🛡️ Detective.AI – Real-Time Fraud Detection System

Detective.AI is a machine learning-powered web application that detects fraudulent transactions in real-time. Trained on the IEEE-CIS Fraud Detection dataset, this app leverages XGBoost, SMOTE, and Streamlit to deliver fast and reliable predictions based on transaction data.

🚀 Features

• Predicts fraudulent vs legitimate transactions
• User-friendly Streamlit interface
• XGBoost model with SMOTE to handle class imbalance
• Custom preprocessing pipeline (label encoding, scaling)
• Interactive form inputs for simulation

🧠 Model Details

Component	Description
Model	XGBoost Classifier
Preprocessing	SMOTE Oversampling, Label Encoding
Input Features	TransactionAmt, card1, card2, addr1, addr2, P_emaildomain, ProductCD_W, card4_visa, card6_credit, M6_T, etc.
Target Variable	isFraud (Binary Classification)

📁 Project Structure

Detective.AI/
│
├── models/ # All serialized ML assets
│ ├── fraud_detection_model.joblib
│ ├── model_columns.joblib
│ └── email_encoder.joblib
│
├── app.py # Main Streamlit app
├── requirements.txt # Project dependencies
└── README.md # You're reading it 

🧪 Model Evaluation

Classification Report

Class	Precision	Recall	F1-Score	Support
0 (Legitimate)	0.98	0.98	0.98	113,952
1 (Fraudulent)	0.54	0.54	0.54	4,154
Accuracy			0.97	118,106
Macro Avg	0.76	0.76	0.76	118,106
Weighted Avg	0.97	0.97	0.97	118,106

🚨 Note: While the model achieves high overall accuracy, the recall for fraudulent transactions (class 1) is moderate. This is expected due to the imbalanced nature of the dataset. Techniques like SMOTE have been applied to mitigate this.