CSCI 479 -- Machine Learning
Fall 2026 - Assignment 1
Submit deadline: 12:30pm, 2 October 2026, Friday

Objectives:

• Practice data exploration and pre-processing
• Practice documenting data exploration and pre-processing

References:

• Lab 1
• Lab 2
• Introduction
• Data

Problem Description:

The dataset stored in GermanCredit_Dataset.xlsx contains a set of customers described by a set of attributes, and is generally used to classify these customers as good or bad credit risks.

The data in the provided Excel file are downloaded from OpenML website, and in the original format provided by Prof. Hofmann. The dataset contains categorical and numerical attributes, and even comes with a cost matrix (although it's irrelevant for this assignment's purposes).

In the "Meta Data" worksheet, there is some superficial information regarding the actual data, which is stored in the "Data" worksheet.

Your Tasks:

Use an available and suitable tool to explore the provided German Credit Dataset, and write a report to document your exploration process, exploration result, and propose a discretization method and result of a certain continuous attribute (age).

Your exploration result should include at least the following information for each attribute in the dataset:

• your understanding of the semantics of this attribute when viewing the simple explanation of this attribute in the "Meta Data" worksheet and the exploration of the actual data in the "Data" worksheet;
• the data item count, whether there is any missing value in this attribute and the percentage of the missing values if there is any;
• if the attribute is a categorical one, show the cardinality (number of the unique values) of this attribute, the actual values and the number of times each value appears in the dataset;
• if the attribute is a continuous one, show its range, min and max value, average value, and the top three values that appears the most in the dataset and their corresponding frequency.

For your exploration process, your document must provide details so that your exploration process can be easily replicated by others to generate the exploration result as shown in your report. For example,

• If you used any machine learing platform to explore the data, explain clearly the tools you used and the steps your performed.
• If you used any database management system to explore the data, describe the database management system and any query statements used.
• If you used your own program to explore the data, the source code file of your program, which should be properly commented, should be included in your submission. And your report should include the instructions about the programming languaged used in your program, how to execute your program on the lab machines, a general description about your program and how to interpret the output of your program if its output has different format from the exploration result shown in your document.

Viewing the data in the provided dataset, the attribute "age" is clearly a continuous and numerical one. Many machine learning algorithms can't handle such attributes. Propose a method to discretize this attribute, show the exploration result of this attribute after it's been discretized, and justify your discretization method using your understanding of the data in this particular dataset and/or any other general knowledge you have about age.

How to submit:

To submit your assignment, login to your VIU Learn account, find the CSCI 479 course page, click on the "Assessment" drop-down menu, click on the "Assignments" item, then click on the folder named "A1". Then you can click on the "Add a File" button to browse and upload your report and any other associated files.

Your report file should be in one of the following formats: pdf, docx, md, and txt, preferably pdf. If you need to submit your own program, only the source code files should be included.

If you have any problem with submitting on the VIU Learn, please email me to let me know and attach your solution files with your email.