Predict Housing Prices in R on Boston Housing Data

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estrelas
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Coursera Project Network
Neste projeto guiado, você irá:

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
IntermediateIntermediário
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

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.

Habilidades que você desenvolverá

Machine LearningR ProgrammingData AnalysisRandom ForestExploratory Data Analysis

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  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.

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo

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