CS 4320: Machine Learning

Assignment : Complex Linear Regression

In this assignment, you will use linear regression to fit a model to a collection of data. Your goal is to minimize the MSE on a set of test data. The data is more complex that the last assignment. You will need to use a data processing pipeline.

Use your personal data set available on Canvas in the regression-2 folder.

Explore and analyze this data as you did in previous assignments. Include the plots and analysis in your report.

Design and use a data processing pipeline that will scale the data into a better range, and will add derived data features.

Fit a linear regression model to the data. Note this means find the parameters.

It is expected that you will use the sklearn.linear_model.SGDRegressor to find the best model.

You will need to record the MSE found on the training data, and the MSE found on the testing data.

Required Steps

Download your data.
Explore and analyze your data.
Split the data 80%/20%, for training/testing.
Write (or modify) a Python program using sklearn to process and fit the training data to a SGDRegressor model.
Report the MSE loss obtained for your best model on the training data.
Report the MSE loss obtained for your best model on the testing data.
Report the linear model coefficients found.
Report your model function.
Include your analysis of the quality of your model’s fit to the data.
Commit and push your code in the git repository.
Submit the report (as PDF) to Canvas.

Last Updated 01/16/2023