EXP-007 / Business Analytics / Published

Coffee Shop Profit Predictor

A business analytics workflow that uses SQLite feature engineering, model comparison, cross-validation, baseline checks, candidate-location scoring, and interpretable profit diagnostics for coffee-shop site selection.

EXP-007 / question

Technical question

Can SQL features and interpretable regression support site-selection decisions?

EXP-007 / method

Method and workflow

  1. Generate or load location-level business data.
  2. Create modeling features through SQL transformations.
  3. Train and compare regression models against simple baselines.
  4. Evaluate holdout metrics and cross-validation behavior.
  5. Score candidate shop locations and explain key profit drivers.
  6. Save outputs and diagnostics for review.
location data SQLite features model comparison validation diagnostics candidate scoring

EXP-007 / evidence

Evidence of work

Business framing

Predictions are translated into candidate-location ranking rather than only reporting a model score.

Model honesty

Baseline checks, CV, and residual diagnostics make performance easier to interpret.

Engineering

SQLite features and repeatable scripts make the workflow more reproducible than a notebook-only demo.

EXP-007 / stack

Technical stack

PythonpandasNumPySQLitescikit-learnmatplotlibjoblibunittestRuffBlackmypyGitHub Actions
Open repository ↗

EXP-007 / limitations

Limitations and honesty check

  • The data is synthetic or demo-oriented, so results should not be treated as real market advice.
  • Site selection would require lease terms, foot traffic, competition, local events, and operational constraints.
  • The model supports decision review; it should not replace business judgment.

EXP-007 / next

Next improvements

  • Add richer geospatial features and local competition signals.
  • Add scenario analysis for rent, labor, and demand shifts.
  • Add a small dashboard for candidate comparison.
  • Add more robust model cards and data documentation.