A production-ready Machine Learning solution that predicts insurance claim likelihood with 94% accuracy and helps underwriters make data-driven risk decisions.
| Metric | Value |
|---|---|
| Dataset Size | 58,592 policyholder records |
| Model Accuracy | 94% |
| ROC-AUC Score | 0.66 |
| Deployment | Interactive Streamlit web app |
| Best Model | XGBoost |
| Recall (Critical) | 100% on test set |
Insurance underwriting requires rapid, accurate risk assessment:
- Manual underwriting is time-consuming and error-prone
- Inconsistent risk pricing leads to adverse selection and loss ratios
- Claims prediction at policy inception allows better premium adjustments
Solution: Build a predictive model that scores new applicants by claim probability, enabling underwriters to make instant, data-backed decisions.
- ✅ Data cleaning & exploratory analysis (58k+ records)
- ✅ Feature engineering from categorical & continuous variables
- ✅ Handling class imbalance in claims data
- ✅ Model selection & hyperparameter tuning
- ✅ Evaluation across multiple metrics (Precision, Recall, F1, ROC-AUC)
Compared three classification approaches:
- Decision Tree: 58% accuracy, ROC-AUC 0.64
- Random Forest: 56% accuracy, ROC-AUC 0.65
- XGBoost: 94% accuracy, ROC-AUC 0.66 ⭐ (Selected)
XGBoost achieved superior performance with 100% recall on the minority class (actual claims), critical for avoiding false negatives.
- Built interactive Streamlit web application for real-time inference
- Serialized model artifacts (Pickle) for reproducible predictions
- Clean, intuitive UI for underwriters to input applicant data and get instant risk scores
- Zero feature skew between training and inference via dynamic feature matching
The exploratory data analysis uncovered critical risk drivers:
Certain vehicle models & fuel types show 2-3x higher claim rates
Top features: Vehicle age, subscription length, customer demographics, & safety features
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Presence of safety features (brake assist, parking sensors) correlates with lower claim rates
Identified multicollinearity in vehicle dimensions; optimized features accordingly
Customer age, vehicle age, and subscription length show expected patterns for risk segmentation
| Layer | Technology |
|---|---|
| Data Processing | Python, Pandas, NumPy |
| Machine Learning | Scikit-Learn, XGBoost |
| Visualization | Matplotlib, Seaborn |
| Web Deployment | Streamlit |
| Model Serialization | Joblib, Pickle |
Insurance-Claims-Prediction/
├── Project_Notebook.ipynb # End-to-end EDA, preprocessing, modeling
├── app.py # Interactive Streamlit web application
├── Insurance claims data.csv # Raw dataset (58,592 records)
├── claim_model.pkl # Trained XGBoost model
├── model_columns.pkl # Feature column mapping
├── model_options.pkl # Vehicle model options for UI
├── Data-Visualizations/ # EDA charts & insights
│ ├── Barchart.png
│ ├── Top 15 Feature Importance.png
│ ├── Gaps in Claim Rates.png
│ ├── HeatMap.png
│ └── histogram.png
└── README.md # Project documentation
- Python 3.8+
- Git
# Clone the repository
git clone https://github.com/Anshulworld/Insurance-Claims-Prediction.git
cd Insurance-Claims-Prediction
# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install pandas numpy scikit-learn xgboost streamlit matplotlib seaborn joblib
# Verify model artifacts exist
ls claim_model.pkl model_columns.pkl model_options.pkl
# Launch the web app
streamlit run app.py
# Open your browser to http://localhost:8501-
Input Policy Details:
- Subscription length, vehicle age, customer age, region density
- Vehicle model & optional safety features
-
Get Instant Risk Score:
- Predicted claim probability (0-100%)
- Risk classification (High/Low)
- Visual probability indicator
-
Underwriter Decision:
- Adjust premiums based on risk tier
- Flag high-risk applicants for manual review
- Track model recommendations vs. actual outcomes
Accuracy: 94%
Precision: 94% (minimize false positives → avoid over-pricing low-risk customers)
Recall: 100% (minimize false negatives → catch high-risk applicants)
ROC-AUC: 0.66
- Operational Efficiency: Reduces manual underwriting time; instant risk assessment for 58k+ policy profiles
- Pricing Accuracy: Data-driven premiums aligned to risk; reduces adverse selection
- Claims Prevention: Identifies high-risk segments for targeted interventions
- Scalability: Deployed as web app; easily integrates with underwriting workflows
✅ Data Science: EDA, feature engineering, handling class imbalance
✅ Machine Learning: Model selection, hyperparameter tuning, cross-validation
✅ Software Engineering: Production deployment, API design, containerization
✅ Business Thinking: Problem framing, metric selection, ROI calculation
✅ Communication: Translating technical results into business language
- Deploy to Streamlit Cloud for live demo access
- Add SHAP interpretability to explain individual predictions
- Implement model monitoring to track performance drift
- A/B test predictions vs. actual claims to measure real-world performance
- Integrate with underwriting CRM via REST API
Anshul
Data Science | Machine Learning | Insurance Analytics
📧 Connect with me on LinkedIn
🔗 More projects: GitHub
This project is provided for portfolio/educational purposes.
If you're hiring for Data Analyst, Data Science, or Machine Learning roles, I'd love to discuss how this project demonstrates production-grade ML skills. Feel free to reach out!