Linear Regression

Folder Architecture:

• LinearRegression/LinearRegression.py -> This file contains our implementation of Linear Regression. You can import this file and use the class LinearRegression to perform Linear Regression on your data.
• ./test.ipynb -> This file contains the code to test our implementation of Linear Regression. This also compares our implementation with the implementation of Linear Regression in sklearn.

How to use:

• Import the file LinearRegression.py in your code.
• Create an object of the class LinearRegression.
• Run the fit method on the object to train the model.
• Use the predict method to predict the output for a given input.
• Use the score method to get the R^2 score of the model.

You can also adjust the learning rate and epochs for the model by passing them as arguments while creating the object of the class LinearRegression.

How to run the test file:

• Open the test.ipynb file in Jupyter Notebook.
• Make sure that sklearn is installed in your system. If not, install it using the command pip install sklearn.
• Run the cells in the file to test our implementation of Linear Regression.

Room for improvement:

• This is not a full implementation of Linear Regression. It only works for a single variable. We can extend this to work for multiple variables.
• We also need to implement different types of gradient descent algorithms like Stochastic Gradient Descent, Mini Batch Gradient Descent, etc.
• We could also implement different types of regularization like L1, L2, etc.
• We could also implement different types of loss functions like Mean Squared Error, Mean Absolute Error, etc.
• We could also implement different types of optimizers like Adam, RMSProp, etc.