Common mistakes when carrying out machine learning and data science

This is part two of this series, find part one here - How to build a data science project from scratch. After scraping or getting the data, there are many steps to accomplish before applying a machine learning model. You need to visualize each of the variables to see distributions, find the outliers, and understand why there are such outliers. What can you do with missing values in certain features? What would be the best way to convert categorical features into numerical ones?