{"id":3526,"date":"2023-08-31T11:12:58","date_gmt":"2023-08-31T11:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/vehicle-insurances\/"},"modified":"2025-09-17T12:18:37","modified_gmt":"2025-09-17T10:18:37","slug":"vehicle-insurances","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/vehicle-insurances\/","title":{"rendered":"Target customers for a vehicle insurance using machine learning"},"content":{"rendered":"
<\/section>\n

An insurance company that already offers health insurance now wants to target customers likely to be interested in vehicle insurance.<\/p>\n

To do this, we will build a machine learning model that predicts which customers are most likely to show interest.<\/p>\n

Customer targeting<\/a> involves identifying individuals who are more likely to be interested in a specific product or service.<\/p>\n

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

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Contents<\/span><\/h2>\n<\/section>\n<\/section>\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
  8. Tutorial video<\/a>.<\/li>\n<\/ol>\n<\/section>\n
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    1. Application type<\/h2>\n

    This project involves classification<\/a> as the predictor variable is binary (interested or not interested).<\/p>\n

    The goal is to create a model to obtain the probability of interest as a function of customer features.<\/p>\n<\/section>\n

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    2. Data set<\/h2>\n

    The data set contains information to create our model. We need to configure three things:<\/p>\n