In this article, I will share with you some of the methods I studied during the last project led by Fiverr.
You'll get some ideas about the basic methods I've tried and the more complicated methods that get the best results - remove the 60% or more features while maintaining accuracy and achieving higher stability for our model. Sex. I will also share our improvements to the algorithm.
If you really want to avoid the embarrassing situation of your machine learning model producing garbage results, you need to understand the importance of "effective data preprocessing and feature engineering" as the title implies.
This article contains 3 parts: feature engineering, from raw data to features. Characteristics of good features. How to understand the data.