All first-party physical damage claims will be filtered by claim handlers and some of them will be referred to the fraud detection team. Your data is a sample of these referred claims from 2015 to 2016. There are two datasets, **train** and **test**. There is no fraud indicator in the test data. Your work is to build a model on the training data and apply your model to predict the fraud indicator for each claim in the test data. 

## Files

*   **train.csv** - the training set
*   **test.csv** - the test set on which you will make your predictions
*   **sample_submission.csv** - a sample submission file in the correct format

## Variable Descriptions

* `claim_number` - Claim ID (cannot be used in model)
* `age_of_driver` - Age of driver
* `gender` - Gender of driver
* `marital_status` - Marital status of driver
* `safty_rating` - Safety rating index of driver
* `annual_income` - Annual income of driver
* `high_education_ind` - Driver’s high education index
* `address_change_ind` - Whether or not the driver changed living address in past 1 year
* `living_status` - Driver’s living status, own or rent
* `zip_code` - Driver’s living address zipcode
* `claim_date` - Date of first notice of claim
* `claim_day_of_week` - Day of week of first notice of claim
* `accident_site` - Accident location, highway, parking lot or local
* `past_num_of_claims` - Number of claims the driver reported in past 5 years
* `witness_present_ind` - Witness indicator of the claim
* `liab_prct` - Liability percentage of the claim 
* `channel` - The channel of purchasing policy 
* `policy_report_filed_ind` - Policy report filed indicator
* `claim_est_payout` - Estimated claim payout
* `age_of_vehicle` - Age of first party vehicle
* `vehicle_category` - Category of first party vehicle
* `vehicle_price` - Price of first party vehicle
* `vehicle_color` - Color of first party vehicle
* `vehicle_weight` - Weight of first party vehicle
* `fraud` - **Fraud indicator (0=no, 1=yes). This is the response variable.**