Graduate Admission Prediction with Pyspark ML
Learn to build the Linear Regression Model using Pyspark ML to predict admission
Learn to setup Pyspark and work with Pyspark dataframes in Colab Environment
Learn to clean and prepare data for analysis.
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. 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 learning purposes. By the end of this project, you will be able to build the linear regression model using Pyspark ML to predict admission chances.You will also be able to setup and work with Pyspark on the 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 Linear Regression algorithm. 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 and Installing Dependencies
Clone and Explore the Dataset
Data Cleaning
Correlation analysis and Feature Selection
Build the Linear Regression Model
Evaluate and Test the model
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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