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README.md

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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.