Mastering Feature Engineering: Techniques for Better Model Performance.

4 Effective Techniques for Feature Engineering

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You are an engineer, right? You solve problems in your domain. That is what engineering is.

Then, what is feature engineering? — Imagine we have made a model with the features in the data, but it does not work well.

Why?—Maybe the data features are not good enough to make a model that is accurate.

This is a feature problem, right?

So, the way to fix this problem by making new and better features for machine learning models from the existing data is feature engineering.

The only goal of feature engineering is to improve the model’s performance.

However, feature engineering can also be challenging, time-consuming, and require domain knowledge.

Note: Do you want to improve your domain knowledge?
Read my eBook: “Domain Knowledge Handbook for Data Aspirants.”

In this article, I will share four interesting techniques and best practices for feature engineering that can help you in your data science projects.

I won’t bore you much with “what and when is it considered feature engineering?” or “why it is important.”