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In today’s reality, data gathered by a company is a fundamental source of information for any business. Unfortunately, it is not that easy to drive valuable insights from it.
Problems with which all data scientists are dealing are the amount of data and its structure. Data has no value unless we process it. To do so, we need big data software that will help us in transforming and analyzing data.
It is very difficult to find a concise article containing a comprehensive guide to implement a Machine Learning or Data Science project. There are many online articles that provide detailed information on how we need to implement parts of a Machine Learning/Data Science project. Sometimes, companies only need high-level steps that show a clear overview.
A lot of Data Science project leads today forget about CRISP-DM, which is Cross-industry standard process for data mining created in 1996. In 2015, IBM released a new methodology called Analytics Solutions Unified Method for Data Mining/Predictive Analytics (also known as ASUM-DM) which refines and extends CRISP-DM.[1]