{"id":9189,"date":"2023-01-11T16:57:32","date_gmt":"2023-01-11T21:57:32","guid":{"rendered":"https:\/\/blogs.mathworks.com\/student-lounge\/?p=9189"},"modified":"2026-03-09T08:15:25","modified_gmt":"2026-03-09T12:15:25","slug":"weather-forecasting-in-matlab-for-wids-datathon-2023","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/student-lounge\/2023\/01\/11\/weather-forecasting-in-matlab-for-wids-datathon-2023\/","title":{"rendered":"Weather Forecasting in MATLAB for the WiDS Datathon 2023"},"content":{"rendered":"
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In today’s blog, Grace Woolson<\/a> gives us an insight into how you can get started with using Machine Learning and MATLAB for Weather Forecasting to take on the WiDS Datathon 2023 challenge. Over to you Grace..<\/div>\n

Introduction<\/h2>\n
Today, I’m going to show an example of how you can use MATLAB for the WiDS Datathon 2023<\/a>. This year’s challenge tasks participants with creating a model that can predict long-term temperature forecasts, which can help communities adapt to extreme weather events often caused by climate change. WiDS participants will submit their forecasts on Kaggle<\/a>. This tutorial will walk through the following steps of the model-making process:<\/div>\n
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  1. Importing a Tabular Dataset<\/a><\/li>\n
  2. Preprocessing Data<\/a><\/li>\n
  3. Training and Evaluating a Machine Learning Model<\/a><\/li>\n
  4. Making New Predictions and Exporting Predictions<\/a><\/li>\n<\/ol>\n
    MathWorks is happy to support participants of the Women in Data Science Datathon 2023 by providing complimentary MATLAB licenses, tutorials, workshops, and additional resources. To request complimentary licenses for you and your teammates, go to this <\/span>MathWorks site<\/span><\/a>, click the \u201cRequest Software\u201d button, and fill out the software request form.<\/span><\/div>\n
    To register for the competition and access the dataset, go to the Kaggle<\/a> page, sign-in or register for an account, and click the ‘Join Competition’ button. By accepting the rules for the competition, you will be able to download the challenge datasets available on the ‘Data’ tab.<\/div>\n

    Import Data<\/h2>\n
    First, we need to bring the training data into the MATLAB workspace. For this tutorial, I will be using a subset of the overall challenge dataset, so the files shown below will differ from the ones you are provided. The datasets I will be using are:<\/div>\n