Coding questions · Voleon

Pre-process Financial Data for Linear Regression Modeling

This question evaluates data preprocessing and feature engineering skills—handling missing values, winsorizing outliers, standardizing predictors, and assembling X/y datasets using Python/Pandas within the Data Manipulation (SQL/Python) domain.

market_data

+------------+----------+----------+--------+
| date       | feature1 | feature2 | target |
+------------+----------+----------+--------+
| 2024-01-02 | 1.23     | 0.34     | 0.05   |
| 2024-01-03 | 1.25     | 0.30     | -0.01  |
| 2024-01-04 | 1.20     | 0.28     | 0.02   |
| 2024-01-05 | 1.18     | 0.27     | -0.03  |
+------------+----------+----------+--------+

Scenario

Voleon DS tech round: pre-processing a financial time-series dataset before modeling.

Question

Using the table below, write Python/Pandas code to clean nulls, winsorize extreme values at the 1st/99th percentiles, standardize predictors, and create an X, y pair ready for linear regression.

Hints

Focus on dataframe operations: dropna, clip, StandardScaler, and separate features/target.