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---
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.
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