FenetShewarega/insurance-risk-analytics

End-to-end insurance risk analytics & predictive modeling — ACIS South African auto-insurance (AlphaCare Insurance Solutions) # Insurance Risk Analytics & Predictive Modeling End-to-end analytics project for **AlphaCare Insurance Solutions (ACIS)**: analyse 18 months of South African auto-insurance claim data (Feb 2014 – Aug 2015), validate risk hypotheses, and build risk-based pricing models. ## Business Context ACIS is preparing for an aggressive growth phase in the South African auto-insurance market. The goals are to: 1. Identify **low-risk customer segments** where premiums can be reduced to attract new clients. 2. **Statistically validate** hypotheses about risk drivers (province, zip code, gender). 3. Build **predictive models** for claim severity and claim probability that feed a dynamic, risk-based premium. 4. Deliver clear **business-facing recommendations**. ## Key Metrics - **Loss Ratio** = `TotalClaims / TotalPremium` — portfolio profitability. - **Margin** = `TotalPremium − TotalClaims` — per-policy profit contribution. - **Claim Frequency** — proportion of policies with at least one claim. - **Claim Severity** — mean claim amount given a claim occurred. ## Project Structure ``` insurance-risk-analytics/ ├── .github/workflows/ci.yml # Lint + tests on every push ├── data/ # DVC-tracked, not in Git ├── notebooks/ │ ├── 01_eda.ipynb │ ├── 02_hypothesis_testing.ipynb │ └── 03_modeling.ipynb ├── src/ # Reusable Python modules │ ├── data_loader.py │ ├── eda_utils.py │ ├── hypothesis_tests.py │ └── modeling.py ├── reports/final_report.md ├── tests/ ├── dvc.yaml ├── requirements.txt └── README.md ``` ## Setup ```bash # 1. Clone & enter the repo git clone && cd insurance-risk-analytics # 2. Create a virtual environment python -m venv .venv && source .venv/bin/activate # 3. Install dependencies pip install -r requirements.txt # 4. Pull the data from the DVC remote dvc pull ``` ## Reproducing the Data Pipeline (DVC) Data is versioned with **DVC** so every analysis is reproducible. ```bash # 1) Install …