Diabetes Prediction With Pyspark MLLIB
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
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
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:
Introduction & Install Dependencies
Clone and Explore Dataset
Data Cleaning and Preparation
Correlation analysis and Feature Selection
Split Dataset and Build the Logistic Regression Model
Evaluate and Save the model
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
Frequently asked questions
Frequently Asked Questions
What will I get if I purchase a Guided Project?
Are Guided Projects available on desktop and mobile?
Who are the instructors for Guided Projects?
Can I download the work from my Guided Project after I complete it?
What is the refund policy?
Is financial aid available?
Can I audit a Guided Project and watch the video portion for free?
How much experience do I need to do this Guided Project?
Can I complete this Guided Project right through my web browser, instead of installing special software?
What is the learning experience like with Guided Projects?
More questions? Visit the Learner Help Center.
Coursera Footer
