Detect lets you leverage DataChat's powerful machine learning capabilities to analyze your data. With
Detect, you can identify:
- Cyclical trends in a column using a datetime column as a reference.
- Outliers in a numerical column.
- Outliers in a numerical dataset.
Detect works only on numerical columns. If any categorical or string columns are detected, you can choose to either keep them in the analysis or remove them. Removing them might make the analysis quicker.
Detect outliers in the columns <columns> (using the method <method>)
Detect outliers in the dataset <dataset> (using the method <method>)
Detect any cyclical trends in the column <column> using the column <datetime column>
Detect uses the following parameters:
column(required). The column in which to detect cyclical trends.
datetime column(required). The datetime column to use as a reference when detecting cyclical trends in the columns above.
columns(required). A comma-separated list of numeric columns to analyze for outliers.
dataset(required). The dataset to analyze for outliers.
method(optional). The method to use when detecting outliers. By default, the isolation forest method is used.
If any outliers are detected, a success message appears in the chat history and a table appears in the display panel and becomes [dataset]_Outliers. The table includes the values that were identified as outliers, their score, and their ranking.
If any cyclical trends are detected, a success message appears in the chat history and a bar chart appears in the display panel. The chart illustrates the top five periods with a positive correlation and the confidence of each interval.
To detect cyclical trends in a column called "Temperature" using a column called "Date" as a reference, enter
Detect any cyclical trends in the column Temperature using the column Date.
To detect outliers in a column called "Age," enter
Detect outliers in the columns Age.
To detect outliers in the columns called "Age" and "TicketPrice," enter
Detect outliers in the columns Age, TicketPrice.