Pre-process Financial Data for Linear Regression Modeling

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PracHub provides an interactive SQL console where you can write and test queries against real database schemas. Get instant feedback and compare your solution with the expected output.

Q: What difficulty level is this coding question?

This is a medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Technical Screen rounds at Voleon Group.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Voleon Group during technical interviews.

Question

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.

PracHub · Accepted Answer

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.

Quick Overview

Pre-process Financial Data for Linear Regression Modeling