https://github.com/jaypanchal9/fraud-detection-case-study
A comprehensive case study applying machine learning techniques to detect fraudulent transactions effectively.
https://github.com/jaypanchal9/fraud-detection-case-study
machine-learning matplotlib numpy pandas python3 scikit-learn seaborn xgboost
Last synced: 11 months ago
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A comprehensive case study applying machine learning techniques to detect fraudulent transactions effectively.
- Host: GitHub
- URL: https://github.com/jaypanchal9/fraud-detection-case-study
- Owner: jaypanchal9
- License: gpl-3.0
- Created: 2025-03-07T16:55:04.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-03-07T17:03:19.000Z (11 months ago)
- Last Synced: 2025-03-07T18:19:36.462Z (11 months ago)
- Topics: machine-learning, matplotlib, numpy, pandas, python3, scikit-learn, seaborn, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 259 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fraud Detection Case Study
## Overview
This project focuses on building a robust model for detecting fraudulent transactions using machine learning techniques. The implementation leverages exploratory data analysis (EDA), feature engineering, and various predictive modeling techniques to identify and mitigate financial fraud.
## Objectives
- Perform exploratory data analysis to understand the characteristics of fraudulent transactions.
- Apply data preprocessing techniques to clean and prepare the dataset for modeling.
- Engineer meaningful features to improve predictive performance.
- Train and evaluate multiple machine learning models to detect fraud effectively.
- Select the best-performing model based on evaluation metrics.
## Dataset
The dataset used contains historical transaction data labeled as fraudulent or legitimate. Key attributes include transaction amount, time, user details, and transaction type.
## Methodology
### 1. Data Exploration
- Understanding data distribution.
- Identifying patterns and anomalies indicative of fraud.
### 2. Data Preprocessing
- Handling missing values.
- Dealing with class imbalance using techniques such as undersampling or oversampling.
### 3. Feature Engineering
- Creating derived features that enhance the ability to detect fraud.
- Scaling and normalizing data for improved model performance.
### 4. Modeling
- Implementing machine learning models such as Logistic Regression, Random Forest, and XGBoost.
- Evaluating models using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.
### 5. Model Selection and Evaluation
- Comparing performance across different models.
- Selecting the best-performing model based on metrics suited for imbalanced datasets.
## Usage
- Clone this repository.
- Install necessary libraries using `pip install -r requirements.txt`.
- Run the Jupyter notebook `Fraud_Detection_Case_Study_Code.ipynb` to reproduce the analysis.
## Dependencies
- Python 3.x
- Pandas
- NumPy
- Scikit-learn
- XGBoost
- Matplotlib
- Seaborn
## Results
The best-performing model achieves a high level of accuracy and recall, significantly reducing false negatives and effectively identifying fraudulent transactions.
## Future Work
- Explore additional advanced modeling techniques like neural networks.
- Implement real-time fraud detection solutions.
- Continuously update models with new transaction data to maintain accuracy.
## License
This project is licensed under the GNU General Public License v3.0. See the [LICENSE](LICENSE) file for details.