- This project was a part of data mining course assignment at the University of Chicago.
Step 1: Clean and prepare your data: There are several entries where values have been deleted to simulate dirty data. Please clean the data with whatever method(s) you believe is best/most suitable. Note that some of the missing values are truly blank (unknown answers) and thus may be impossible to clean; use your discretion.
Step 2: Build your models: Please build machine learning/statistical models in Python to predict the interest rate assigned to a loan. When writing the code associated with each model, please have the first part produce and save the model, followed by a second part that loads and applies the model.
Step 3: Test your models using the data found within the "Holdout for Testing" file. Save the results of the final model (remember you will only predict the first column in holdout test set with your best model results) in a single, separate CSV titled "Results from" *insert your name or UChicago net ID.
Step 4: Submit your work: Please submit all of your code for cleaning, prepping, and modeling your data, your "Results" file, a brief write-up comparing the pros and cons of the modeling techniques you used (no more than a paragraph). Your work will be scored on techniques used (appropriateness and complexity), model performance - measured by RMSE - on the data hold out, an understanding of the techniques you compared in your write-up, and your overall code.
- Linear Regression was selected as the best model out of other applied models like Decision Tree Regressor and Random Forest Regressor.
Happy Coding!