Credit Card Fraud Detection Project

Overview

This project focuses on building a machine learning model for detecting fraudulent activities in credit card transactions.

Table of Contents

1. Introduction
2. Project Structure
3. Data Collection
4. Data Preprocessing
5. Exploratory Data Analysis (EDA)
6. Feature Engineering
7. Additional Visualizations
8. Results
9. Next Steps

Introduction

This project aims to develop a machine learning model for detecting fraudulent activities in credit card transactions. The model is trained on the Credit Card Fraud Detection dataset from Kaggle.

Project Structure

The project is structured as follows:

• notebooks/: Jupyter notebooks for different stages of the project.
• src/: Python scripts for reusable code.

Data Collection

• Dataset: Credit Card Fraud Detection
• Source: Kaggle

Data Preprocessing

• Handled missing values.
• Normalized numerical features.
• No encoding for categorical variables in this dataset.

Exploratory Data Analysis (EDA)

• Explored feature distributions, correlations, and outliers.

Feature Engineering

• Created a new feature, 'Hour,' representing the hour of the day for each transaction.

Additional Visualizations

• Learning Curves.
• SHAP Values or Feature Importance Plot.
• Testing the Model - Sample Input and Predicted Output Comparison.

Results

• Precision: 0.97
• Recall: 0.79
• F1-score: 0.87
• Accuracy: 0.80

Next Steps

• Fine-tune the model further.
• Implement continuous monitoring.
• Evaluate the model on new, unseen data.
• Enhance model explainability with additional visualizations.