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# 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!
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