Build a Visit Duration Prediction Model with scikit learn
Group: Use Case
Product Category: AI Engineering
Sub Category: ML Engineer
Solution Available
Source code, datasets, and other related files for the solution will be provided.
Prerequisites
About this Product
Healthcare practices over-book appointments into fixed 15-minute slots, causing visits to overrun and waiting rooms to back up.
What you build
- Feature pipeline: Time, age band and interaction features inside a scikit-learn Pipeline with a leakage guard.
- Model selection: Cross-validate four candidates (baseline, ridge, random forest, gradient boosting) and select the best.
- Batch CLI: Validate tomorrow's schedule and score with the approved model to write a plan with slot recommendations.
Skills you practise
- Feature engineering: Creating time-based and categorical features while detecting and guarding against data leakage.
- Cross validation: Patient-grouped folds to prevent the same patient appearing in both training and validation.
- Model selection: Comparing candidates with reproducible metrics and applying promotion gates.
- Metrics: MAE, RMSE, R2, within-5-minute rate and improvement over baseline.
Outcome
- Portfolio ready: A training and batch inference workflow you can explain in interviews and publish on GitHub.
- Reproducible: Identical results with seeded data, configuration via .env and run records documenting every decision.
Interview Q&A Included
1 practice set bundled with this lab
-
Visit Duration Prediction and Batch Scoring Assessment
Category: AI Engineering Subcategory: ML Engineer 41 questionsThis assessment checks what you learned while building a visit duration model for a family practice, from raw rows to a batch plan of recommended appointment slots. Questions are grouped …
Resources
The complete brief for the visit duration model and nightly slot planner you build: the problem, the rules and how success is measured.
- The Harrowgate Family Health problem: flat fifteen minute slots, overrunning visits and a backed up waiting room
- Fifteen numbered business and technical rules, from data minimisation to the batch run controls
- Sample training and schedule rows, including the invalid ones your validator must reject
- The five step workflow from synthetic data to a validated slot plan, with the expected files and metrics
- Seven edge cases and eight acceptance criteria that define when the lab is complete
- Skill mapping, the pinned technology stack, the project structure and the deliverables you hand in
Topics
Skills
Languages
English
Level
Intermediate
Business Domain
Healthcare and Patient Management
Project Mentors
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