The problem
Before a model can be trusted, its data needs to be understood. Applied ML Lab is a growing workspace for practicing that process on real datasets: inspect the raw records, investigate suspicious values, document decisions, and make the analysis repeatable.
Current work
The repository currently contains a data-exploration module and a data-cleaning module. The exploration module has a download script and a notebook; the cleaning module has a separate notebook. Its learning tracker marks the first exploration module complete and the cleaning module in progress.
| Stage | Current scope |
|---|---|
| Exploration | Inspect columns, types, distributions, timestamps, and missing data |
| Cleaning | Investigate duplicates, malformed values, and consistency issues |
| Modeling | A later stage in the learning roadmap |
| Serving and deployment | Future engineering work |
Method
The focus is on explaining data decisions rather than applying transformations blindly. A repeatable workflow starts with the source data, identifies a concrete quality issue, records the decision, and checks the resulting dataset.
The code and notebooks are the evidence for the current stage. Predictive models, a production API, and cloud infrastructure are roadmap items rather than completed deliverables.
Evaluation and limits
There is no predictive model score to report for this lab yet. At this stage, useful evidence is a reproducible analysis, explicit data-quality checks, and documented cleaning choices.
Future modeling reports should state the target, dataset version, train/validation boundaries, baseline, metric, and limitations. A plausible-looking score without that context would not demonstrate a reliable model.
What I am learning
- How exploratory analysis turns unfamiliar records into a concrete problem.
- Why data quality and leakage affect the validity of later model results.
- How to keep notebooks understandable and rerunnable.
- How to distinguish an experiment from a production pipeline.
Explore the work
See the source repository and its learning tracker. The tracker separates current deliverables from the longer roadmap.