NTCU Machine Learning Assignment Repository

NTCU-Machine-Learning repository for the Machine Learning course at NTCU. This repository is used for submitting assignments related to machine learning projects, with a focus on Credit Card Fraud Detection.

Project Overview

This assignment focuses on building a machine learning model for credit card fraud detection. You will use a dataset to train and evaluate models, applying techniques such as data preprocessing, feature scaling, and classification algorithms (e.g., Random Forest) or clustering (e.g., KMeans).

Objectives:

• Load and preprocess the dataset.
• Train a machine learning model to detect fraudulent transactions.
• Evaluate the model using metrics like accuracy, precision, recall, F1-score, ROC AUC, and confusion matrix.

Setup Instructions

To set up your environment and work on the assignment, follow these steps:

1. Fork the Repository

• Fork the NTCU-Machine-Learning repository to your GitHub account.
• Clone your forked repository to your local machine:

  git clone <your-forked-repo-url>

2. Install Git

Ensure Git is installed on your system:

• Windows/Mac: Download and install Git from git-scm.com.
• Ubuntu/Linux:

  sudo apt update
  sudo apt install git

Submission Guidelines

1. Fork and Clone: Fork this repository and clone it to your local machine.
2. Create a Branch: Create a branch for your assignment (e.g., assignment-<your-student-id>).
3. Implement Your Code: Modify the provided code template (see Code Structure) to complete the assignment.
4. Commit and Push:

   git add .
   git commit -m "Submit assignment for <your-student-id>"
   git push origin <your-branch-name>

5. Create a Pull Request: Submit a pull request from your forked repository to the main repository for review.
6. File Naming: Name your main script as <ex_number>.py.
7. Create a New Folder: Remeber put your each file into <your-student-id>_<ex_number>.

Important:

• Do not modify the TEST_SIZE (set to 0.3) or RANDOM_SEED (set to 42) in the code.
• Ensure your code is well-documented with comments explaining your approach.
• Submit your pull request before the deadline.

Dataset

The dataset for this assignment is available via KaggleHub. Use the following code to load it:

import kagglehub
path = kagglehub.dataset_download("mlg-ulb/creditcardfraud")
data = pd.read_csv(f"{path}/creditcard.csv")

• The dataset contains credit card transaction data with features like transaction amount, time, and anonymized features (V1 to V28).
• The target variable is Class (0 for non-fraudulent, 1 for fraudulent).

Tasks

1. Preprocess the data (e.g., handle missing values, scale features using StandardScaler).
2. Split the dataset into training (70%) and testing (30%) sets.
3. Train a classification model (e.g., RandomForestClassifier) or a clustering model (e.g., KMeans).
4. Evaluate the model using the following metrics:
   • Accuracy
   • Precision
   • Recall
   • F1-score
   • ROC AUC score
   • Confusion Matrix
   • (For clustering) Silhouette Score

Evaluation Metrics

Your model will be evaluated based on:

• Correctness: Does the code run without errors and produce the expected outputs?
• Performance: How well does your model perform on the test set (based on the metrics above)?
• Code Quality: Is the code well-organized, commented, and easy to understand?
• Documentation: Include a brief explanation of your approach in the pull request description.

Contact

For questions or issues, contact the teaching assistant via:

• Email: [bcs113116@gm.ntcu.edu.tw]
• Email: [bcs113115@gm.ntcu.edu.tw]

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