FITE7410 Financial Fraud Analytics

DEPARTMENT OF COMPUTER SCIENCE

FITE7410 Financial Fraud Analytics

First Semester, 2024-2025

Assignment 1 – Exploratory Data Analysis (EDA)

(Due Date: 11 Oct, 2024 (Fri) 23:59)

Assessment Criteria:

· Plagiarism: Please follow the guidelines laid down by our department.

· You are allowed to discuss the assignment with your classmates, however, you should submit your individual work.  Any direct copy and paste is PROHIBITED and would be considered as PLAGIARISM.

· Assignments would be marked based on the logic, presentation and understanding of the problem; not only on accuracy.

· LATE PENALTIES: 50% of assignment marks will be deducted for late submissions.  0 marks if the submission is later than 2 weeks.

Objectives of this assignment:

· Perform. data cleaning and preparation.

· Explore and visualize the data to identify patterns and trends.

· Engineer new features based on domain knowledge or insights from EDA.

· Prepare a report summarizing the findings from EDA.

Instructions of this assignment:

1. (50%) Exploratory Data Analysis

a. Use the provided dataset for the mini-case study.

Download the dataset (A1_data.csv) from Moodle, which is a modified version of IEEE-CIS Fraud Dataset. 

b. Using the R package, conduct exploratory analysis of the dataset downloaded.

· Identify and handle missing values, outliers, and inconsistencies, if applicable.

· Explore the distribution of features (e.g. univariant, bi-/multi-variant analysis) using histograms, box plots, scatter plots, correlation plots, etc.

· Create new features that may be relevant for fraud detection.

NOTE: A sample R script. is provided, but you still need to complete the program. Or you can build the model by yourselves and use whatever library you like.

2. (50%) Write a short report on the following:

a. Describe the dataset based on the EDA result, including:

·    A description of the data cleaning and preparation process.

·    Visualizations of the data, with clear labels and explanations.

·    A discussion of the key findings from EDA, including insights and potential hypotheses.

·    A description of the engineered features and their rationale.

NOTE: The short report should consist of a main body of maximum 2-3 pages, focusing on your analysis and insights. Additional figures and diagrams can be included in a separate Appendix to support your report.

3. Submission on Moodle:

a. R language script.

b. A pdf version report

NOTE: Report submissions will be checked for similarity using Turnitin.