credible-clients

This repository contains a small pre-task for potential ML team members for UBC Launch Pad.

Overview

The dataset bundled in this repository contains information about credit card bill payments, courtesy of the UCI Machine Learning Repository. Your task is to train a model on this data to predict whether or not a customer will default on their next bill payment.

Most of the work should be done in model.py. It contains a barebones model class; your job is to implement the fit and predict methods, in whatever way you want (feel free to import any libraries you wish). You can look at main.py to see how these methods will be called. Don't worry about getting "good" results (this dataset is very tough to predict on) — treat this as an exploratory task!

To run this code, you'll need Python and three libraries: NumPy, SciPy, and scikit-learn. After invoking python main.py from your shell of choice, you should see the model accuracy printed: approximately 50% if you haven't changed anything, since the provided model predicts completely randomly.

Instructions

Here are the things you should do:

1. Fork this repo, so we can see your code!
2. Install the required libraries using pip install -r requirements.txt (if needed).
3. Ensure you see the model's accuracy/precision/recall scores printed when running python main.py.
4. Replace the placeholder code in model.py with your own model.
5. Fill in the "write-up" section below in your forked copy of the README.

Good luck, and have fun with this! 🚀

Write-up

I chose to do a KNN approach since I am just starting out with machine learning but am passionate to learn more. My value for K was 9, since that seemed to produce the best accuracy, precision and recall values. I could increase the accuracy to 78 at higher values of K, or using SVM Classification methodology, but I found this reduced the recall to an insignificant amount, hence I opted for the first approach in my final submission. I tried to play around with it using tensorflow, but have not had time to come to a complete solution.

Accuracy: 76.813 Precision: 40.244 Recall: 14.198

Data Format

X_train and X_test contain data of the following form:

Column(s)	Data
0	Amount of credit given, in dollars
1	Gender (1 = male, 2 = female)
2	Education (1 = graduate school; 2 = university; 3 = high school; 4 = others)
3	Marital status (1 = married; 2 = single; 3 = others)
4	Age, in years
5–10	History of past payments over 6 months (-1 = on-time; 1 = one month late; …)
11–16	Amount of previous bill over 6 months, in dollars
17–22	Amount of previous payment over 6 months, in dollars

y_train and y_test contain a 1 if the customer defaulted on their next payment, and a 0 otherwise.