Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Testing the recommendations

Finally, our machine learning-based recommender system is ready. It will provide a significant boost in user experience for any bookshop, for sure. But before we start advertising it, we should make sure that it's reliable. Remember that we put aside 10% of our dataset for testing purposes. The idea is to compare the recommendations with actual ratings from the test data to see what degree of similarity exists between the two; that is, how many of the actual ratings from the dataset were in fact recommended. Depending on the data that's used for the training, you may want to test that both correct recommendations are made, but also that bad recommendations are not included (that is, the recommender does not suggest items that got low ratings, indicating a dislike). Since we only used ratings of 8, 9, and 10, we won't check if low-ranked recommendations were provided. We'll just focus on checking how many of the recommendations are actually part of the user's data...