R Data Analysis Projects
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- Build end-to-end predictive analytics systems in R
- Study an experimental design to gather data and conduct analysis
- Implement a recommender system from scratch using different approaches
- Use and leverage RShiny to build reactive programming applications
- Build systems for varied domains including market research, network analysis, social media analysis, and more
- Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively
- Communicate modeling results using Shiny Dashboards
- Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling
- A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this video.
R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis.
This video will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets.
You’ll implement time-series modeling for anomaly detection and understand cluster analysis for streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow code.
With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The video covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.
By the end of this video, you’ll have a better understanding of data analysis with R, and will be able to put your knowledge to practical use without any hassle.
About the Author
Gopi Subramanian is a scientist and author with over 18 years of experience in the fields of data mining and machine learning. During the past decade, he has worked extensively in data mining and machine learning, solving a variety of business problems.
He has 16 patent applications with the US and Indian patent offices and several publications to his credit. He is the author of Python Data Science Cookbook by Packt Publishing.
- This video will take you all the way through the practical application of advanced and effective analytics methodologies in R.
Content-based methods rely on the product properties to create
recommendations, they can ignore the user preferences, to begin with.
Content-based method dishes out the Needed recommendation and user
profile can be built in the background.
In this video, we will load the RLdata500 from the Record Linkage
package and display to the user, implement the weights algorithm and
display the weight range as a histogram and allow the user to select the
lower and upper thresholds of weights for classification.