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Chris Shockley
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/fsuvyc/predict
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces creating a training and test set
Familiarizes users with looking at data distribution using ggplot2
Provides an opportunity to apply a Random Forest model to the data
Examines RMSE and a Confusion Matrix to assess model performance
Suitable for learners with basic knowledge of data science
Best suited for individuals based in the North America region due to regional restrictions
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Save Predict Housing Prices in R on Boston Housing Data to your list so you can find it easily later:
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Reviews summary
Not a good course for housing prices
The course "Predict Housing Prices in R on Boston Housing Data" received mixed reviews. Some students found the content helpful, while others found it lacking. Overall, the sentiment towards this course is negative, with many students expressing dissatisfaction with the course content.
Course material is difficult to understand
"The instructor doesn't even properly explain stuff"
Course objectives are wrong
"The course objectives are all wrong + it's not actually for predicting housing prices!"
Course not about Housing Price Prediction
"Course content not matching description."
"Nothing about Housing Data. It is all about predicting the age of the house."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Predict Housing Prices in R on Boston Housing Data with these
activities:
Brush Up on Basics
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Review the fundamental concepts of probability and statistics to strengthen your foundation for Random Forest.
Browse courses on
Probability
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-
Go through your previous notes or textbooks
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Solve practice problems to test your understanding
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Seek help from a tutor or mentor if needed
Resource Roundup
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Gather useful resources on Random Forest to enhance your learning.
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Resources
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Search for articles, tutorials, and videos on Random Forest
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Bookmark or save the most relevant resources
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Organize your resources into a folder or document
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Share your resources with classmates or fellow learners
Attend a workshop on Data Science with R
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Gain practical experience and learn from experts in the field of Data Science with R.
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Data Science
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Find a workshop on Data Science with R.
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Register for and attend the workshop.
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Actively participate in the workshop activities.
12 other activities
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Join a study group or discussion forum
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Connect with other students, discuss course materials, and enhance your learning through collaborative discussions.
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Collaboration
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Find a study group or discussion forum.
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Join the group and actively participate in discussions.
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Share your knowledge and learn from others.
Follow Along with Experts
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Enhance your understanding by following guided tutorials on Random Forest.
Show steps
-
Search for online tutorials or courses on Random Forest
-
Choose a tutorial that aligns with your learning style
-
Follow the instructions and complete the exercises
-
Take notes and ask questions as needed
Complete the Coursera R Programming Drills
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Improve your proficiency in R programming, which is essential for data analysis in this course.
Browse courses on
R Programming
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Complete the Coursera R Programming Drills.
Make a Forest
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Practice building and customizing your own Random Forest model using R.
Browse courses on
Random Forest
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-
Load necessary libraries
-
Prepare your data
-
Create your Random Forest model
-
Customize your model parameters
-
Evaluate your model's performance
Review 'The Elements of Statistical Learning'
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Help solidify your understanding of the fundamental concepts of statistical learning.
View
An Introduction to Statistical Learning
on Amazon
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-
Identify and describe the key theoretical concepts of statistical learning.
-
Summarize and contrast the different approaches to statistical learning, including supervised and unsupervised learning.
-
Apply the concepts and algorithms of statistical learning to practical problems.
Complete the 'Introduction to Random Forests' tutorial on YouTube
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Gain a deeper understanding of Random Forest models and their application in data analysis.
Browse courses on
Random Forests
Show steps
-
Find the 'Introduction to Random Forests' tutorial.
-
Watch the tutorial and follow along with the examples.
Solve Python coding challenges on LeetCode
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Sharpen your coding skills and improve your understanding of Python, which is essential for data analysis.
Browse courses on
Python
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Create a LeetCode account.
-
Start practicing by solving LeetCode problems.
Explain it to a Friend
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Solidify your understanding by explaining Random Forest to someone who knows nothing about it.
Show steps
-
Identify a friend or family member who is willing to listen
-
Explain the basics of Random Forest, including its purpose and how it works
-
Draw diagrams or use examples to illustrate your points
-
Answer their questions and clarify any misunderstandings
Develop a Random Forest model to predict customer churn
Show steps
Apply your knowledge of Random Forest models to a real-world problem.
Browse courses on
Random Forests
Show steps
-
Define the problem and gather the data.
-
Explore the data and prepare it for modeling.
-
Build and train a Random Forest model.
-
Evaluate the model's performance using metrics such as accuracy and F1-score.
-
Deploy the model to make predictions and monitor its performance.
Predict Tree Heights
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Apply your Random Forest skills to a real-world problem by predicting tree heights.
