AWS SageMaker Canvas Tutorial 🔥

You have your dataset extracted in a csv file and now you want to generate accurate machine learning predictions.

Can you do this using Amazon SageMaker Canvas and without writing any code?

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SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

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Tutorial Summary:
Demonstrates using Amazon SageMaker Canvas for machine learning predictions without writing code.
Starts by selecting input values: 50 for age, 35 for BMI, and affirming "Smoker" to expect a high insurance cost.
Guides through preparing a dataset in Excel: creates input and output columns with random numbers and saves it as "my_data.csv."
Uploads the file to an S3 bucket in the SageMaker Canvas tab.
Creates a dataset by selecting "S3" as the data source, choosing the uploaded file, and creating the dataset.
Builds a predictive model by defining the target column and using "quick build" to automatically create the model.
Tests the model's accuracy by providing different input values and checking if it predicts the correct output values.
Introduces a new dataset (insurance.csv) with columns defining insurance variables.
Creates a new model based on this insurance dataset to predict yearly insurance costs based on defined variables.
Shows how being a smoker significantly impacts yearly insurance costs in the model.
Tests the model by inputting values and expecting a high insurance cost for a smoker.
Concludes by advising to log out to avoid incurring Canvas charges and ends with a humorous question.

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P.S. Why did the data scientist bring Amazon SageMaker to the comedy club? Because they wanted to train their model to recognize a good "punch" line!