Description
Machine Learning Analysis on Public Data: Basic
A full machine learning analysis built on open data, from sourcing to interpretation, for researchers without their own dataset. Delivered in 3 weeks.
The problems that undermine a public-data machine learning study
- The dataset you cannot find. Public indicators sit across World Bank, IMF and national agency portals with different codes, years and country names.
- The merge that quietly breaks. Mismatched country names, missing years and inconsistent units corrupt a panel before modeling starts.
- The model nobody can explain. A random forest or XGBoost result without interpretation will not satisfy a reviewer who asks which variables matter.
- The score that is not validated. Reporting one train-test split instead of cross-validation and model comparison makes performance claims weak.
- The analysis you cannot reproduce. Without documented sources and code, no one, including you, can rerun it later.
What’s Included
- Public data sourcing from World Bank, IMF, national statistics or comparable open databases
- Data cleaning and variable construction
- Multi-algorithm modeling: regression, random forest, XGBoost and others as appropriate
- Cross-validation and model comparison
- SHAP interpretability analysis
- Results visualization
- Data source and extraction documentation
- Model performance comparison charts and SHAP importance plots
- Complete analysis report
- R/Python analysis code
Best For
- Researchers without their own primary dataset
- Prediction or classification questions answerable from public indicators
- Economics, policy and social science applications
Timeline
Standard delivery: 3 weeks.
Compare the two tiers
Tiers differ by data construction, algorithm comparison, delivery time and price. Compare both tiers: Basic and Standard.
| What you get | Basic | Standard |
|---|---|---|
| Public data sourcing and documentation | Yes | Yes |
| Data cleaning and variable construction | Yes | Yes |
| Multi-algorithm modeling | Yes | Yes |
| Cross-validation and model comparison | Yes | Yes |
| SHAP interpretability analysis | Yes | Yes |
| Performance charts and SHAP importance plots | Yes | Yes |
| Complete analysis report | Yes | Yes |
| R/Python analysis code | Yes | Yes |
| Data construction | Included | Multi-source |
| Algorithm comparison | Included | Expanded |
| Delivery | 3 weeks | 4 weeks |
| Price | $1,200 | $1,700 |
Editverse compared with a typical analysis service
Machine learning results are only useful if they can be checked. Both tiers include the documentation to do so.
| What matters | Typical analysis service | Editverse |
|---|---|---|
| Documents the data sources | Varies | Data source and extraction documentation on both tiers |
| Supplies the analysis code | Often withheld or extra cost | R/Python code on both tiers |
| Explains which variables drive the model | Varies | SHAP interpretability analysis on both tiers |
How it works
- Brief. You describe your question and outcome variable.
- Source. We identify and document the public datasets.
- Model. Cleaning, variable construction, modeling and cross-validation.
- Interpret. SHAP analysis and results visualization.
- Deliver. You receive the report, charts and R/Python code.
Our Ethical Commitment
We analyse your question using public data. We do not alter source data or tune results to fit a conclusion. Authorship, ethics approval and data governance follow COPE, ICMJE and the policies of your institution and country, and remain with you.
Have a Question First?
Not sure which tier fits? Describe your question on our contact page and we will recommend one.
