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Lumen — Data Analysis Dashboard
A leak-proof split enforced before the model ever sees the data, so the accuracy score means what it says.
10/10 tests passingAutoML pipeline
- The problem
- Upload-your-data tools tend to give you charts nobody asked for, or a model whose accuracy score is meaningless because the pipeline leaked the answer into the training set.
- What I built
- Upload to dashboard in one step: KPIs, plain-English findings, anomaly detection with adjustable sensitivity, and a cross-validated AutoML pipeline built so it cannot leak. The cleaning steps export as runnable pandas.
- Where it is now
- The analysis engine is tested (10 of 10 passing), and every cleaning workflow reproducible outside the app.
Technical detail
End-to-end automated data-analysis web app turning any uploaded dataset into a dashboard with KPIs, plain-English insights, and charts. Dataset-agnostic anomaly detection using scikit-learn Isolation Forest with adjustable sensitivity. Leak-free AutoML pipeline with Pipeline + ColumnTransformer, cross-validated.
Pure-function analysis engine with full pytest coverage (10/10 passing). Reproducible cleaning workflows that export as runnable pandas code.
Built with
PythonStreamlitscikit-learnPandasPlotlypytest