Upload your dataset. DataForge audits it in under 2 seconds — scoring quality 0–100 and surfacing the exact issues that will break your training run before you waste a single GPU hour.
No signup required · Runs in your browser · Your data never leaves your machine
$ dataforge audit job_salary_prediction_dataset.csv
→ Parsing 250,000 rows × 10 columns...
→ Running quality checks + agentic analysis...
✗ experience_years ↔ salary: correlation 0.85 (potential leakage)
⚠ education_level: outliers detected — ['PhD', 'Master's'] z-score > 2
⚠ industry: frequency imbalance — ['Technology', 'Finance'] > 10%
✓ No exact duplicates
✓ No missing values
Quality Score: 80/100 (Grade B)
Completed in 1.2s · 250k rows processed
You spent 6 hours debugging a training run.
It was a class imbalance in a column you never checked.
DataForge catches it in 2 seconds.
Epoch 1/50 — loss: 1.847 — val_acc: 0.521
Epoch 5/50 — loss: 1.841 — val_acc: 0.519
Epoch 20/50 — loss: 1.843 — val_acc: 0.521
Epoch 50/50 — loss: 1.847 — val_acc: 0.522
Training complete. Model accuracy: 52.2%
A quality score, ranked issues, and the exact training impact of each problem.
Dataset Quality Audit
Correlation coefficient: 0.85 with target (salary). Model may memorize this relationship instead of generalizing.
boltConsider feature selection or target encoding.
Values with z-score > 2: ['PhD', 'Master's'] — may indicate data entry inconsistency.
boltReview and standardize categorical values.
['Technology', 'Finance'] account for > 10% each. Underrepresented categories may affect generalization.
boltConsider stratified sampling or oversampling.
Auto Visualizations
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Drop a file. Parsed entirely in your browser using PapaParse. Nothing sent to a server — your data stays on your machine.
→ Handles up to 250k+ rows
Static checks + an agentic reasoning layer run in parallel. Duplicates, imbalance, correlations, outliers, missing values — detected automatically.
→ Completes in under 2 seconds
Quality score 0–100, severity-ranked issue list, and a one-line training impact explanation for each problem. No ambiguity.
→ No more "why did training fail"
So you don't debug it at 2am.
Gini impurity on label/class/target columns. Exact majority:minority ratio.
Training impact: A 37x imbalance means your model predicts the majority class 97% of the time. Accuracy looks fine. Model is useless.
Pearson correlation between numeric columns and likely target. Flags coefficients > 0.8.
Training impact: High correlation = potential data leakage. Model memorizes the shortcut instead of learning the pattern.
Full row hash via JSON.stringify. Zero approximation.
Training impact: Duplicates in train that appear in eval inflate metrics without reflecting real model capability.
Pairwise column similarity ≥ 90% across sampled rows. Catches what exact matching misses.
Training impact: Synthetic and scraped datasets are full of these. They pad size without adding signal.
Catches null, undefined, "NA", "NaN", nan (string). Per-column % with severity tiers.
Training impact: 40%+ missing in a feature column makes it unusable. Silently imputed, it actively degrades predictions.
Z-score > 2 on numeric and categorical columns. Named outlier values surfaced.
Training impact: Outliers skew learned distributions and distort feature scaling. Often data entry errors in disguise.
Separate from null detection. Empty string "" is not null but breaks pipelines identically.
Training impact: Tokenizers and encoders treat "" as valid input. These are invisible missing values.
Per-column numeric vs string ratio. Flags mostly-numeric columns with string outliers.
Training impact: One string in a numeric column forces pandas to cast the entire column to object. Silent pipeline break.
Core parsing, analysis, and visualization happens locally via your browser's JavaScript engine. This ensures immediate feedback and a high degree of privacy for your datasets.
If you're working with sensitive research data or proprietary training sets, DataForge provides the localized speed and security you need for professional model development.
# You write this every time, for every dataset
df.isnull().sum()
df.duplicated().sum()
df['label'].value_counts()
df.corr()
# Still no quality score
# Still no training impact explanation
# Still 20 min per dataset
# You: drop a CSV.
Quality Score: 80/100 (Grade B)
🔴 experience_years: correlation 0.85
🟡 education_level: outliers detected
🟢 No duplicates · No missing values
# Done. 1.2 seconds.
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