in the appropriate directories as specified in the script.

5. **Run the Script:**

   ```bash

   jupyter notebook Linear Regression Gradient Descent.ipynb

   ```

## Usage

1. **Load Data:** The script loads training and test data from CSV files.

2. **Normalize Data:** The feature values are normalized to improve model performance.

3. **Train Model:** Linear regression parameters are learned using gradient descent.

4. **Visualize Results:** The script generates plots of the training data, regression line, and error convergence.

5. **Make Predictions:** Predictions are made on the test data and saved to `output.csv`.

## Functions

- `hypothesis(x, theta)`: Computes the hypothesis function for given inputs.

- `error(X, Y, theta)`: Calculates the mean squared error for the given parameters.

- `gradient(X, Y, theta)`: Computes the gradient of the error function.

- `gradientDescent(X, Y, max_iteration, learning_rate)`: Performs gradient descent to optimize the parameters.

- `r2score()`: Calculates the R^2 score to evaluate model performance.

## Contribution

Feel free to contribute to this project by submitting issues or pull requests. For any questions or feedback, please open an issue on the GitHub repository.

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