Graduate Admission Prediction with Pyspark ML

4.7
별점
17개의 평가
제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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.

Clock1.5 hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

  • Machine Learning
  • Data Analysis
  • Big Data
  • Linear Regression
  • PySpark

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Introduction and Installing Dependencies

  2. Clone and Explore the Dataset

  3. Data Cleaning

  4. Correlation analysis and Feature Selection

  5. Build the Linear Regression Model

  6. Evaluate and Test the model

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

GRADUATE ADMISSION PREDICTION WITH PYSPARK ML의 최상위 리뷰

모든 리뷰 보기

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