Skip to main content

Bridget Balkaran has completed

Supervised Learning in R: Case Studies

Start course For Free
4 hr
4,400 XP
Statement of Accomplishment Badge

Loved by learners at thousands of companies

Course Description

Predictive modeling, or supervised machine learning, is a powerful tool for using data to make predictions about the world around us. Once you understand the basic ideas of supervised machine learning, the next step is to practice your skills so you know how to apply these techniques wisely and appropriately. In this course, you will work through four case studies using data from the real world; you will gain experience in exploratory data analysis, preparing data so it is ready for predictive modeling, training supervised machine learning models, and evaluating those models.
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
DataCamp for BusinessFor a bespoke solution book a demo.
  1. 1

    Not mtcars AGAIN

    Free

    In this first case study, you will predict fuel efficiency from a US Department of Energy data set for real cars of today.

    Play Chapter Now
    Making predictions using machine learning
    50 xp
    Choosing an appropriate model
    50 xp
    Visualizing the fuel efficiency distribution
    100 xp
    Building a simple linear model
    100 xp
    Getting started with caret
    50 xp
    Training and testing data
    100 xp
    Training models with caret
    100 xp
    Evaluating your models
    100 xp
    Using the testing data
    100 xp
    Let's sample our data
    50 xp
    Bootstrap resampling
    100 xp
    Plotting modeling results
    100 xp
  2. 2

    Stack Overflow Developer Survey

    Stack Overflow is the world's largest online community for developers, and you have probably used it to find an answer to a programming question. The second chapter uses data from the annual Stack Overflow Developer Survey to practice predictive modeling and find which developers are more likely to work remotely.

    Play Chapter Now
    Essential copying and pasting from Stack Overflow
    50 xp
    Choosing an appropriate model
    50 xp
    Exploring the Stack Overflow survey
    100 xp
    Start with a simple model
    100 xp
    Dealing with imbalanced data
    50 xp
    Training and testing data
    100 xp
    Upsampling
    100 xp
    Understanding upsampling
    50 xp
    Upsampling in your workflow
    50 xp
    Predicting remote status
    50 xp
    Training models
    100 xp
    Confusion matrix
    100 xp
    Classification model metrics
    100 xp
  3. 3

    Get out the vote

    In the third case study, you will use data on attitudes and beliefs in the United States to predict voter turnout. You will apply your skills in dealing with imbalanced data and explore more resampling options.

    Play Chapter Now
    Predicting voter turnout from survey data
    50 xp
    Choosing an appropriate model
    50 xp
    Exploring the VOTER data
    100 xp
    Visualization for exploratory data analysis
    100 xp
    Imbalanced data
    50 xp
    Fit a simple model
    100 xp
    VOTE 2016
    50 xp
    Training and testing data
    100 xp
    Upsampling for imbalanced data
    100 xp
    Cross-validation
    50 xp
    Understanding cross-validation
    50 xp
    Training models with cross-validation
    100 xp
    Comparing model performance
    50 xp
    Confusion matrix for your training data
    100 xp
    Confusion matrix for your testing data
    100 xp
    Which model is best?
    50 xp
  4. 4

    But what do the nuns think?

    The last case study uses an extensive survey of Catholic nuns fielded in 1967 to once more put your practical machine learning skills to use. You will predict the age of these religious women from their responses about their beliefs and attitudes.

    Play Chapter Now
    Surveying Catholic sisters in 1967
    50 xp
    Choosing an appropriate model
    50 xp
    Visualizing the age distribution
    100 xp
    Tidying the survey data
    100 xp
    Exploratory data analysis with tidy data
    50 xp
    Visualizing agreement with age
    100 xp
    Building a simple linear model
    100 xp
    Training, validation, and testing data
    100 xp
    Using your validation set
    50 xp
    Predicting age with supervised machine learning
    50 xp
    Training models
    100 xp
    Making predictions
    100 xp
    Choosing between models
    100 xp
    Estimating uncertainty for new data
    100 xp
    Wrapping up
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

datasets

Fuel efficiency of real cars (2018)Annual Stack Overflow Developer Survey (2017)Survey responses of voters (2016 US Presidential elections)Survey responses of Catholic sisters (1967)

collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Sumedh Panchadhar

prerequisites

Machine Learning with caret in RIntroduction to the Tidyverse
DataCamp Content Creator

Course Instructor

DataCamp offers interactive R, Python, Spreadsheets, SQL and shell courses. All on topics in data science, statistics, and machine learning. Learn from a team of expert teachers in the comfort of your browser with video lessons and fun coding challenges and projects.
See More

Join over 17 million learners and start Supervised Learning in R: Case Studies today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.