Design a house-price prediction workflow

Q: Design a house-price prediction workflow

This is a Machine Learning interview question from xAI for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

Predicting Home Sale Prices: End-to-End ML Design

Context

You have historical home-sale records with features such as lot area, year built, number of rooms, neighborhood, and sale date. You need to build a production-ready system that predicts sale price for new listings.

Task

Design an end-to-end approach that covers:

Problem framing and target transformation (e.g., log-price) and objective.
Data cleaning: missing-value imputation, outlier handling, and leakage risks.
Feature engineering for numeric, categorical, time, and location features; useful interactions.
Baselines and model choices: linear models, regularized regressions, tree-based methods, ensembles.
Evaluation protocol and metrics: RMSE, MAPE, and time-based cross-validation.
Hyperparameter tuning and error analysis.
Interpretability and fairness considerations.
Handling non-stationarity and market shifts: monitoring and retraining.
Deployment plan for a lightweight model under latency and memory constraints.

Design a house-price prediction workflow

Predicting Home Sale Prices: End-to-End ML Design

Context

Task

Solution (Locked)

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