Complete Guide For Feature Engineering

This the continuation of my previous blog “<a href="https://medium.com/p/9130f9013aca#8b86-4e9841830828" rel="noopener">part1</a>”. In it i had started with Feature Transformation. In Feature Transformation there are four sub topics as you can see in the below picture: <img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*Cgk3tyh-VqArnHlazhZCig.png" style="height:571px; width:700px" /> I had discussed “missing value Imputation” in depth visually. Pls do visit it and then come here. <h1>1.2 Handling Categorical Variables/Features/Columns:</h1> After reading the sub heading the 1st question one may get is “what are categories?”. For example when you think about automobiles there are different types right! like bikes,autos,cars, lorries..so on. In a similar way if you find any categories in a column it is called as categorical varible. This the formal definitation: A categorical variable is a type of variable that represents data with distinct, separate categories or groups. Categorical variables can be further divided into nominal and ordinal categories: <ol> <li>Nominal Categorical Variable: This type of categorical variable has categories with no inherent order or ranking. For example, the color of cars (e.g., red, blue, green) is a nominal categorical variable because there’s no meaningful order among the colors.</li> <li>Ordinal Categorical Variable: This type of categorical variables has categoies with order or ranking among them.An example would be education levels (e.g., high school, bachelor’s degree, master’s degree), where there is an order but the difference between “high school” and “bachelor’s degree” is not necessarily the same as the difference between “bachelor’s degree” and “master’s degree.</li> </ol> <a href="https://medium.com/@kothapallijayanth521/complete-guide-for-feature-engineering-part-2-c62909a2dd43">Read More</a>

Complete Guide For Feature Engineering — part 2