pages/home.py

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# *************************************************************   『I』『M』『P』『O』『R』『T』『S』  *************************************************************

import streamlit as st

# Page title
st.title("Welcome to the Financial Fraud Detection System")

# Header with an image
st.image("https://images.unsplash.com/photo-1611974789855-9c2a0a7236a3?ixlib=rb-1.2.1&auto=format&fit=crop&w=1950&q=80", width=850)

# Introduction section
st.header("Introduction")
st.write("""
In the digital age, financial fraud has become a significant concern for individuals, businesses, and financial institutions. 
With the increasing volume of online transactions, the need for robust fraud detection systems has never been more critical. 
Our **Financial Fraud Detection System** leverages advanced machine learning techniques to identify and prevent fraudulent activities in real-time.
""")

# Key features section
st.header("Key Features")
col1, col2, col3 = st.columns(3)

with col1:
    st.subheader("Real-Time Detection")
    st.write("""
    Our system processes transactions in real-time, providing instant fraud detection and alerting. 
    This ensures that fraudulent activities are identified and mitigated as soon as they occur.
    """)

with col2:
    st.subheader("High Accuracy")
    st.write("""
    Utilizing state-of-the-art machine learning algorithms, our system achieves an accuracy rate of over 95%, 
    minimizing false positives and ensuring reliable fraud detection.
    """)

with col3:
    st.subheader("User-Friendly Interface")
    st.write("""
    The system features an intuitive web interface built with Streamlit, allowing users to easily upload transaction data, 
    view fraud predictions, and analyze results with detailed visualizations.
    """)

# How it works section
st.header("How It Works")
st.write("""
Our Financial Fraud Detection System is built on the **XGBoost** algorithm, a powerful machine learning model known for its efficiency and accuracy in handling tabular data. 
The system processes both historical and real-time transaction data, identifying patterns and anomalies that indicate fraudulent behavior.
""")

# Steps in the process
st.subheader("Process Overview")
st.write("""
1. **Data Collection**: Transaction data is collected from various sources, including banks, e-commerce platforms, and payment gateways.
2. **Data Preprocessing**: The data is cleaned, normalized, and transformed to ensure it is suitable for analysis.
3. **Model Training**: The XGBoost model is trained on a large dataset of labeled transactions, learning to distinguish between legitimate and fraudulent activities.
4. **Real-Time Detection**: The trained model is deployed to analyze incoming transactions in real-time, flagging potential fraud for further investigation.
5. **Visualization & Reporting**: Users can view detailed reports and visualizations of fraud predictions, enabling informed decision-making.
""")

# Call to action
st.header("Get Started")
st.write("""
Ready to experience the power of our Financial Fraud Detection System? 
Navigate to the **Fraud Detection** page to upload your transaction data and start detecting fraud today!
""")

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