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Pre-process Financial Data for Linear Regression Modeling

VoleonData Scientist · Technical Screen
MediumData ScientistTechnical ScreenData Manipulation (SQL/Python)

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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.