Seife1/Insurance_Risk_Prediction

This project aims to analyze historical car insurance claim data from South Africa. # AlphaCare Insurance Solutions - Car Insurance Claim Analysis ## Overview This project involves analyzing historical insurance claim data to optimize marketing strategies and identify low-risk targets for premium adjustments. The objective is to use advanced data analytics, machine learning, and statistical modeling techniques to provide actionable insights for AlphaCare Insurance Solutions. The project also incorporates CI/CD pipelines and version control for efficient collaboration and reproducibility. ## Business Objective The analysis should guide AlphaCare Insurance Solutions in determining optimal insurance premiums for different clients based on their risk profiles and geographic locations. This could help the company offer more competitive rates, especially to low-risk customers. ## Data Summary The dataset includes information about car insurance premiums, claims, and other policy details. ## Key Features - **Exploratory Data Analysis (EDA)**: Summarize and visualize data trends. Detect outliers and assess data quality. - **Hypothesis Testing**: Conduct A/B testing to evaluate risk and profitability across demographics and regions. - **Predictive Modeling**: Build and evaluate machine learning models such as: * Linear Regression * Random Forests * Gradient Boosting (XGBoost) Analyze feature importance to understand key drivers of claims and premiums. - **Version Control**: Manage datasets and model versions using DVC. Use Git and GitHub for source code versioning and collaboration. - **CI/CD**: Automate testing, linting, and deployment using GitHub Actions. Dockerize the application for reproducibility and scalability. ## Folder Structure ```bash project-root/ │ ├── data/ # Data storage │ ├── raw/ # Original data files │ └── processed/ # Cleaned and transformed data │ ├── notebooks/ # Jupyter notebooks for analysis │ ├── eda.ipynb # Exploratory Dat …