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Hands-On Recommendation Systems with Python
Hands-On Recommendation Systems with Python

Hands-On Recommendation Systems with Python: Start building powerful and personalized, recommendation engines with Python

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Hands-On Recommendation Systems with Python

Manipulating Data with the Pandas Library

In the next few portions of the book, we are going to get our hands dirty by building the various kinds of recommender systems that were introduced in chapter one. However, before we do so, it is important that we know how to handle, manipulate, and analyze data efficiently in Python.

The datasets we'll be working with will be several megabytes in size. Historically, Python has never been well-known for its speed of execution. Therefore, analyzing such huge amounts of data using vanilla Python and the built-in data structures it provides us is simply impossible.

In this chapter, we're going to get ourselves acquainted with the pandas library, which aims to overcome the aforementioned limitations, making data analysis in Python extremely efficient and user-friendly. We'll also introduce ourselves to the Movies Dataset that...

Technical requirements

Setting up the environment

Before we start coding, we should probably set up our development environment. For data scientists and analysts using Python, the Jupyter Notebook is, by far, the most popular tool for development. Therefore, we strongly advise that you use this environment.

We will also need to download the pandas library. The easiest way to obtain both is to download Anaconda. Anaconda is a distribution that comes with the Jupyter software and the SciPy packages (which includes pandas).


You can download the distribution here: https://www.anaconda.com/download/.

The next step is to create a new folder (I'm going to name it RecoSys) in your desired location. This will be the master folder that contains all the code we write as part of this book. Within this folder, create another folder named Chapter2, which will contain all the code we write as part of this chapter...

The Pandas library

Pandas is a package that gives us access to high-performance, easy-to-use tools and data structures for data analysis in Python.

As we stated in the introduction, Python is a slow language. Pandas overcomes this by implementing heavy optimization using the C programming language. It also gives us access to Series and DataFrame, two extremely powerful and user-friendly data structures imported from the R Statistical Package.

Pandas also makes importing data from external files into the Python environment a breeze. It supports a wide variety of formats, such as JSON, CSV, HDF5, SQL, NPY, and XLSX.

As a first step toward working with pandas, let's import our movies data into our Jupyter Notebook. To do this, we need the path to where our dataset is located. This can be a URL on the internet or your local computer. We highly recommend downloading the data...

The Pandas DataFrame

As we saw in the previous section, the df.head() code outputted a table-like structure. In essence, the DataFrame is just that: a two-dimensional data structure with columns of different data types. You can think of it as an SQL Table. Of course, just being a table of rows and columns isn't what makes the DataFrame special. The DataFrame gives us access to a wide variety of functionality, some of which we're going to explore in this section.

Each row in our DataFrame represents a movie. But how many movies are there? We can find this out by running the following code:

#Output the shape of df
df.shape

OUTPUT:
(45466, 24)

The result gives us the number of rows and columns present in df. We can see that we have data on 45,466 movies.

We also see that we have 24 columns. Each column represents a feature or a piece of metadata about the movie. When we ran...

The Pandas Series

When we accessed the Jumanji movie using .loc and .iloc, the data structures returned to us were Pandas Series objects. You may have also noticed that we were accessing entire columns using df[column_name]. This, too, was a Pandas Series object:

type(small_df['year'])

OUTPUT:
pandas.core.series.Series

The Pandas Series is a one-dimensional labelled array capable of holding data of any type. You may think of it as a Python list on steroids. When we were using the .apply() and .astype() methods in the previous section, we were actually using them on these Series objects.

Therefore, like the DataFrame, the Series object comes with its own group of extremely useful methods that make data analysis a breeze.

First, let's check out the shortest- and longest-running movies of all time. We will do this by accessing the runtime column of the DataFrame as...

Summary

In this chapter, we gained an understanding of the limitations of using vanilla Python and its built-in data structures. We acquainted ourselves with the Pandas library and learned how it overcomes the aforementioned difficulties by giving us access to extremely powerful and easy-to-use data structures. We then explored the two main data structures, Series and DataFrame, by analyzing our movies-metadata dataset.

In the next chapter, we will use our newfound skills to build an IMDB Top 250 Clone and its variant, a type of knowledge-based recommender.

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Key benefits

  • Build industry-standard recommender systems
  • Only familiarity with Python is required
  • No need to wade through complicated machine learning theory to use this book

Description

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques  With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.

Who is this book for?

If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

What you will learn

  • Get to grips with the different kinds of recommender systems
  • Master data-wrangling techniques using the pandas library
  • Building an IMDB Top 250 Clone
  • Build a content based engine to recommend movies based on movie metadata
  • Employ data-mining techniques used in building recommenders
  • Build industry-standard collaborative filters using powerful algorithms
  • Building Hybrid Recommenders that incorporate content based and collaborative fltering

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 31, 2018
Length: 146 pages
Edition : 1st
Language : English
ISBN-13 : 9781788993753
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Product Details

Publication date : Jul 31, 2018
Length: 146 pages
Edition : 1st
Language : English
ISBN-13 : 9781788993753
Vendor :
Google
Category :
Languages :

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Table of Contents

8 Chapters
Getting Started with Recommender Systems Chevron down icon Chevron up icon
Manipulating Data with the Pandas Library Chevron down icon Chevron up icon
Building an IMDB Top 250 Clone with Pandas Chevron down icon Chevron up icon
Building Content-Based Recommenders Chevron down icon Chevron up icon
Getting Started with Data Mining Techniques Chevron down icon Chevron up icon
Building Collaborative Filters Chevron down icon Chevron up icon
Hybrid Recommenders Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4
(11 Ratings)
5 star 36.4%
4 star 27.3%
3 star 0%
2 star 9.1%
1 star 27.3%
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Kindle Customer Jun 29, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
My brother wrote this book. Found it very useful and easy to follow.
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Owner Dec 20, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Extremely helpful walkthrough text. Includes videos, codes, download links for data. Offers just the right level of detail.
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krishnendu kundu Nov 26, 2018
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Good
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Dhanow Apr 15, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I like the way this book connects the theory part and the coding part. It not only helped me understand the concept in depth but also helped me get my hands dirty writing and executing the code. The book is well written and easy for anyone to read and understand. Would recommend buying this book if you want to have a good knowledge of recommendation systems.
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vishnu Nov 27, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Book contains very simple language which helps to understand easily with proper code explanation. This is a very good book to start building recommendation systems.
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