This is a simple Streamlit web application for credit card fraud detection using a logistic regression model. The app allows users to input values for various features and predicts whether a credit card transaction is fraudulent or not.
The goal of this project is to demonstrate how to build a basic credit card fraud detection app using Streamlit. The app uses a pre-trained logistic regression model to make predictions based on user input.
To run the app locally on your machine, follow these steps:
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Clone the repository:
git clone https://github.com/Aayush518/credit-card-fraud-detection-app.git
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Navigate to the project directory:
cd credit-card-fraud-detection-app
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Install the required packages:
pip install -r requirements.txt
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Run the Streamlit app:
streamlit run app.py
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Open the Streamlit app in your web browser by following the instructions in the "Getting Started" section.
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Use the sidebar to input values for the features related to the credit card transaction.
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Click the "Predict" button to see the model's prediction for whether the transaction is fraudulent or not.
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The app will display the prediction result and a confusion matrix heatmap.
- User-friendly interface for inputting credit card transaction features.
- Real-time prediction of whether a transaction is fraudulent or not.
- Visualization of confusion matrix heatmap to assess model performance.
- Histograms showing the distribution of selected features.