Hasty mistakes to be avoided by Novice Machine Learning Engineer

During any ML project it’s very important to perform a bunch of activities before jumping in to the Algorithms. Understanding the Business Purpose; Feature- analysis, selections, scaling and engineering; outlier treatment; Exploratory Data Analysis to mention a few.

In this article, I will dwell more on importance of business purpose and feature selections. We can discuss on other topics in different articles.

There is no point in building any ML model, without understanding why is it needed. Data Scientists have to understand very clearly, what business problem they are addressing. This will help in designing a better model.

Real-time datasets usually have huge number of features. I have seen most of the novice ML engineers directly jump into algorithms considering ‘all’ the features and check the accuracy. I have even seen the ID numbers (without any relevance) were used as features, which is really silly from business point of view! We know not all of the features have equal importance on the target variable. It is very important to carefully select features which are needed for building the better model.

Following could be the reason for a novice ML engineers to be in such haste to jump into algorithms before important and prerequisites activities.

We have discussed behavioral aspect of a amateur ML engineer where they hastily jump into algorithms and perform trial and error tuning to improve the results. Instead, they should be focusing on many important prerequisites steps. Importance Understanding Business Purpose and Feature Selection were discussed in this articles.

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