They say data is the new oil, but we don't use oil directly from its source. It has to be processed and cleaned before we use it for different purposes. The same applies to data, we don't use it directly from its source. It also has to be processed before you can use it for your next machine learning project!

This may be a challenge for beginners in machine learning and data science because data comes from different sources with different data types. Therefore you can not apply the same method of cleaning and processing to different types of data.

"Information can be extracted from data just as energy can be extracted from oil."- Adeola Adesina

You have to learn and apply methods depending on the data you have. Then you can get insight from it or use it for training in machine learning or deep learning algorithms.

After reading this article, you will know:

• What is feature engineering and feature selection
• Different methods to handle missing data in your dataset
• Different methods to handle continuous features
• Different methods to handle categorical features
• Different methods for feature selection

Let's get started! 🚀

What is Feature En