Welcome to the Regression Algorithms Exploration repository! 🌐 This journey takes you through the practical aspects of Linear Regression using Gradient Descent, Stochastic Gradient Descent, and the robust Ridge Regression.
The Dataset2.csv harbors 10,000 data points in the format (R^100, R). Each row represents the dynamic interplay between features and their associated y values.
Experience the power of gradient descent in solving least squares problems. The plot will demonstrate the evolution of kwt - wMLk2 over time.
This script orchestrates stochastic gradient descent, creating a rhythmic dance in the plot of kwt - wMLk2.
Explore the implementation of the gradient descent algorithm for ridge regression. Cross-validation becomes an art, visually depicted in the images below.
- Clone the Repository:
git clone https://github.com/snigdhab7/Regression-Algorithms-Exploration.git cd Regression-Algorithms-Exploration
To explore the world of regression algorithms, run the following scripts and witness the magic unfold!
python LinearRegressionGradientDescent.pypython StochasticGradientDescentLinearRegression.pypython RidgeRegressionImplementation.pyMake sure to replace <Link to the Dataset2.csv> with the actual link to your dataset.
Explore the code, visualize the results, and join the journey of decoding the mysteries within the realm of regression. Let's uncover patterns and insights together!
If you encounter any issues, have suggestions, or want to share your results, feel free to open an issue or pull request. Happy exploring! 🌟
Feel free to use or modify it according to your needs!