https://github.com/alihassanml/credit-card-fraud-detection
Credit-Card-fraud-Detection-Machine-Learning-Project-using-Logistic-Regression-model
https://github.com/alihassanml/credit-card-fraud-detection
Last synced: 8 months ago
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Credit-Card-fraud-Detection-Machine-Learning-Project-using-Logistic-Regression-model
- Host: GitHub
- URL: https://github.com/alihassanml/credit-card-fraud-detection
- Owner: alihassanml
- Created: 2024-04-05T18:57:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-09T17:56:11.000Z (over 1 year ago)
- Last Synced: 2025-01-01T15:11:50.555Z (9 months ago)
- Language: Jupyter Notebook
- Size: 104 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Credit Card Fraud Detection using Machine Learning with Streamlit
This project aims to detect fraudulent credit card transactions using machine learning techniques, implemented with the Streamlit framework. Fraudulent transactions pose a significant threat to financial institutions and consumers alike, making it imperative to develop robust detection methods.
### Libraries Used:
- Pandas: For data manipulation and analysis.
- NumPy: For numerical computations.
- Matplotlib & Seaborn: For data visualization.
- Streamlit: For building interactive web applications.
- Scikit-learn: For machine learning algorithms and evaluation metrics.
### Methodology:
1. **Data Preprocessing**:
- Load the dataset containing credit card transactions.
- Perform exploratory data analysis to understand the distribution of features.
- Handle missing values, if any.
- Scale numerical features to a standard range if required.
2. **Feature Engineering**:
- Extract relevant features from the dataset.
- Perform dimensionality reduction techniques if needed, such as PCA.
3. **Model Building**:
- Utilize machine learning algorithms to train a fraud detection model.
- In this project, Logistic Regression is chosen as a baseline model due to its simplicity and interpretability.
- Split the dataset into training and testing sets.
4. **Model Evaluation**:
- Evaluate the model's performance using appropriate metrics such as accuracy, precision, recall, and F1-score.
- Utilize techniques like cross-validation to ensure the model's generalization ability.
5. **Streamlit Integration**:
- Build an interactive web application using Streamlit to provide a user-friendly interface for detecting fraudulent transactions.
- Users can input transaction details, and the application will predict whether the transaction is fraudulent or not.
### How to Use:
1. Clone the repository to your local machine.
2. Install the required dependencies listed in the `requirements.txt` file.
3. Run the Streamlit application using the command `streamlit run app.py`.
4. Input transaction details in the provided interface.
5. Get instant predictions on the authenticity of the transaction.
### Conclusion:
Credit card fraud detection is a crucial task in the financial industry. By leveraging machine learning techniques and building interactive applications like this, we can enhance fraud detection capabilities, thereby safeguarding the interests of financial institutions and consumers.
### Note:
This project serves as an educational resource and a demonstration of utilizing machine learning for fraud detection purposes. It's essential to continually update and improve the model's performance to adapt to evolving fraud patterns and techniques. Additionally, ensure compliance with data privacy and security regulations when working with sensitive financial data.