This project implements a simple linear regression model using gradient descent optimization. It demonstrates the process of creating a dataset, implementing a linear model, defining a cost function, and optimizing the model parameters using gradient descent.
- Generate synthetic regression data
- Implement a linear regression model
- Define and calculate cost function
- Implement gradient descent optimization
- Visualize data and model predictions
- Python 3.x
- NumPy
- Matplotlib
- Scikit-learn
- Run the script to generate a synthetic dataset.
- The script will train a linear regression model using gradient descent.
- Visualizations of the data and model predictions will be displayed.
linear_regression_gd.py: Main Python script containing the implementationREADME.md: This file, containing project information
- Add command-line arguments for hyperparameters
- Implement additional regression algorithms for comparison
- Create a Jupyter notebook with step-by-step explanations
Contributions, issues, and feature requests are welcome. Feel free to check [issues page] if you want to contribute.