The problem Fraud Detection System solves

1. Swift Fraud Detection: Analyze massive transactions in real-time, identifying and flagging potential fraud much faster than humans.
2. Uncover Hidden Patterns: ML algorithms reveal subtle patterns in data that humans might miss, helping catch sophisticated fraud schemes.
3. Adaptable to Evolving Threats: ML models continuously learn and improve, staying ahead of ever-changing fraudster tactics.
4. Minimize False Positives: Efficient models reduce instances of legitimate transactions being flagged as fraudulent, avoiding unnecessary friction for customers.
   Proactive Prevention: Gain insights to strengthen systems and proactively prevent future fraud attempts, going beyond just catching perpetrators.

Challenges we ran into

1. Understanding Encoded Data: The credit card data features (v1, v2, v3, etc.) are likely encoded for security, making direct interpretation difficult.
2. Random Forest Limitations: Random Forest, while powerful, might have lower precision due to bootstrapping. It typically performs better with explicitly defined features rather than encoded data.
3. Model Selection: Finding an optimal model for encoded data might require exploration, potentially using cross-validation for evaluation.
4. Data Clarity: It seems there's a need to gain a better understanding of the dataset before drawing reliable conclusions.
5. Diverse Inputs: The project implies handling multiple input sources, adding complexity.
6. AI/React Integration: Smoothly integrating the AI solution into a React front-end requires careful technical planning.
7. AWS SageMaker Endpoint Creation: Deploying the selected model on AWS SageMaker necessitates familiarity with its endpoint creation process.