Building Machine Learning Pipelines in PySpark MLlib

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In this Guided Project, you will:

Learn how to create a Random Forest pipeline in PySpark

Learn how to choose best model parameters using Cross Validation and Hyperparameter tuning in PySpark

Learn how to create predictions and assess model's performance in PySpark

Clock1.5 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

By the end of this project, you will learn how to create machine learning pipelines using Python and Spark, free, open-source programs that you can download. You will learn how to load your dataset in Spark and learn how to perform basic cleaning techniques such as removing columns with high missing values and removing rows with missing values. You will then create a machine learning pipeline with a random forest regression model. You will use cross validation and parameter tuning to select the best model from the pipeline. Lastly, you will evaluate your model’s performance using various metrics. A pipeline in Spark combines multiple execution steps in the order of their execution. So rather than executing the steps individually, one can put them in a pipeline to streamline the machine learning process. You can save this pipeline, share it with your colleagues, and load it back again effortlessly. Note: You should have a Gmail account which you will use to sign into Google Colab. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Machine Learning Pipelineshyperparameter tuningPySparkCross Validation

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Install Spark on Google Colab and load a dataset in PySpark

  2. Describe and clean your dataset

  3. Create a Random Forest pipeline to predict car prices

  4. Create a cross validator for hyperparameter tuning

  5. Train your model and predict test set car prices

  6. Evaluate your model’s performance via several metrics

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

More questions? Visit the Learner Help Center.