Lab
A Cloud Guru

Creating a scikit-learn Random Forest Classifier in Amazon SageMaker

Scikit-learn is a great place to start working with machine learning. In this lab, we will use scikit-learn to create a Random Forest Classifier to determine if you prefer cats or dogs. The data set being used is entirely made up, but could easily be swapped with one of your own!

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Path Info

Level

Advanced

Duration

1h 0m

Published

Nov 08, 2023

Challenge

Navigate to the Jupyter Notebook

Log in to the AWS console and navigate to the AWS SageMaker page. From there, load the Jupyter Notebook that has been provided with this hands-on lab.
Challenge

Load and Prepare the Data
1. Load the survey data from data.csv, located beside the notebook.
2. View the data. Look at both the raw data and statistics for the data.
3. Change the column data types so the model can understand them.
4. Split the data into training and testing sets. Use 80% of the data for training.
Challenge

Train the Random Forest Model
1. Create a Random Forest Classifier model using scikit-learn.
2. Train the model using the training data.
Challenge

Evaluate the Model
1. Generate predictions for the testing data set.
2. View the confusion matrix for the predictions.
3. Calculate the sensitivity and specificity.
4. Plot the ROC curve.
5. Calculate the area under the curve.
Challenge

Predict for Yourself
1. Create a survey response for yourself.
2. Have the model predict if you prefer cats or dogs.

Author

A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

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.