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Data Science: Predict Damage Costs of Weather Events

version 1.0.1 (14.3 MB) by Heather Gorr
Use machine learning to predict the damage costs of various storm events based on location, time of year, and type of event


Updated 18 Jul 2019

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The goal of this case study is to explore storm events in various locations in the United States and analyze the frequency and damage costs associated with different types of events. A machine learning model is used to predict the damage costs, based on historical data from 1980 - 2018. The calculations are then performed in an app, which can be shared as a web application.

This example also highlights techniques for preprocessing data in various forms (numeric, text, categorical, dates and times) and working with large data sets which do not fit into memory.

The example is used in the "Data Science with MATLAB" webinar series.

Cite As

Heather Gorr (2020). Data Science: Predict Damage Costs of Weather Events (, MATLAB Central File Exchange. Retrieved .

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Updated for Data Science w/ MATLAB webinar

MATLAB Release Compatibility
Created with R2019a
Compatible with any release
Platform Compatibility
Windows macOS Linux