DataFit — Know if your data is ready before you train

About DataFit

Most data-quality tools stop at "your dataset has 18:1 class imbalance." That's a statistic, not an answer. DataFit connects the problem to its likely consequence for the ML task you're actually trying to do, and where useful, runs a lightweight experiment to show real evidence of the impact, rather than asking you to take its word for it.

DataFit never decides your data is "wrong" on your behalf. It surfaces unusual or risky patterns and helps you judge whether they matter, because an unusual reading might be completely legitimate, and no automated tool should quietly overwrite it.

/100 — needs investigation before training

Class imbalance — 18:1, minority class is 5.3% of data

Missingness concentrated in 2 features

No significant duplicates detected

How it works

CONNECT → ANALYZE → DETECT → SCORE → EXPLAIN → RECOMMEND

CONNECT →

Upload a CSV and tell DataFit what you're building: classification, regression, or general use.

ANALYZE →

DataFit checks for missing values, duplicates, outliers, imbalance, distribution issues, leakage indicators, and more. Ten checks run automatically, none of them altering your data.

DETECT →

Every finding is scored by how much it matters for your specific task, rolled up into a task-specific heuristic risk assessment out of 100.

EXPLAIN →

Each issue comes with a plain-language explanation of what it could mean for your model, not just what was detected, but why it matters.

RECOMMEND →

DataFit suggests what to investigate next: class weighting, resampling, a closer look at specific records, and can run an experiment to show whether a fix would genuinely help.