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  • Book Overview & Buying R Data Science Essentials
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R Data Science Essentials

R Data Science Essentials

By : Koushik, Kumar Ravindran
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R Data Science Essentials

R Data Science Essentials

3 (3)
By: Koushik, Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
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9
Index

Recommendations using user-based CF


In the user-based CF method, we get the recommendations based on the computation of the similarity between the users. There are different methods to compute the similarity between the users. We will see the implementation of similarity computation using the cosine method.

The following function computes the cosine similarity between the users. The logic to compute the cosine similarity is mentioned here, but in order to know more about the cosine similarity methodology, refer to the online articles pertaining to cosine similarity. We need to pass the user IDs to be compared to the function. First, we convert the data into a data frame format and omit the products that were not purchased by both the users as including them would bias the similarity score. Then, we compute the cosine similarity for the users. If the pair of users has not bought at least three common products, then we will not be computing the similarity but instead, will make it as zero:

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