Chevron Left
返回到 Graduate Admission Prediction with Pyspark ML

學生對 Coursera Project Network 提供的 Graduate Admission Prediction with Pyspark ML 的評價和反饋

4.7
17 個評分
6 條評論

課程概述

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....

熱門審閱

篩選依據:

1 - Graduate Admission Prediction with Pyspark ML 的 6 個評論(共 6 個)

創建者 Cheikh B

2021年5月13日

Great project very clear and easy to understand. Thank you for this great project i hope you will make the same project for regression, deeplearning in pyspark.

Thank tou Coursera

創建者 Feng J

2021年5月16日

This class is explained very clearly, so that I could understand how to use pyspark completely. Thank you so much for teaching us in such a great way !

創建者 Alexandra A

2021年8月26日

Straightforward tutorial of how to use pyspark for a simple machine learning task.

創建者 Carlos A P

2020年10月25日

Good taste for PySpark ML

創建者 Muhammad M

2020年12月25日

very informative

創建者 Aruparna M

2021年1月31日

More details were required.