You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

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Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Building a movie recommender with Naïve Bayes

After the toy example, it is now time to build a movie recommender (or, more specifically, movie preference classifier) using a real dataset. We herein use a movie rating dataset (https://grouplens.org/datasets/movielens/). The movie rating data was collected by the GroupLens Research group from the MovieLens website (http://movielens.org).

For demonstration purposes, we will use the small dataset, ml-latest-small (downloaded from the following link: http://files.grouplens.org/datasets/movielens/ml-latest-small.zip of ml-latest-small.zip (size: 1 MB)) as an example. It has around 100,00 ratings, ranging from 1 to 5, given by 6,040 users on 3,706 movies (last updated September 2018).

Unzip the ml-1m.zip file and you will see the following four files:

movies.dat: It contains the movie information in the format of MovieID::Title::Genres.
ratings.dat: It contains user movie ratings in the format of UserID...

You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Table of Contents (17) Chapters

Building a movie recommender with Naïve Bayes

Authors (1)

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