# Predict missing values

The **Predict missing values** task predicts the values of empty cells using all
the cells in the sheet. See
[case #1 of the tutorial](tutorial)
for a complete example.

This task is suitable for recovering data but also for predicting valuable and
hard-to-obtain information. For example, if a column contains how much each of
your clients spends on your product, you can use this task to predict how
much any potential new clients will spend (before they do so). This way, you can
better target those large future clients.

Use this task as follows:

1.  Make sure you have a column with some missing values. Not all values should
    be missing. The more non-missing values you have, the better will be the
    prediction of the missing values. There should also be at least one other
    column in the sheet. See the [formatting](sheet_format) page to make sure
    your sheet is well formatted.
2.  In the "target column" section, select the column with missing values. Note
    that other columns can also contain missing values.
3.  **(Optional, advanced)** Remove some source columns. In most cases, leaving
    all the source columns will work best.
4.  **(Optional, advanced)** Change the learning algorithm. Gradient Boosted
    Trees and Random Forests are both excellent on tabular data. The decision
    tree algorithm is more interpretable.
5.  Click the "Predict" button.

A certain number of new columns will be created:

-   "pred:[target column]" is the predicted value for the target column.
-   "pred:Conf.[target column]" is the confidence (between 0% and 100%) of the
    prediction. The confidence is only available for classification objectives.

If you change values in the sheet, or if you add new rows or columns, press
"Predict" again to update the predicted value.

## How are missing values predicted?

To predict missing values, Simple ML trains a machine learning model using the
examples (rows) that don't have missing values in the target column. This model is then used to make prediction for
the missing values.