Graduate Admission Prediction with Pyspark ML

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提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

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
Intermediate中級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

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

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  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

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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