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🚗 Insurance Risk Modeling & Dynamic Pricing System

This project develops a robust, explainable, and risk-aware pricing model for auto insurance policies. It incorporates statistical analysis, machine learning, and reproducible data practices to predict insurance claim severity and optimize premium pricing.

✅ Completed as part of the Week 3 Challenge at 10Academy.

🧭 Project Goals

  • Understand and explore insurance data to uncover actionable insights.
  • Establish a reproducible data pipeline using Git, GitHub, and DVC.
  • Statistically validate hypotheses related to insurance risk.
  • Build predictive models to estimate:
    • 💰 Claim Severity — How much we might pay.
    • 📈 Claim Probability — How likely a customer is to claim.
  • Construct a dynamic pricing formula that incorporates business margins.

🔧 Technologies & Tools

Area Tools Used
Programming Python, Jupyter
Data Handling Pandas, NumPy, DVC
Visualization Matplotlib, Seaborn, Plotly
Modeling Scikit-learn, XGBoost, SHAP, LIME
Version Control Git, GitHub, GitHub Actions
CI/CD GitHub Actions
Environment venv + requirements.txt

📂 Repository Structure

.
├── data/ # Raw and processed data (tracked via DVC)
├── models/ # Saved models
├── notebooks/ # Jupyter notebooks for EDA, testing, modeling
├── src/ # Core source code
│ ├── preprocessing/ # Cleaning, transformation, encoding
│ ├── task_3/ # Hypothesis testing modules
│ └── task_4/ # Modeling pipeline and interpretation
├── tests/ # Unit tests
├── .dvc/ # DVC metadata
├── .github/workflows/ # GitHub Actions CI pipeline
├── dvc.yaml # DVC pipeline definition
├── requirements.txt # Python dependencies
└── README.md # Project overview (this file)

📊 Task Breakdown

🔍 Task 1: EDA & Git Setup

  • Configured Git and GitHub, created task-1 branch
  • Performed EDA on claims, premiums, and customer demographics
  • Visualized insights across provinces, genders, and vehicle types
  • Identified key drivers of loss ratio and risk

💾 Task 2: Data Version Control (DVC)

  • Installed DVC and initialized version control
  • Added data files to DVC tracking
  • Set up a local remote storage and pushed data
  • Ensured reproducibility and auditability of datasets

📊 Task 3: Hypothesis Testing

  • Formulated and tested statistical hypotheses:
    • 📍 Risk varies across provinces and zip codes
    • 👥 Gender differences in claim frequency and severity
    • 💸 Profitability margins vary by region
  • Used t-tests, z-tests, chi-squared where applicable
  • Business interpretations provided for each result

🧠 Task 4: Predictive Modeling

  • Built severity regression models: Linear, Random Forest, XGBoost
  • Evaluated using RMSE, R²
  • Used SHAP and LIME for feature importance
  • Modeled claim probability (classification) for pricing
  • Final pricing formula:

📈 Key Insights & Recommendations

Insight Impact on Pricing Strategy
≤4 Cylinder vehicles → ↑ Severity Risk Apply loading to small-engine vehicles
Non-VAT Registered → ↑ Risk Raise base rate for unregistered customers
Converted/Modified Vehicles = ↑ Risk Apply higher risk surcharge
Alarm/Immobilizer → ↓ Risk Provide discount for security features
New Vehicles → ↓ Risk Discount for newer vehicles

📦 Setup Instructions

  1. Clone the repository: 
    git clone https://github.com/ayanasamuel8/End-to-End-Insurance-Risk-Analytics-and-Predictive-Modeling.git
cd insurance-risk-model

Install dependencies:

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

Run notebooks:

jupyter notebook

Run tests:

pytest

🧪 Data Versioning with DVC bash dvc init dvc add data/raw/insurance_data.csv dvc remote add -d localstorage /path/to/your/storage dvc push To reproduce the data pipeline:

bash dvc pull ✅ CI/CD GitHub Actions is configured for:

Code linting

Unit tests

Model validation (optional step)

Workflow defined in .github/workflows/deploy.yml.

📌 Results Summary 🧮 Best Severity Model: XGBoost RMSE improvement: +Δ% vs. baseline Top features: Engine Size, Vehicle Age, Province, Conversion Status

🧠 Classification Accuracy: ~X% Enables dynamic, fair, and risk-adjusted premium pricing

👥 Contributors 👤 Ayana Samuel Role: Full Data Science Workflow Skills: EDA, DVC, Statistical Testing, Machine Learning, GitOps GitHub: https://github.com/ayanasamuel8/End-to-End-Insurance-Risk-Analytics-and-Predictive-Modeling.git

📜 License This project is licensed for academic and demonstration use. Contact the author for commercial usage rights.