Numerical vs. Categorical Data | Represent Your Dataset Correctly!

#ai #ml #datascience #aiexplained #artificialintelligence #categorical #numerical #python #computerscience #data 🔥 This video defines two main data types in tabular data: numerical and categorical. Numerical data represents numbers both discrete and continuous. Discrete data consists of whole numbers, often they represent counting things. Continuous data contains any numbers including decimals and fractions. Categorical data is the trickiest one. You may have either nominal (unordered) or ordinal (ordered) categories. In ordinal data categories are ordered, so you just need to map higher integers to categories higher in the hierarchy. While in the unordered (nominal) case, you need to use a technique called one-hot encoding. One-hot encoding defines a new binary variable for each category in the variable. It is important to note that one-hot encoding can result in a bunch of new columns, which can significantly expand the dimensionality (number of columns) of the data. You have to think twice before applying one-hot encoding directly to a variable that has many unique variables. More on this we will talk in the upcoming videos! 🔍 Key points covered: 0:00 - Introduction. 0:13 - Numerical data. 0:20 - Continuous data. 0:27 - Discrete data. 0:38 - Categorical data. 0:53 - Binary encoding. 1:07 - Non-binary case. 1:15 - Ordinal data. 1:30 - Nominal data. 1:42 - One-hot encoding. 2:08 - Subscribe to us! 🔔 Don't forget to like, subscribe, and hit the bell icon to stay updated with our latest videos! 🤖 Note that we use synthetic generations, such as AI-generated images and voices, to enhance the appeal and engagement of our content. 🌐 If you have any questions or topics you want us to cover, leave a comment below. Additionally, share with your thoughts about the content, how do you think we can make them better? Thanks for watching!