prislymj / credit-card-fraud-detection

Credit Card Fraud Detection using ML algorithms

Credit-card-fraud-detection

Credit Card Fraud Detection using ML algorithms

Source: https://www.geeksforgeeks.org/ml-credit-card-fraud-detection/

Credit Card Fraud Detection Project

Overview

This project aims to build a credit card fraud detection system using machine learning techniques. The objective is to identify fraudulent credit card transactions so that customers are not charged for items they did not purchase.

Challenges in Credit Card Fraud Detection

• Enormous Data: Large volumes of data need to be processed quickly to respond to scams in real-time.
• Imbalanced Data: The majority of transactions are not fraudulent, making it challenging to detect the fraudulent ones.
• Data Availability: The data is mostly private and sensitive, which poses challenges for data access and privacy protection.
• Misclassified Data: Not every fraudulent transaction is caught and reported, leading to misclassification.
• Adaptive Techniques: Scammers use adaptive techniques against the model to evade detection.

Approach

To tackle these challenges, the following approach is taken:

1. Use a simple and fast model that can detect anomalies quickly.
2. Implement techniques to handle data imbalance if necessary.
3. Protect user privacy by reducing data dimensionality.
4. Verify the data from a trustworthy source for model training.

Dataset

The dataset used in this project contains credit card transactions with labeled fraud cases and valid transactions. The dataset has 31 features, including transaction time, amount, and anonymized transaction details.

Code Description

The provided code demonstrates the process of credit card fraud detection using a Random Forest Classifier. It covers the following steps:

1. Loading and understanding the dataset.
2. Analyzing the data to identify imbalances between fraudulent and valid transactions.
3. Building a Random Forest Classifier model and evaluating its performance.
4. Visualizing the confusion matrix to assess the model's effectiveness.

Results

The Random Forest Classifier achieved the following evaluation metrics on the test set:

• Accuracy: 99.95%
• Precision: 98.67%
• Recall: 75.51%
• F1-Score: 85.55%
• Matthews correlation coefficient: 86.30%

Conclusion

The Random Forest Classifier shows promising results for credit card fraud detection. However, further analysis and fine-tuning may be required to address any specific challenges in the dataset. Additional techniques, such as data resampling or using alternative models, could be explored to improve the detection performance.

Please note that the code and analysis provided here are a basic implementation, and real-world applications may require more extensive testing and optimization to achieve optimal results.