# Overview
The data has been split into two groups:

- training set (audit_train.csv)
- test set (audit_test.csv)

The training set should be used to build your machine learning models. For the training set, we provide the target outcome (also known as the “ground truth”) for each individual. Your model will be based on “features” like individual's occupation or income. You can also use feature engineering to create new features.

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each individual. 

## Data Dictionary

A data frame. In line with data mining terminology we refer to the rows of the data frame (or the observations) as entities. The columns are refered to as variables. The entities represent people in this case. We describe the variables here: 

    ID         - This is a unique identifier for each person.
    Age        - The age.
    Employment - The type of employment.
    Education  - The highest level of education.
    Marital    - Current marital status.
    Occupation - The type of occupation.
    Income     - The amount of income declared.
    Gender     - The persons gender.
    Deductions - Total amount of expenses that a person claims in their financial statement.
    Hours      - The average hours worked on a weekly basis.
    Accounts   - The main country in which the person has most of their money banked.
    Adjustment - This variable records the monetary amount of any adjustment to the person's financial claims as a result of a productive audit.
    Target     - The target variable for modelling (generally for classification modelling).