Predict Taxi Driver Classes
Boston Consulting Group● HardFULLTIMEINTERNOAProblem statement
Use the prepared training and test data to train a classification model and produce driver-class predictions with high accuracy.
FastPrep practice interpretation
For this exercise, assume the prepared inputs are train_data, validation_data, and test_data. Train a binary classifier using the labeled training and validation rows, then predict driver_class for every test row.
0represents A class.1represents B class and is the positive class.- Do not use
driver_idas a model feature. - You may choose any deterministic classification method available in the Pandas runtime.
Return exactly one column named driver_class, with predictions in the original test_data row order. The practice objective is to identify all positive rows while avoiding false positives; each published fixture has one exact expected label per test row.
Table schema
Use the same input data with any supported language. Open the Schema tab in the editor to see the generated SQL setup or Pandas DataFrames.
train_data
The labeled training split.
| Column | Type | Nullable | Description |
|---|---|---|---|
| driver_idPK | Integer | No | — |
| car_model | Integer | No | — |
| car_manufacture_year | Integer | No | — |
| days_since_inspection | Integer | No | — |
| age | Integer | No | — |
| experience | Integer | No | — |
| second_language | Integer | No | — |
| rating | Decimal | No | — |
| net_worth_of_tips | Decimal | No | — |
| number_of_rejected_rides | Integer | No | — |
| number_of_upvotes | Integer | No | — |
| number_of_complaints | Integer | No | — |
| number_of_incidents | Integer | No | — |
| driver_class | Integer | Yes | — |
validation_data
The labeled validation split.
| Column | Type | Nullable | Description |
|---|---|---|---|
| driver_idPK | Integer | No | — |
| car_model | Integer | No | — |
| car_manufacture_year | Integer | No | — |
| days_since_inspection | Integer | No | — |
| age | Integer | No | — |
| experience | Integer | No | — |
| second_language | Integer | No | — |
| rating | Decimal | No | — |
| net_worth_of_tips | Decimal | No | — |
| number_of_rejected_rides | Integer | No | — |
| number_of_upvotes | Integer | No | — |
| number_of_complaints | Integer | No | — |
| number_of_incidents | Integer | No | — |
| driver_class | Integer | Yes | — |
test_data
The unlabeled test split to classify.
| Column | Type | Nullable | Description |
|---|---|---|---|
| driver_idPK | Integer | No | — |
| car_model | Integer | No | — |
| car_manufacture_year | Integer | No | — |
| days_since_inspection | Integer | No | — |
| age | Integer | No | — |
| experience | Integer | No | — |
| second_language | Integer | No | — |
| rating | Decimal | No | — |
| net_worth_of_tips | Decimal | No | — |
| number_of_rejected_rides | Integer | No | — |
| number_of_upvotes | Integer | No | — |
| number_of_complaints | Integer | No | — |
| number_of_incidents | Integer | No | — |
| driver_class | Integer | Yes | — |
Expected result
Your query or function must return these columns.
| Column | Type | Nullable | Description |
|---|---|---|---|
| driver_class | Integer | No | — |
Row order: must match exactly. Numeric tolerance: 0.
Constraints
- Each input table is non-empty.
- Training and validation labels are
0or1, and both classes occur in the combined labeled data. - Test labels are null and must not be used.
- Every feature value is finite.
- All three tables have the same feature columns.
- The output has exactly one row per test row.