pages/project_details.py

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import streamlit as st

# Page title
st.title("Project Details")

# Header with an image
st.image("https://images.unsplash.com/photo-1454165804606-c3d57bc86b40?ixlib=rb-1.2.1&auto=format&fit=crop&w=1950&q=80", width=850)

# Introduction section
st.header("Introduction")
st.write("""
The **Financial Fraud Detection System** is an advanced solution designed to identify and prevent fraudulent transactions in real-time. 
With the increasing volume of online transactions, the need for a robust and scalable fraud detection system has become critical. 
Our project leverages state-of-the-art machine learning techniques to provide accurate and efficient fraud detection, helping financial institutions and businesses minimize losses and enhance security.
""")

# Objectives section
st.header("Project Objectives")
st.write("""
The primary objectives of the Financial Fraud Detection System are:
""")
st.markdown("""
- **Real-Time Fraud Detection**: Detect fraudulent transactions as they occur, enabling immediate intervention.
- **High Accuracy**: Achieve a fraud detection accuracy rate of over 95% to minimize false positives and false negatives.
- **Scalability**: Handle large volumes of transactions efficiently, ensuring the system can scale with growing demand.
- **User-Friendly Interface**: Provide an intuitive and easy-to-use interface for financial analysts and decision-makers.
- **Continuous Learning**: Enable the system to adapt to new fraud patterns by continuously retraining the model with new data.
""")

# Methodology section
st.header("Methodology")
st.write("""
Our methodology for developing the Financial Fraud Detection System involves the following steps:
""")
st.markdown("""
1. **Data Collection**: Gather transaction data from various sources, including banks, e-commerce platforms, and payment gateways.
2. **Data Preprocessing**: Clean, normalize, and transform the data to ensure it is suitable for analysis.
3. **Feature Engineering**: Extract relevant features from the transaction data, such as transaction amount, frequency, and user behavior.
4. **Model Training**: Train the XGBoost machine learning model on a labeled dataset of transactions to distinguish between legitimate and fraudulent activities.
5. **Model Evaluation**: Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score.
6. **Deployment**: Deploy the trained model in a production environment, enabling real-time fraud detection.
7. **Monitoring & Retraining**: Continuously monitor the system's performance and retrain the model with new data to adapt to evolving fraud patterns.
""")

# Technology Stack section
st.header("Technology Stack")
st.write("""
The Financial Fraud Detection System is built using the following technologies:
""")
st.markdown("""
- **Programming Language**: Python
- **Machine Learning Framework**: Scikit-learn, XGBoost
- **Data Processing**: Pandas, NumPy
- **Visualization**: Matplotlib, Seaborn, Plotly
- **Web Interface**: Streamlit
- **Model Serialization**: Joblib
- **Version Control**: Git
""")

# Key Features section
st.header("Key Features")
st.write("""
The Financial Fraud Detection System offers the following key features:
""")
st.markdown("""
- **Real-Time Processing**: Analyze transactions in real-time to detect fraud as it happens.
- **Batch Processing**: Upload and analyze bulk transaction data in CSV format.
- **Interactive Dashboard**: Visualize fraud detection results with interactive charts and graphs.
- **Fraud Probability Scores**: Provide a fraud risk score for each transaction, helping analysts prioritize investigations.
- **Decision Explainability**: Offer insights into why a transaction was flagged as fraudulent, enhancing transparency.
- **Scalable Architecture**: Designed to handle high volumes of transactions without performance degradation.
""")

# Future Enhancements section
st.header("Future Enhancements")
st.write("""
We are continuously working to improve the Financial Fraud Detection System. Some of the planned enhancements include:
""")
st.markdown("""
- **Integration with Banking Systems**: Enable seamless integration with existing banking and payment systems for live fraud detection.
- **Advanced Feature Engineering**: Incorporate additional features such as behavioral analytics and device tracking to improve detection accuracy.
- **Automated Model Retraining**: Implement an automated pipeline for retraining the model with new data to adapt to evolving fraud patterns.
- **Mobile-Friendly Interface**: Develop a mobile-friendly version of the web interface for on-the-go fraud detection monitoring.
""")

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# Footer
st.markdown("---")
st.write("""
© 2024 Financial Fraud Detection System. All rights reserved.
""")