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Case study
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-
Gather data on tree heights and relevant factors
-
Clean and prepare your data
-
Train and evaluate your Random Forest model
-
Make predictions on new data
-
Interpret and communicate your results
Write a blog post or article on Random Forests
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Enhance your communication skills and deepen your understanding of Random Forest models by explaining them to others.
Browse courses on
Random Forests
Show steps
-
Choose a topic for your blog post or article.
-
Research and gather information about Random Forests.
-
Write a compelling blog post or article that clearly explains Random Forests.
Build a Decision Support Tool
Show steps
Demonstrate your mastery by creating a decision support tool that utilizes Random Forest.
Show steps
-
Identify a business problem that can be solved using Random Forest
-
Design and develop your tool
-
Test and validate your tool
-
Present your tool to stakeholders
Brush Up on Basics
Show steps
Review the fundamental concepts of probability and statistics to strengthen your foundation for Random Forest.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Go through your previous notes or textbooks
- Solve practice problems to test your understanding
- Seek help from a tutor or mentor if needed
Resource Roundup
Show steps
Gather useful resources on Random Forest to enhance your learning.
Browse courses on
Resources
Show steps
Show steps
Show steps
- Search for articles, tutorials, and videos on Random Forest
- Bookmark or save the most relevant resources
- Organize your resources into a folder or document
- Share your resources with classmates or fellow learners
Attend a workshop on Data Science with R
Show steps
Gain practical experience and learn from experts in the field of Data Science with R.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a workshop on Data Science with R.
- Register for and attend the workshop.
- Actively participate in the workshop activities.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Join a study group or discussion forum
Show steps
Connect with other students, discuss course materials, and enhance your learning through collaborative discussions.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Find a study group or discussion forum.
- Join the group and actively participate in discussions.
- Share your knowledge and learn from others.
Follow Along with Experts
Show steps
Enhance your understanding by following guided tutorials on Random Forest.
Show steps
Show steps
Show steps
- Search for online tutorials or courses on Random Forest
- Choose a tutorial that aligns with your learning style
- Follow the instructions and complete the exercises
- Take notes and ask questions as needed
Complete the Coursera R Programming Drills
Show steps
Improve your proficiency in R programming, which is essential for data analysis in this course.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Complete the Coursera R Programming Drills.
Make a Forest
Show steps
Practice building and customizing your own Random Forest model using R.
Browse courses on
Random Forest
Show steps
Show steps
Show steps
- Load necessary libraries
- Prepare your data
- Create your Random Forest model
- Customize your model parameters
- Evaluate your model's performance
Review 'The Elements of Statistical Learning'
Show steps
Help solidify your understanding of the fundamental concepts of statistical learning.
View
An Introduction to Statistical Learning
on Amazon
Show steps
Show steps
Show steps
- Identify and describe the key theoretical concepts of statistical learning.
- Summarize and contrast the different approaches to statistical learning, including supervised and unsupervised learning.
- Apply the concepts and algorithms of statistical learning to practical problems.
Complete the 'Introduction to Random Forests' tutorial on YouTube
Show steps
Gain a deeper understanding of Random Forest models and their application in data analysis.
Browse courses on
Random Forests
Show steps
Show steps
Show steps
- Find the 'Introduction to Random Forests' tutorial.
- Watch the tutorial and follow along with the examples.
Solve Python coding challenges on LeetCode
Show steps
Sharpen your coding skills and improve your understanding of Python, which is essential for data analysis.
Browse courses on
Python
Show steps
Show steps
Show steps
- Create a LeetCode account.
- Start practicing by solving LeetCode problems.
Explain it to a Friend
Show steps
Solidify your understanding by explaining Random Forest to someone who knows nothing about it.
Show steps
Show steps
Show steps
- Identify a friend or family member who is willing to listen
- Explain the basics of Random Forest, including its purpose and how it works
- Draw diagrams or use examples to illustrate your points
- Answer their questions and clarify any misunderstandings
Develop a Random Forest model to predict customer churn
Show steps
Apply your knowledge of Random Forest models to a real-world problem.
Browse courses on
Random Forests
Show steps
Show steps
Show steps
- Define the problem and gather the data.
- Explore the data and prepare it for modeling.
- Build and train a Random Forest model.
- Evaluate the model's performance using metrics such as accuracy and F1-score.
- Deploy the model to make predictions and monitor its performance.
Predict Tree Heights
Show steps
Apply your Random Forest skills to a real-world problem by predicting tree heights.
Browse courses on
Case study
Show steps
Show steps
Show steps
- Gather data on tree heights and relevant factors
- Clean and prepare your data
- Train and evaluate your Random Forest model
- Make predictions on new data
- Interpret and communicate your results
Write a blog post or article on Random Forests
Show steps
Enhance your communication skills and deepen your understanding of Random Forest models by explaining them to others.
