| # Data Challenge Overview | |
| Welcome to the data challenge! This dataset release contains information for three predictive modeling tasks involving auto insurance policies and claims. | |
| ## Directory & File Structure | |
| The dataset is organized into CSV files and two folders containing images and PDF documents: | |
| ``` | |
| ├── train_claims.csv | |
| ├── test_claims.csv | |
| ├── train_policies_subset.csv | |
| ├── test_policies_subset.csv | |
| ├── vehicle_images/ | |
| │ ├── train/ | |
| │ │ ├── <PolicyID>.jpg | |
| │ └── test/ | |
| │ ├── <PolicyID>.jpg | |
| ├── invoices/ | |
| │ ├── train/ | |
| │ │ ├── invoice_<ClaimID>.pdf | |
| │ └── test/ | |
| │ ├── invoice_<ClaimID>.pdf | |
| ``` | |
| ## Claims Tables | |
| **Files:** `train_claims.csv`, `test_claims.csv` | |
| **Columns:** | |
| - **ClaimID**: Unique identifier for each claim. | |
| - **PolicyID**: Identifier linking the claim to a policy. | |
| - **ClaimDate**: Date the claim occurred. | |
| - **ClaimType**: Type of claim ("Fender-Bender", "Major Collision", "Theft/Comprehensive"). | |
| - **ReportedDamage**: Reported monetary damage of the claim. | |
| - **NumParties**: Number of parties involved in the claim. | |
| - **Description**: Textual description of the claim. | |
| - **ClaimComplexityLabel** (train only): Label indicating claim complexity ("Simple", "Moderate", "Complex"). | |
| - **FraudLabel** (train only): Binary indicator (0 or 1) of whether the claim is fraudulent. | |
| > Note: `ClaimComplexityLabel` and `FraudLabel` are not provided in the test set. | |
| ## Policies Tables | |
| These policy tables are a subset of policies—only those for which a vehicle image is available. | |
| **Files:** `train_policies_subset.csv`, `test_policies_subset.csv` | |
| **Columns:** | |
| - **PolicyID**: Unique identifier for the insurance policy. | |
| - **HolderAge**: Age of the policyholder. | |
| - **VehicleType**: Type of vehicle (Sedan, SUV, Sports). | |
| - **AnnualMileage**: Miles driven annually by the vehicle. | |
| - **LocationUrban**: 1 if urban, 0 if rural. | |
| - **CreditScore**: Credit score of policyholder (0 to 1 scale). | |
| - **PolicyStart**: Policy start date. | |
| - **PolicyEnd**: Policy end date. | |
| - **NextYearLoss** (train only): Total insurance loss expected for the next year (target variable). | |
| > Note: `NextYearLoss` is not provided in the test set. | |
| ## Image & PDF Folders | |
| `vehicle_images/train/` and `vehicle_images/test/`: | |
| Contains images named as `<PolicyID>.jpg`, visually indicating the vehicle's condition. | |
| `invoices/train/` and `invoices/test/`: | |
| Contains PDF invoices for each claim, named as `invoice_<ClaimID>.pdf`. | |
| ## Challenge Tasks & Submission Format | |
| ### Challenge 1: Claims Complexity Prediction | |
| Predict the `ClaimComplexityLabel` for each claim in `test_claims.csv`. | |
| **Submission CSV:** `ClaimID,ClaimComplexityLabel` | |
| **Evaluation Metric:** Macro-F1. | |
| ### Challenge 2: Risk-Based Pricing | |
| Predict the `NextYearLoss` for each policy in `test_policies_subset.csv`. | |
| **Submission CSV:** `PolicyID,NextYearLoss` | |
| **Evaluation Metric:** Normalized Gini. | |
| ### Challenge 3: Fraud Detection | |
| Predict the binary `FraudLabel` for each claim in `test_claims.csv`. | |
| **Submission CSV:** `ClaimID,FraudLabel` | |
| **Evaluation Metric:** Macro-F1. | |
| ## Notes & Tips | |
| - Only the described columns are provided. Participants must infer from provided text, images, or PDFs. | |
| - Ensure submissions strictly adhere to the specified CSV formats. | |
| Good luck! | |