Upload your data
Start with CSV, Excel, or Parquet files from the work your team already tracks.
No-code machine learning for business data
From business file to model-backed insight
Upload CSV, Excel, or Parquet data, describe what you want to understand, and use SaborData to review analysis setup, model results, metrics, plots, and predictions without writing code.
Use results to compare patterns and support decisions; SaborData does not guarantee business outcomes.
Workflow
How SaborData works
SaborData keeps the path from file to analysis visible, so business teams can understand each step before they use results.
Describe the goal
Use a plain-language goal to say what you want to understand, forecast, classify, or segment.
Review the analysis setup
SaborData reviews your columns and prepares a structured analysis setup for you to confirm.
Run the analyzer
The real app runs the analyzer, trains models, and prepares metrics and plots for review.
Review results and predict
Compare model results, then use the trained model for predictions when the results support your decision process.
Supported analysis
Designed for tabular business data
Bring familiar business data formats and choose analysis types through plain examples instead of machine learning code.
Data formats
- CSV
- Excel
- Parquet
Analysis types
- regression — forecast a numeric value
- classification — classify an outcome
- time series — analyze change over time
- clustering — segment similar records
Example results
See the kind of output SaborData produces
These illustrative charts show the analysis types above in action — forecasts, classifications, segments, trends, distributions, comparisons, and correlations rendered from sample business data.
Demand forecast
Churn classification
Customer segments
Sales trend
Category breakdown
Distribution analysis
Revenue composition
Feature correlation
Spread & outliers
Use cases
Use cases for everyday business data
Forecast demand or revenue
Use historical rows to estimate what may happen next month or next period.
Classify customer outcomes
Review likely churn, renewal, or lead outcomes from the columns you already collect.
Segment accounts or products
Group similar records by behavior, usage, spend, or operational patterns.
Spot operational trends
Explore changes across time so teams can compare patterns before acting.
Start in the app
Start with the data files your team already has
Review the setup and results before using them to support decisions.