https://github.com/chenna7893/optimizing-credit-card-fraud-detection-using-ensemble-methods
The project demonstrates the effectiveness of combining AdaBoost and Majority Voting for credit card fraud detection, providing a reliable and accurate solution to combat fraudulent activities in financial transactions.
https://github.com/chenna7893/optimizing-credit-card-fraud-detection-using-ensemble-methods
adaboost-algorithm cp-abe knn-algorithm majority-voting sha256
Last synced: 4 months ago
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The project demonstrates the effectiveness of combining AdaBoost and Majority Voting for credit card fraud detection, providing a reliable and accurate solution to combat fraudulent activities in financial transactions.
- Host: GitHub
- URL: https://github.com/chenna7893/optimizing-credit-card-fraud-detection-using-ensemble-methods
- Owner: Chenna7893
- Created: 2024-11-25T03:17:27.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-01-15T16:47:10.000Z (5 months ago)
- Last Synced: 2025-01-15T18:35:27.632Z (5 months ago)
- Topics: adaboost-algorithm, cp-abe, knn-algorithm, majority-voting, sha256
- Homepage:
- Size: 9.13 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Credit Card Fraud Detection using Ensemble Methods (AdaBoost and Majority Voting)
## Overview
This project aims to detect credit card fraud using ensemble learning methods, specifically **AdaBoost** and **Majority Voting**. The dataset used contains transaction details, including features such as transaction amount, merchant, and customer information. The goal is to classify transactions as either legitimate or fraudulent.
### Key Features:
- **Ensemble Methods**: Utilizes AdaBoost and Majority Voting to improve model performance.
- **Data Preprocessing**: Handles imbalanced data and applies techniques like SMOTE for oversampling.
- **Evaluation**: Performance metrics such as accuracy, precision, recall, and F1 score are computed.
- **Visualization**: Interactive graphs and plots to better understand the performance and model decision-making process.
---
## Technologies Used
- Python
- Scikit-learn
- Pandas
- NumPy
- Matplotlib & Seaborn
- imbalanced-learn (for oversampling techniques like SMOTE)
---
## Dataset
The dataset used is a simulated credit card transaction dataset, containing features that represent transaction details. The target variable indicates whether the transaction was fraudulent (1) or legitimate (0).
> **Note:** For the sake of this project, we have used a publicly available dataset, such as the [Kaggle Credit Card Fraud Detection Dataset](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud).
---
## Installation
Example Outputs
Model Performance
Here’s an example of what the evaluation output may look like:
Accuracy: 98.5%
Precision: 92.7%
Recall: 91.2%
F1-Score: 91.9%
### Clone the Repository
```bash
git clone https://github.com/yourusername/credit-card-fraud-detection.git
cd credit-card-fraud-detection