Dataset Analyzer — Stats & Quality Reporter
Upload a CSV, Excel, or JSON file to understand its structure, quality, and patterns. Get column profiles, data quality scores, duplicate detection, outlier analysis, and AI-powered insights — all in your browser.
How to Use Dataset Analyzer — Stats & Quality Reporter
How to Use the Dataset Analyzer
Step 1: Upload Your Dataset
Drag and drop or click to select a file in CSV, Excel (.xlsx/.xls), or JSON format. Files up to 20MB are supported.
Step 2: Review the Free Overview
Instantly see:
- Row and column count
- File size and format
- Inferred column types (string, number, date, boolean)
- A preview of the first 10 rows
No account required for this step.
Step 3: Run Full Analysis (5 Credits)
Click Analyze Dataset to unlock the Data Quality tab:
- Quality Score (0–100) — weighted across completeness, uniqueness, validity, consistency, and outliers
- Column Profiles — per-column stats including null %, unique values, min/max/mean, top values
- Duplicate Detection — exact row duplicates flagged and counted
- Outlier Detection — IQR-based outlier counts for numeric columns
- Issue List — prioritized list of data quality problems with severity labels
Step 4: Generate AI Insights (5 Credits)
Click Generate AI Insights on the AI Insights tab to get:
- What your dataset likely represents
- Key patterns and findings
- Quality risks to be aware of
- Cleaning and preprocessing recommendations
- Suggested analysis directions
Step 5: Download Report
Click Download Report to export a structured JSON file containing all analysis results and AI insights. Use this for documentation, sharing with teammates, or feeding into data pipelines.
Supported Formats
- CSV — auto-detects comma, semicolon, tab, and pipe delimiters
- Excel (.xlsx, .xls) — reads the first sheet via SheetJS
- JSON — supports arrays of objects; nested objects are flattened up to 3 levels deep
Tips
- For large files (>50,000 rows), analysis runs on a 5,000-row sample for speed
- Column type detection uses value patterns — a column named "email" with valid email values will be tagged as "Email"
- The quality score is directional, not absolute — a 70/100 may still be production-ready depending on your use case
- AI insights reference your actual column names and values — they are dataset-specific, not generic
Frequently Asked Questions
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