From the course: Data Science Methodologies: Making Business Sense

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Data mining methodologies

Data mining methodologies

- [Instructor] Data mining methodologies have been around for more than 20 years. CRISP-DM and KDD were introduced around 1996 and form the core of other methodologies. CRISP-DM stands for Cross-Industry Standard Process for Data Mining, and is one of the most widely applied methodologies. It was developed by a consortium of companies in Europe and was formally published in the year 2000. Later, SEMMA was introduced by SAS Institute, ASUM-DM DM by IBM and more recently, TDSP by Microsoft. In this lesson, we will take a look at CRISP-DM. CRISP-DM is an iterative methodology that has six phases starting with business understanding and then data understanding, data preparation, modeling, evaluation, and deployment. Note the arrows going in reverse direction between some phases. For example, from data understanding to business understanding, modeling to data preparation, and from evaluation to business understanding.…

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