Ordinal Encoder with Python Machine Learning (Scikit-Learn)
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Are you ready to enhance your data preprocessing skills? 📊 Join us in this informative tutorial where we delve into the world of Ordinal Encoding using Python and the powerful Scikit-Learn library.
Whether you're a data analyst, machine learning enthusiast, or a developer aiming to improve your data handling techniques, this tutorial is your gateway to mastering Ordinal Encoding and its applications.
By the end of this tutorial, you'll have a strong grasp of Ordinal Encoding and be equipped to handle categorical data with order in your machine learning and data analysis projects. Whether you're working on recommendation systems, customer segmentation, or any application involving categorical features, Ordinal Encoding is a valuable skill to possess.
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Learn how to use ordinal encoder in Python with sklearn preprocessing to convert ordinal categorical data into numerical values for machine learning models. In this tutorial, I walk you through the complete process of implementing ordinal encoder, explaining the difference between ordinal and nominal data, and showing you practical examples using real code.
I start by explaining what ordinal data is—categorical data with a natural hierarchy like education levels (high school, bachelor's, master's, PhD) or skill levels (beginner to expert). Then I demonstrate how to import the necessary libraries, create sample data with store sizes (small, medium, large), and apply the ordinal encoder to transform this categorical data into numbers that machine learning models can process.
You'll see exactly how to define custom category orders instead of relying on alphabetical order, which is crucial for maintaining the proper hierarchy in your data. I show you how to use the fit_transform method and integrate the encoded values back into your pandas dataframe. The video includes all the code you need, which I'll provide in the comments section so you can follow along easily.
By the end of this tutorial, you'll be able to confidently use ordinal encoder for preprocessing your machine learning data and understand when to use it versus other encoding methods like one hot encoder for nominal data.
TIMESTAMPS
00:00 Introduction to Ordinal Encoder
00:39 What is Ordinal Data?
01:31 Importing Pandas and Loading Data
02:19 Identifying Ordinal vs Nominal Data
02:56 Finding Unique Values
03:42 Importing OrdinalEncoder from sklearn
04:32 Defining Categories Order
05:15 Applying the Transformation
05:54 Updating the DataFrame
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Who is Ryan
Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF.
Who is Matt
Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One.
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