{"id":3402,"date":"2023-08-31T10:59:22","date_gmt":"2023-08-31T10:59:22","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/market-basket-analysis-using-r-neural-designer\/"},"modified":"2025-09-10T13:31:17","modified_gmt":"2025-09-10T11:31:17","slug":"market-basket-analysis-using-r-neural-designer","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/market-basket-analysis-using-r-neural-designer\/","title":{"rendered":"Market basket analysis using machine learning"},"content":{"rendered":"

Market basket analysis is a well-known problem in data mining that can help businesses better understand customer behavior and optimize their operations.<\/p>\n

Recently, machine learning and neural networks have emerged as powerful tools for solving this problem.<\/p>\n

Here, we explore how these techniques can be used to perform market basket analysis and provide examples of their practical applications in the retail industry.<\/p>\n

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Contents<\/h3>\n
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  1. Introduction<\/a>.<\/li>\n
  2. Application type<\/a>.<\/li>\n
  3. Data set<\/a>.<\/li>\n
  4. Neural network<\/a>.<\/li>\n
  5. Model deployment<\/a>.<\/li>\n<\/ol>\n

    We use the data science and machine learning platform\u00a0Neural Designer<\/a>\u00a0to\u00a0solve this example. To follow it step by step, you can use the free trial<\/a>.<\/p>\n<\/section>\n

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    Introduction<\/h2>\n

    In this blog post, we’ll explore how businesses can identify which products are commonly purchased together by analyzing transactional data and leverage this insight to enhance product recommendations, marketing strategies, and store layout.<\/p>\n

    Machine learning algorithms have made market basket analysis more accessible than ever, allowing retailers to extract valuable insights from large datasets in real-time.<\/p>\n

    In this example, we’ll see how neural networks are a powerful tool that can be used to discover latent patterns in transactional data and generate meaningful rules that allow us to create practical applications of market basket analysis in the retail industry.<\/p>\n<\/section>\n

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    Application type<\/h2>\n

    In this application type, the number of outputs is the same as the number of inputs. Therefore, this is an\u00a0auto-association<\/a>\u00a0problem.<\/p>\n

    Auto association, specifically in the context of market basket analysis, refers to a technique used to identify patterns in customer purchase behavior by analyzing the co-occurrence of items in transactions.<\/p>\n

    The idea behind the auto association is to build a model from the associations between different items that tend to be purchased together, then use this knowledge to make recommendations or optimize product placement in a store. This is the primary goal of this example.<\/p>\n<\/section>\n

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    Data set<\/h2>\n

    The first step is to prepare the data set<\/a>, which is the source of information for this auto association problem.
    \nIt is composed of:<\/p>\n