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Machine Learning for the Web

You're reading from   Machine Learning for the Web Gaining insight and intelligence from the internet with Python

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781785886607
Length 298 pages
Edition 1st Edition
Languages
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Authors (2):
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Andrea Isoni Andrea Isoni
Author Profile Icon Andrea Isoni
Andrea Isoni
Steve Essinger Steve Essinger
Author Profile Icon Steve Essinger
Steve Essinger
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Practical Machine Learning Using Python FREE CHAPTER 2. Unsupervised Machine Learning 3. Supervised Machine Learning 4. Web Mining Techniques 5. Recommendation Systems 6. Getting Started with Django 7. Movie Recommendation System Web Application 8. Sentiment Analyser Application for Movie Reviews Index

Commands


The commands used in this application are needed to load the data into the memory (cache) and make the user experience fast. Although the movie database is the same used in Chapter 4, Web mining techniques (that is 603 movies rated more than 50 times by 942 users), each movie needs a description to set up an information retrieval system on the movies to rate. The first command we develop takes all the movie titles in the utility matrix used in Chapter 4, Web Mining Techniques and collects the corresponding descriptions from Open Movie Database (OMDb) online service:

from django.core.management.base import BaseCommand
import os
import optparse
import numpy as np
import json
import pandas as pd
import requests
class Command(BaseCommand):

    option_list = BaseCommand.option_list + (
            optparse.make_option('-i', '--input', dest='umatrixfile',
                                 type='string', action='store',
                                 help=('Input utility matrix')),  ...
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