Implementation of Data Mining in Shopping Cart Analysis using the Apriori Algorithm

Susy Rahmawati, Miftahul Nuril Silviyah, Nur Syifa’ul Husna
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Abstract

Market basket analysis is one of the techniques of knowledge mining used in a broad dataset or database to find a collection of items that are interwoven. Generally used in a sale, the most relevant shopping cart data is used. This methodology has been widely applied in different multinational or foreign industries and is very useful in consumer buying preferences. Technology advances change business trends dramatically, shifting customer demands require increased surgical accuracy of business. In this research, the writer wants to analyze the shopping cart using apriori algorithm, with a dataset from the Kaggle web. Using anaconda software features with the Python programming language is expected to create knowledge overwriting consumer buying patterns. In conclusion, this pattern can be used to support industry in managing its company activities.
利用Apriori算法实现购物车分析中的数据挖掘
市场购物篮分析是一种用于广泛数据集或数据库的知识挖掘技术,用于查找相互交织的项目集合。通常用于销售,使用最相关的购物车数据。这种方法已广泛应用于不同的跨国或外国工业,对消费者的购买偏好非常有用。技术进步极大地改变了业务趋势,不断变化的客户需求要求提高业务的精确度。在本研究中,作者想要使用apriori算法对购物车进行分析,数据集来自Kaggle web。使用蟒蛇软件特性和Python编程语言有望创建覆盖消费者购买模式的知识。总之,此模式可用于支持行业管理其公司活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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