Build a baseline linear regression pipeline
Overview
This question evaluates a candidate's ability to construct a leakage-safe preprocessing and modeling pipeline for tabular data, including imputation, categorical encoding, feature scaling, ordinary least squares linear regression, and interpretation of coefficients and performance metrics.
Citadel
Aug 8, 2025, 12:00 AM
Data Scientist
Technical Screen
Machine Learning
0
0
Task: Baseline Linear Regression Pipeline (Python)
Context
You are given a tabular dataset in a pandas DataFrame df. The goal is to predict a continuous target column target. Build a leakage-safe baseline linear regression pipeline and report performance and coefficients.
Requirements
- Split the data into train/validation sets (e.g., 80/20) using a fixed random seed.
- Avoid data leakage: fit imputers/encoders/scalers only on training data by using a single sklearn Pipeline.
-
Preprocess features:
- Impute missing values: median for numeric, most_frequent for categorical.
- One-hot encode categorical features (drop one level to avoid multicollinearity; ignore unknown categories at validation).
- Scale numeric features (standardization).
- Train an ordinary least squares linear regression model.
- Evaluate on the validation set using RMSE and R^2.
-
Output:
- Fitted coefficients with feature names (and intercept).
- Key metrics (RMSE, R^2).
- A brief interpretation of results.
Assumptions
- DataFrame: df with target column named target; all remaining columns are features.
Deliverable
Provide clean, runnable Python code plus a short interpretation of the metrics and coefficients.
Solution
Login required
Show