Perform Feature Engineering Using Amazon SageMaker

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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.

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Lab Info

Level

Intermediate

Last updated

Oct 14, 2025

Duration

45m

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.
Challenge

Load Libraries and Prepare the Data
1. Use the Pandas library and load the data from "Employee_encoding.csv".
2. Display the top few rows and ensure the data is read successfully.
Challenge

Apply Encoding Techniques
1. Use **OrdinalEncoder **and encode the title feature.
2. Check the categories and ensure the encoder's categories follow the required ordering.
3. Use OneHotEncoder and encode the gender feature.
4. Use Labelencoder and encode the department feature.
Challenge

Apply Scaling Techniques
1. Use MinMaxScaler and scale the salary feature to values between 0 and 1.
2. Use the scaler's describe function and validate the values.
Challenge

Apply Binning Techniques
1. Initialize KBinsDiscretizer and apply the equal-frequency strategy to the age feature.
2. Use matplotlib and plot the binned data.

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