Statistical analysis, made simple
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Train machine learning regressors to predict continuous outcomes with R², RMSE, and prediction plots.
Select a numeric outcome (Y) and numeric or categorical predictors, then pick a regressor and tune its settings.
First 5 rows of your dataset. Column type changes below update the Select Variables section and are sent to the backend when you run analysis.
Age | StudyHours | Salary |
|---|---|---|
| 23 | 4.5 | 42000 |
| 25 | 5.2 | 48000 |
| 22 | 6.0 | 51000 |
| 24 | 4.8 | 45000 |
| 26 | 5.5 | 50000 |
Numeric columns are filled with the mean (or median if skewed), categorical columns with the mode, and any column with more than 30% missing values is dropped.
Choose which columns missing-value handling applies to. Rows are only dropped or imputed based on the variables selected here.
Select at least one variable, or missing-value handling will use your analysis variables when you run the model.