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In this article, we will talk about solving problems using data science and machine learning. Before we go into the technical details, lets answer an important question: why should we follow a process?

As engineers, we love solving problems and often want to jump straight into finding solutions. However, if we dont properly define the problems were working on, we can waste a lot of time and money on the wrong issues. Additionally, if we dont follow a structured approach and perform the right tasks in the right order, we risk inefficiency and failure. For instance, if we start modeling before cleaning and processing our data, we might end up with a poor-quality model, regardless of our efforts. The old saying garbage in, garbage out is particularly relevant here.

A systematic process is essential for organizing work, distributing responsibilities among team members, and ensuring that each step is completed properly. Various processes are available for data science projects, but in this article, we will focus on the most common one: CRISP-DM (Cross Industry Standard Process for Data Mining).

CRISP-DM was developed in 1996 by a group of European companies from different industries. It is a flexible, industry-neutral approach to data mining and machine learning projects. Even though it is over 25 years old, it remains the most widely used method in data science. Major corporations, including IBM, use CRISP-DM or versions of it.

See the article here:

Managing ML Projects: The CRISP-DM Process | by Mohsen Nabil | Jul, 2024 - DataDrivenInvestor