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Predict Diabetes with a Random Forest using R
Coursera Project Network

Predict Diabetes with a Random Forest using R

Taught in English

Chris Shockley

Instructor: Chris Shockley

3,220 already enrolled

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

2 Hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.5

(113 reviews)

What you'll learn

  • Complete a random Training and Test Set from one Data Source using an R function.

  • Practice data distribution using R and ggplot2.

  • Apply a Random Forest model.

Details to know

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Guided Project

Learn, practice, and apply job-ready skills with expert guidance

Intermediate level

Recommended experience

2 Hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.5

(113 reviews)

See how employees at top companies are mastering in-demand skills

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Learn, practice, and apply job-ready skills in less than 2 hours

  • Receive training from industry experts
  • Gain hands-on experience solving real-world job tasks
  • Build confidence using the latest tools and technologies
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About this Guided Project

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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 Pima Indians data set. There will be a short discussion about the Interface and an Instructor Bio.

  2. Task 2: The Learners will get experience looking at the data 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 and the Instructor will go over two of them in this Task.

  4. Task 4: The Learner will get experience with the syntax of the Caret, an R package. There will be a discussion on how you can apply hundreds of algorithms to a single problem using the same syntax using Caret as well.

  5. Task 5: The Learner will get experience evaluation models in this Task. RMSE will be discussed as well as the Confusion Matrix. The conclusion of the course will use the two evaluation metrics see how well the model performed on the test data set.

Recommended experience

Basic knowledge of Random Forest Models and Machine Learning

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Instructor

Instructor ratings
4.3 (8 ratings)
Chris Shockley
Coursera Project Network
10 Courses24,664 learners

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How you'll learn

  • Skill-based, hands-on learning

    Practice new skills by completing job-related tasks.

  • Expert guidance

    Follow along with pre-recorded videos from experts using a unique side-by-side interface.

  • No downloads or installation required

    Access the tools and resources you need in a pre-configured cloud workspace.

  • Available only on desktop

    This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.

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Learner reviews

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4.5

113 reviews

  • 5 stars

    67.25%

  • 4 stars

    20.35%

  • 3 stars

    7.07%

  • 2 stars

    3.53%

  • 1 star

    1.76%

DA
5

Reviewed on Dec 24, 2022

VA
5

Reviewed on Jun 10, 2020

DD
5

Reviewed on Apr 29, 2020

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