Analysis Ready
Stop cleaning before every analysis
Standardize fields, handle missing markers, remove configured duplicates and export a typed dataset with readiness evidence.
- Clean column names
- Safe type conversion
- Quality score + report
EazyDataFix Studio · Public product preview
Messy files in.Decision-ready data out.
Clean, validate and prepare CSV or Excel data for analysis, machine learning and Power BI. Preview every proposed change before anything is applied.
Deterministic by default Complete change log AI does not control fixes
Analysis Ready workflow
Transparent preview · no black box
90 → 100
customer_upload.csv
3 rows shown| customer_id | name | salary | city |
|---|---|---|---|
| C-101 | Anika Rao | ₹72,000 | HYD |
| C-102 | Rahul | N/A | Hyderabad |
| C-102 | Rahul | N/A | hyderabad |
6 reviewable changes
Ready for analysis
2 rows shown| customer_id | name | salary | city |
|---|---|---|---|
| C-101 | Anika Rao | 72000 | Hyderabad |
| C-102 | Rahul | null | Hyderabad |
v1.4.0
Production engine
3.10–3.13
Python verified
CSV · Excel
Practical inputs
MIT
Open-source core
One engine · three outcomes
Prepare data for the work you are actually doing.
Generic cleaning is not enough. EazyDataFix applies different readiness checks for analysis, supervised machine learning and Power BI models.
ML Ready
Prepare features without silent leakage
Create train/test inputs with transformations fitted only on training data and a reusable preprocessing artifact.
- Target-aware preparation
- Leakage safeguards
- Reusable artifact
Power BI Ready
Give Power BI cleaner model inputs
Validate field types, keys and relationships, generate a date table and export documented model-ready files.
- Key validation
- Relationship checks
- CSV/Excel export pack
From upload to evidence
A workflow people can trust, not an “auto-fix” button.
EazyDataFix is designed around inspection. The customer sees the problem, the proposed action and the resulting dataset before exporting anything.
Try the workflow01
Bring a real file
Start with the CSV or Excel file already slowing down your reporting or analysis.
02
See what is wrong
Review missing values, duplicate records, unsafe types, inconsistent labels and model-readiness issues.
03
Approve every change
Accept or reject proposed corrections. Important data is never silently rewritten.
04
Export with evidence
Download clean data together with scores, warnings, validations and a complete change log.
Trust is a product feature
Deterministic first. AI optional and accountable.
Cleaning decisions come from reproducible rules—not a model guessing what your data should mean. Optional AI can explain evidence after the deterministic workflow, but it does not secretly rewrite source values.
Preview before applying
Protect identifiers and leading zeroes
Document every transformation
Keep the open-source engine inspectable
Built on an open-source Python engine
Use the Studio or own the code path.
import eazydatafix as edf
result = edf.powerbi_ready({
"sales": "sales.csv",
"customers": "customers.csv",
})
result.save("powerbi_output")Start with the file that is wasting your time today.
The public Studio preview runs locally in your browser and demonstrates the complete review workflow. Assisted team pilots add repeatable recipes and business-specific rules.