Missing values
Review incomplete fields and missingness patterns that may require cleanup or explanation.
CSV Data Quality Checker
Check missing values, risky columns, schema signals, and readiness before reporting, sharing, cleanup, or AutoML - directly in your browser.
Your CSV stays in your browser. No upload is required for the browser-local check.
Why check CSV quality first?
CSV files often look usable before hidden issues appear. Missing values, identifier-like columns, inconsistent types, high-cardinality fields, and small samples can affect reports, cleanup work, and machine-learning workflows.
MLdeck Data Quality gives non-technical users a readable first review before deeper work. It focuses on browser-local profile signals, practical recommendations, and clear limits rather than overconfident claims.
What the checker reviews
Identifier-like columns
Surface column names that may indicate IDs or keys that should be reviewed before reporting or AutoML.
High-cardinality fields
Flag categorical/text fields with many distinct values when the row count is large enough to judge.
Constant columns
Review columns that appear to add little analytical value, while avoiding misleading warnings for tiny samples.
Schema/type signals
Inspect numeric, categorical, text, and date-like signals in a readable column summary.
Small-sample limits
Show when a file has too few rows for reliable constant-column or cardinality checks.
Readiness before AutoML
Use quality signals to decide whether the CSV is ready for reporting, cleanup, sharing, or model training.
Privacy-first CSV review
The Data Quality workflow is designed for browser-local CSV review. The file is not uploaded for the check, and the PDF report is generated in the browser. Results are based on profile signals and do not certify correctness, compliance, privacy status, or production readiness.
Demo flow
This static demo uses synthetic labels only. It does not process data on a server.
Upload or try a sample CSV
Start from the browser-local Data Quality module and choose a CSV file on your device.
Review readiness signals
See a profile summary, quality score, limited-sample notes, and issue recommendations.
Export a readable PDF report
Signed-in free accounts can export a browser-generated PDF report without uploading the CSV.
Mock result preview
Selected file: synthetic_retail_sample.csv
Readiness: Needs attention - review missing values and identifier-like columns.
Issues: Missing values, customer_id review, small-sample limitation when too few rows are present.
PDF: Generated in the browser for signed-in free accounts.
Plan positioning
Free browser check
Upload a CSV, review the browser-local profile, see a quality score, inspect issues, and read recommendations.
Free account
Export a cleaner browser-local PDF report. The report is generated in your browser and your CSV is not uploaded.
Planned Pro features
Larger-file workflows, branded reports, batch CSV checks, and team-ready report templates may be added later.
Data Quality before AutoML
Start with Data Quality when the question is whether the CSV is usable. Move to AutoML when the goal is to train, compare, validate, and export machine-learning models.
CSV Data Quality Checker FAQ
What is a CSV data quality checker?
It reviews a CSV for profile-based signals such as missing values, schema hints, identifier-like columns, high-cardinality fields, constant columns, and readiness limits.
Does MLdeck upload my CSV?
No upload is required for the browser-local check. Your CSV stays in your browser during the Data Quality review.
What problems can it detect?
It can surface missingness, identifier-like names, high-cardinality categorical fields, constant columns, schema/type signals, and small-sample limitations.
Is the report a certification?
No. It is a readiness review and does not certify correctness, compliance, privacy status, or production readiness.
Should I check data quality before AutoML?
Yes. A quick data-quality review can reveal issues to clean or document before model training.
Can I export a PDF report?
Yes. PDF export is available for signed-in free accounts when the feature is enabled.
Do I need machine-learning knowledge?
No. The review is designed for readable quality signals and next steps before reporting, sharing, cleanup, or AutoML.
Start a browser-local CSV quality review
Use MLdeck Data Quality to review CSV readiness signals first, then continue to AutoML only if model training is the right next step.