Data Science: Predict Damage Costs of Weather Events

Explore data and use machine learning to predict the damage costs of storm events based on location, time of year, and type of event
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更新 2021/5/21
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 - 2020. The calculations are then performed in an app, which can be shared as a web application.
This example also highlights techniques for cleaning 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.


Heather Gorr, PhD (2024). Data Science: Predict Damage Costs of Weather Events (, GitHub. 取得済み .

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作成: R2019a
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Included examples for Intro to MATLAB webinar


Link to GitHub


Included recent data, updated scripts to include Live Editor Tasks for data cleaning (available in R2019b)


Updated for Data Science w/ MATLAB webinar


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