Graduate Admission Prediction with Pyspark ML

In this 1-hour project-based course, you will master building a linear regression model using Pyspark ML to predict graduate admission chances. The project utilizes the graduate admission 2 dataset from Kaggle and focuses on implementing a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library.

Throughout the course, you will work in the Google Colab environment with Pyspark installation. Expect to learn how to set up and manipulate Pyspark dataframes in the Colab environment, as well as clean and prepare data for analysis. Prior familiarity with the Python programming language and a theoretical understanding of the Linear Regression algorithm are recommended.

• Build a linear regression model using Pyspark ML to predict admission
• Set up Pyspark and work with Pyspark dataframes in the Colab Environment
• Clean and prepare data for analysis

By the end of the project, you will have the skills to construct a linear regression model using Pyspark ML for predicting admission chances. Please note that the dataset and model used in this project are for educational purposes only and not intended for real-life application.