Detecting and handling outliers

Detecting and handling outliers in data analysis and science projects is essential due to their potential to distort results and undermine data quality. Outliers can significantly impact predictive models, disrupt data visualizations, and violate assumptions made in the analysis.

Addressing outliers not only enhances the robustness of the analysis but also facilitates more accurate inferences, making the insights more interpretable. This is crucial for informed decision-making. Moreover, some industries have regulatory requirements mandating the detection and handling of outliers, adding an extra layer of importance to this process.

In this recipe, we’ll cover a few methods for detecting outliers as well as handling them.

Getting ready

Just like in the previous recipe, you need to install Plotly. Use the following command to install it in pip if you haven’t already:

>>> pip install plotly

You’d also need...