Diabetes Prediction With Pyspark MLLIB

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stars

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In this Guided Project, you will:

Learn to Build and Train Logistic Regression Classifier using Pyspark MLLIB

Learn to set up Pyspark on the Google Colab Environment

Learn to work with Pyspark Dataframe

1.5 hours
Intermediate
No download needed
Split-screen video
English
Desktop only

In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark.You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the educational purpose. By the end of this project, you will be able to build the logistic regression classifier using Pyspark MLlib to classify between the diabetic and nondiabetic patients.You will also be able to setup and work with Pyspark on Google colab environment. Additionally, you will also be able to clean and prepare data for analysis. You should be familiar with the Python Programming language and you should have a theoretical understanding of the Logistic Regression algorithm. You will need a free Gmail account to complete this project. 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

  • Data Science

  • Machine Learning

  • Python Programming

  • Google colab

  • PySpark

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. Introduction & Install Dependencies

  2. Clone and Explore Dataset

  3. Data Cleaning and Preparation

  4. Correlation analysis and Feature Selection

  5. Split Dataset and Build the Logistic Regression Model

  6. Evaluate and Save the model

  7. Model Prediction on a new set of unlabelled data

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

By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.