Browse courses on
Random Forests
Show steps
Show steps
Show steps
- Choose a topic for your blog post or article.
- Research and gather information about Random Forests.
- Write a compelling blog post or article that clearly explains Random Forests.
Build a Decision Support Tool
Show steps
Demonstrate your mastery by creating a decision support tool that utilizes Random Forest.
Show steps
Show steps
Show steps
- Identify a business problem that can be solved using Random Forest
- Design and develop your tool
- Test and validate your tool
- Present your tool to stakeholders
Career center
Learners who complete Predict Housing Prices in R on Boston Housing Data will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to extract meaningful insights. This course can be very useful for this role, as it provides hands-on experience with data analysis techniques using R. The course also covers how to apply a Random Forest model to data, which is a valuable skill for Data Analysts.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course can be useful for this role, as it provides a foundation in machine learning and modeling. The course also covers how to apply a Random Forest model to data, which is a valuable skill for Machine Learning Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which is a valuable skill for Quantitative Analysts.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course can be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which is a valuable skill for Statisticians.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to solve business problems. This course can be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which is a valuable skill for Data Scientists.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course can be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Financial Analysts make more informed investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course can be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which is a valuable skill for Operations Research Analysts.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. This course can be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which is a valuable skill for Actuaries.
Business Analyst
Business Analyst
Business Analysts use data to analyze business processes and make recommendations for improvement. This course can be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Business Analysts make more informed recommendations.
Data Journalist
Data Journalist
Data Journalists use data to tell stories. This course may be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Data Journalists make their stories more compelling.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. This course may be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Market Researchers make more informed decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Software Engineers build more robust and efficient applications.
Computer Programmer
Computer Programmer
Computer Programmers write, test, and maintain code for software applications. This course may be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Computer Programmers develop more efficient and accurate code.
Web Developer
Web Developer
Web Developers design and develop websites. This course may be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Web Developers build more user-friendly and engaging websites.
Data Entry Clerk
Data Entry Clerk
Data Entry Clerks enter data into computer systems. This course may be useful for this role, as it provides a foundation in data analysis and modeling. The course also covers how to apply a Random Forest model to data, which can help Data Entry Clerks improve their accuracy and efficiency.
For more career information including salaries, visit:
OpenCourser.com/course/fsuvyc/predict
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Predict Housing Prices in R on Boston Housing Data.
This classic textbook provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, decision trees, and support vector machines. It valuable reference for advanced learners who want to deepen their understanding of machine learning theory.
Save
Provides a comprehensive overview of deep learning theory and practice. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It also includes hands-on exercises and case studies.
Save
Delves into the practical aspects of predictive modeling, covering topics such as data preparation, feature engineering, model selection, and model evaluation. It provides a solid foundation for building and deploying machine learning models.
This textbook offers a comprehensive introduction to statistical learning methods, with a focus on practical applications. It covers topics such as regression, classification, and clustering, and provides hands-on examples and exercises.
Provides a comprehensive overview of reinforcement learning theory and practice. It covers topics such as Markov decision processes, dynamic programming, and Q-learning. It also includes hands-on exercises and case studies.
Provides a comprehensive overview of machine learning techniques using the R programming language. It covers a wide range of topics, including data preprocessing, model selection, and model evaluation. It also includes hands-on exercises and case studies.
Focuses on practical machine learning applications using the R programming language. It provides a comprehensive overview of machine learning algorithms, data manipulation techniques, and model evaluation methods.
Save
Focuses on data mining techniques using the R programming language. It covers topics such as data exploration, feature selection, and model building. It also includes hands-on exercises and case studies.
Provides a practical introduction to machine learning for non-technical readers. It covers topics such as data wrangling, model building, and model evaluation. It also includes hands-on exercises and case studies.
Focuses on machine learning applications using the Python programming language. It provides a hands-on approach to building and deploying machine learning models, covering topics such as data preprocessing, feature engineering, and model evaluation.
Provides a gentle introduction to machine learning using the Python programming language. It covers topics such as data preprocessing, feature engineering, and model evaluation. It also includes hands-on exercises and case studies.
Provides a great introduction to machine learning concepts and techniques, making it a good starting point for learners with no prior knowledge in the field. It covers fundamental concepts such as supervised and unsupervised learning, model selection, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fsuvyc/predict
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Time
2 hours
Via
Coursera
Institutions
Rhyme
Coursera Project Network
Instructor
Chris Shockley
Good to know
Introduces creating a training and test set
Familiarizes users with looking at data distribution using ggplot2
Provides an opportunity to apply a Random Forest model to the data
Examines RMSE and a Confusion Matrix to assess model performance
Suitable for learners with basic knowledge of data science
Best suited for individuals based in the North America region due to regional restrictions
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