# 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!