| tags: | |
| - fraud-detection | |
| - random-forest | |
| - sklearn | |
| library_name: sklearn | |
| pipeline_tag: tabular-classification | |
| # Random Forest Fraud Detection Model | |
| This model uses Random Forest classification to detect potential fraud based on various account and transaction features. | |
| ## Model Description | |
| - **Input Features:** | |
| - Account Age (months) | |
| - Frequency of credential changes (per year) | |
| - Return to Order ratio | |
| - VPN/Temp Mail usage (binary) | |
| - Credit Score | |
| - **Output:** Binary classification (Fraud/Not Fraud) | |
| - **Type:** Random Forest Classifier | |
| ## Usage | |
| ```python | |
| import joblib | |
| import numpy as np | |
| # Load model and scaler | |
| model = joblib.load('random_forest_model.joblib') | |
| scaler = joblib.load('rf_scaler.joblib') | |
| # Prepare input (example) | |
| input_data = np.array([[25, 0.5, 0.4, 0, 800]]) | |
| # Scale input | |
| scaled_input = scaler.transform(input_data) | |
| # Get prediction | |
| prediction = model.predict(scaled_input) | |
| probability = model.predict_proba(scaled_input) | |
| ``` | |
| ## Limitations and Bias | |
| This model should be used as part of a larger fraud detection system and not in isolation. | |