{"id":3418,"date":"2023-08-31T10:59:21","date_gmt":"2023-08-31T10:59:21","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/space-missions\/"},"modified":"2025-09-12T17:29:15","modified_gmt":"2025-09-12T15:29:15","slug":"space-missions","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/space-missions\/","title":{"rendered":"Predict failure of space flights using machine learning"},"content":{"rendered":"

In this example, we build a machine learning model to predict whether a space flight succeeds or fails based on variables related to the mission.<\/p>\n

Space missions have evolved greatly since the first launch in 1957.<\/p>\n

Today, they are carried out by governments, private companies, and international organizations, aiming to explore the solar system and beyond.<\/p>\n

These missions push the limits of science and technology while expanding human presence beyond Earth.<\/p>\n

This example is solved with Neural Designer<\/a>. To follow it step by step, you can use the free trial<\/a>.<\/p>\n

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Contents<\/h3>\n
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  1. Application type<\/a>.<\/li>\n
  2. Data set<\/a>.<\/li>\n
  3. Neural network<\/a>.<\/li>\n
  4. Training strategy<\/a>.<\/li>\n
  5. Model selection<\/a>.<\/li>\n
  6. Testing analysis<\/a>.<\/li>\n
  7. Model deployment<\/a>.<\/li>\n<\/ol>\n<\/section>\n
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    1. Application type<\/h2>\n

    The variable to be predicted is binary. Therefore, this is a classification<\/a> project.<\/p>\n

    The main goal is to model the state of the mission as a function of variables such as temperature, nature of the payload, payload target orbit, etc.<\/p>\n<\/section>\n

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    2. Dataset<\/h2>\n

    The first step is to prepare the dataset<\/a>, which is the source of information for the classification problem. It is composed of:<\/p>\n