- Lab
- AI
- Cloud
Perform Feature Engineering Using Amazon SageMaker
Imagine you are the data engineer, and you have been assigned the task of preprocessing the data and getting it ready for the machine learning engineers to create a highly predictable model. Your data contains both text and numerical data. The numerical data is of different ranges, and some text features require proper ordering. In this hands-on lab, you will learn how to encode, scale, and bin the data using scikit-learn.
Path Info
Table of Contents
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Challenge
Launch SageMaker Notebook
Log in to the AWS console and navigate to **AWS SageMaker **. From there, load the Jupyter Notebook that has been provided with this hands-on lab.
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Challenge
Load Libraries and Prepare the Data
- Use the Pandas library and load the data from "Employee_encoding.csv".
- Display the top few rows and ensure the data is read successfully.
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Challenge
Apply Encoding Techniques
- Use **OrdinalEncoder **and encode the title feature.
- Check the categories and ensure the encoder's categories follow the required ordering.
- Use OneHotEncoder and encode the gender feature.
- Use Labelencoder and encode the department feature.
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Challenge
Apply Scaling Techniques
- Use MinMaxScaler and scale the salary feature to values between 0 and 1.
- Use the scaler's describe function and validate the values.
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Challenge
Apply Binning Techniques
- Initialize KBinsDiscretizer and apply the equal-frequency strategy to the age feature.
- Use matplotlib and plot the binned data.
What's a lab?
Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.
Provided environment for hands-on practice
We will provide the credentials and environment necessary for you to practice right within your browser.
Guided walkthrough
Follow along with the author’s guided walkthrough and build something new in your provided environment!
Did you know?
On average, you retain 75% more of your learning if you get time for practice.