End-to-End Insurance Risk Analytics & Predictive Modeling

A complete, modular, production-ready machine learning pipeline for insurance analytics.

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Project Overview

This project implements a fully modular end-to-end ML pipeline for insurance risk analytics and predictive modeling. It supports real-world insurance business applications such as:

• Analyzing historical policies, claims, and exposures\
• Performing EDA and anomaly detection\
• Conducting hypothesis tests to validate key risk drivers\
• Building models for claim probability, claim severity, and premium optimization\
• End-to-end reproducible ML pipeline with CI/CD support\
• Integrated reporting, logging, and versioning

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Business Objective

AlphaCare Insurance Solutions (ACIS) aims to:

• Identify low-risk customer segments\
• Optimize premium pricing while maximizing profitability\
• Understand factors contributing to claims\
• Support actuarial and underwriting decisions\
• Enhance customer retention with targeted strategies

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Full Project Folder Structure

End-to-End-Insurance-Risk-Analytics-Predictive-Modeling/
├── .github/
│   └── workflows/                 # CI/CD pipelines (tests, linting, dvc)
├── configs/
│   ├── data.yaml                  # Dataset configuration
│   ├── dvc_remote.yaml            # DVC remote configuration
│   ├── logs.yaml                  # Logging settings
│   └── modeling.yaml              # ML model configurations
├── data/
│   ├── raw/                       # Original data
│   ├── processed/                 # Cleaned & feature engineered data
├── docs/                          # Documentation & reports
├── notebooks/
│   ├── analysis/
│   │   ├── hypothesis_tests.ipynb
│   │   └── model_building.ipynb
│   └── exploration/
│       ├── data_overview.ipynb
│       └── eda.ipynb
├── scripts/
│   ├── __init__.py
│   ├── clean_data.py
│   ├── run_eda_pipeline.py
│   ├── run_hypothesis_tests.py
│   └── train_models.py
├── src/
│   └── insurance_analytics/
│       ├── __init__.py
│       ├── core/
│       │   ├── __init__.py
│       │   ├── config.py
│       │   ├── logger.py
│       │   ├── registry.py
│       │   └── scheduler.py
│       ├── eda/
│       │   ├── __init__.py
│       │   ├── exploration.py
│       │   └── visualization.py
│       ├── models/
│       │   ├── __init__.py
│       │   ├── evaluation.py
│       │   ├── interpretability.py
│       │   ├── linear_regression.py
│       │   ├── random_forest.py
│       │   └── xgboost_model.py
│       ├── preprocessing/
│       │   ├── __init__.py
│       │   ├── cleaner.py
│       │   └── feature_engineering.py
│       ├── utils/
│       │   ├── __init__.py
│       │   ├── io_utils.py
│       │   ├── metrics.py
│       │   ├── project_root.py
│       │   ├── system.py
│       │   └── validation.py
│       └── viz/
│           ├── __init__.py
│           └── plots.py
├── tests/
│   ├── integration/
│   │   ├── __init__.py
│   │   ├── test_dvc_integration.py
│   │   ├── test_eda_pipeline.py
│   │   ├── test_full_pipeline.py
│   │   └── test_model_pipeline.py
│   └── unit/
│       ├── __init__.py
│       ├── test_cleaners.py
│       ├── test_features.py
│       ├── test_hypothesis.py
│       ├── test_loaders.py
│       ├── test_models.py
│       └── test_registry.py
├── .gitignore
├── README.md
└── requirements.txt

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How to Run the Project

Create a Virtual Environment

python -m venv venv
venv\Scripts\activate        # Windows
source venv/bin/activate     # macOS/Linux

Install Dependencies

pip install -r requirements.txt

Run Data Cleaning

python scripts/clean_data.py

Run EDA Pipeline

python scripts/run_eda_pipeline.py

Train Machine Learning Models

python scripts/train_models.py

Use Jupyter Notebooks

jupyter notebook

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Key Features

• ✔ Modular ML architecture\
• ✔ Clear data/configs/scripts separation\
• ✔ DVC versioning\
• ✔ CI-ready workflows\
• ✔ Logging & validation utilities\
• ✔ Interpretability (SHAP, feature importance)\
• ✔ Reproducible experiments

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Reports

• docs/interim_report.md\
• docs/final_report.md

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Testing

pytest

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Version Control

git init
git add .
git commit -m "Initial project structure"