This project implements a machine learning system to predict driver risk levels (Low, Medium, High) based on telematics data. The model helps insurance companies assign risk categories for personalized premium pricing using synthetic driving behavior data. Insurance Telematics Risk Assessment:
Predict driver risk levels from telematics features
Enable data-driven premium pricing
Reduce claims through risk-aware pricing
Improve customer segmentation
📊 Features Used
The model analyzes 8 key telematics features:
Speed Variance - Variability in driving speed (km/h)
Harsh Braking Events - Sudden braking incidents per week
Hard Acceleration Count - Rapid acceleration events per week
Night Driving Frequency - Percentage of trips during night hours
Cornering Intensity - Aggressiveness in taking turns (1-5 scale)
Mileage Per Trip - Average distance per driving session (km)
Rapid Lane Changes - Quick lane switching events per week
Idle Time Ratio - Percentage of time spent idling
🏗️ Model Architecture Algorithms Implemented:
Random Forest Classifier
XGBoost Classifier
Neural Network (3-layer DNN)
Performance Metrics:
Accuracy Score
Classification Report
Confusion Matrix
Cross-validation
📁 Project Structure text
driver-risk-scoring/ ├── driver_risk_scoring.ipynb # Main Colab notebook ├── driver_risk_model.pkl # Trained model (output) ├── scaler.pkl # Feature scaler (output) ├── feature_names.txt # Feature list (output) └── README.md # This file
🚀 Quick Start
- Installation & Setup python
!pip install sdv xgboost tensorflow scikit-learn pandas numpy matplotlib seaborn
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Data Generation
Synthetic dataset of 10,000 drivers
8 telematics features with realistic distributions
3 risk categories: Low, Medium, High
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Model Training python
from xgboost import XGBClassifier model = XGBClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train)
- Risk Prediction python
sample_driver = [15.3, 3.2, 4.5, 0.4, 2.5, 22.1, 2.8, 0.15] risk_prediction = model.predict([sample_driver]) premium_multiplier = calculate_premium(risk_prediction)
💡 Key Features 🔍 Exploratory Data Analysis
Feature distributions by risk level
Correlation matrix visualization
Risk category balance analysis
🤖 Multiple ML Models
Random Forest: Robust ensemble method
XGBoost: High-performance gradient boosting
Neural Network: Deep learning approach
📈 Model Evaluation
Accuracy comparison across algorithms
Feature importance analysis
Confusion matrices
Business impact assessment
💼 Insurance Application
Risk-based premium calculation
Premium multipliers:
Low Risk: 0.8x (20% discount)
Medium Risk: 1.0x (standard premium)
High Risk: 1.5x (50% surcharge)
📊 Results Summary Model Performance:
XGBoost: ~95% accuracy (recommended)
Random Forest: ~94% accuracy
Neural Network: ~92% accuracy
Top Predictive Features:
Harsh Braking Events
Speed Variance
Night Driving Frequency
Business Impact:
Risk-adjusted premium pricing
Fair customer segmentation
Data-driven underwriting decisions
🛠️ Usage Examples Individual Risk Assessment python
driver_data = [8.2, 1.5, 2.1, 0.2, 1.8, 12.3, 1.2, 0.08] result = calculate_risk_score_and_premium(driver_data, model)
print(f"Risk Level: {result['predicted_risk']}") print(f"Premium Multiplier: {result['suggested_premium_multiplier']}x")
Batch Processing python
risk_predictions = model.predict(batch_driver_data) premiums = [calculate_premium(pred) for pred in risk_predictions]
📈 Visualizations
The project includes comprehensive visualizations:
Risk distribution charts
Feature importance plots
Model accuracy comparisons
Training history graphs (Neural Network)
Correlation heatmaps
🔮 Future Enhancements
Real telematics data integration
Time-series analysis of driving patterns
Geospatial risk factors
Weather condition integration
Real-time risk scoring API
Mobile app integration
📚 Technical Details Data Synthesis:
Generated using sklearn's make_classification
Realistic value ranges based on industry data
Balanced risk categories
Model Selection:
XGBoost chosen for best performance
Hyperparameter tuning ready
Cross-validation implemented
Deployment Ready:
Model serialization with joblib
Feature scaling pipeline
Production inference code
👥 Target Audience
Insurance Companies: Risk assessment teams
Data Scientists: ML model development
Product Managers: Insurance product design
Researchers: Telematics and risk modeling
📄 License
This project is for educational and demonstration purposes. Adapt for commercial use with proper validation and compliance with insurance regulations.
Note: This implementation uses synthetic data. For production use, validate with real telematics data and ensure compliance with local insurance regulations and data privacy laws.