What is Feature Engineering? - A Tutorial from Mohamed Salem Jedidi
Getting started · 5 Apr 2019, 12:09 · 2 mins read ·

“At the end of the day, some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used.”

-Dr. Pedro Domingos

What is feature engineering?

Feature engineering is the process of using domain knowledge to choose and transform the variables that will feed into your machine learning model. The process involves a combination of understanding the problem, data analysis, and applying good judgement. Feature Engineering is as much an art as it is a science.

Feature engineering happens prior to modeling, and is an essential part of building a machine learning solution. Appropriate well-designed features are often the deciding factor of the performance of your final algorithm. For this reason, data scientists often spend 70%-80% of their time on the pre-modelling phase, which largely consists of feature engineering.

Nairobi Traffic Jam Challenge

Mohamed Salem Jedidi, the winner of the Nairobi Traffic Jam Challenge, has prepared a tutorial on feature engineering for the Zindi community to learn from!

He explains the approach he took that helped him win the Nairobi Traffic Challenge and the target encoding technique he uses to transform categorical data into numerical variables. Here it is!

Please email Zindi at zindi@zindi.africa if you have a tutorial you would like to share with the community.

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