Qiqi93/Data-Analysis

The study projects of data analysis

This project involves the school project in data analysis

Fraud detection

Fraud Detection Project using XGBoost

As part of a dynamic project focused on enhancing transaction security, I successfully implemented a fraud detection model utilizing XGBoost. The project aimed to identify and prevent fraudulent transactions, contributing to a robust and secure financial environment.

Key Components:

1. Model Selection:

   • Implemented XGBoost as the primary machine learning model for fraud detection.
   • Leveraged its ensemble learning capabilities for improved accuracy.

2. Performance Evaluation:

   • Assessed model performance using key indicators:
     • F1 Score
     • Precision
     • Recall
   • Provided a comprehensive analysis of the model's effectiveness in identifying fraudulent activities.

3. Explanatory Analysis:

   • Employed SHAP (SHapley Additive exPlanations) analysis to interpret the model's decision-making process.
   • Investigated and visualized the relationships between variables to enhance interpretability.

Achievements:

• Achieved notable results in minimizing false positives and false negatives, optimizing fraud detection accuracy.
• Contributed to a more secure financial system through the successful implementation of advanced machine learning techniques.

Technologies Used:

• XGBoost for machine learning model implementation.
• F1, Precision, and Recall as evaluation metrics.
• SHAP analysis for interpretability.

Outcome:

• The project resulted in an enhanced fraud detection system, improving the overall security and reliability of financial transactions.