Predict Housing Prices in R on Boston Housing Data

4.4
5 個評分
1 條評論
提供方
Coursera Project Network
在此指導項目中,您將:

How to create Testing and Training Sets via R.

Ability to apply GBM, Random Forest, and Linear Models to a data set.

Ability to evaluate and choose the most accurate models.

Clock2 Hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data, and examine the results using RMSE and a Confusion Matrix). 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.

您要培養的技能

Machine LearningR ProgrammingData AnalysisRandom ForestExploratory Data Analysis

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Task 1: In this task the Learner will be introduced to the Course Objectives, which is to how to execute a Random Forest Model using R and the Boston Housing Data set. There will be a short discussion about the Interface and an Instructor Bio.

  2. Task 2: The Learners will get practice doing Exploratory Analysis using ggplot2. This is important in order for the practitioner to see the balance of the data, especially as it relates to the Response Variable.

  3. Task 3: The Learner will get experience creating Testing and Training Data Sets. There are multiple ways to do this in R. The Instructor will show the Learner how to do it using the Base R way and also using a function from the caret package.

  4. Task 4: The Learner will get experience with the syntax of the Caret, an R package. Then the Learner will create three models (Linear Regression, GBM, Random Forest) in one function call.

  5. Task 5: The Learner will get practice compiling the model results from the various models to decide which one performed the best.

  6. Task 6: The Learner will get practice looking and comparing multiple models using RMSE among other metrics.

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

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  • 是,您可以在瀏覽器的雲桌面中獲得完成指導項目所需的一切。

  • 您可以直接在瀏覽器中於分屏環境下完成任務,以此從做中學。在屏幕的左側,您將在工作空間中完成任務。在屏幕的右側,您將看到有講師逐步指導您完成項目。

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