This is the code repository for Hands-On Recommendation Systems with Python, published by Packt.
Start building powerful and personalized, recommendation engines with Python
First Paragraph from the Long Description
This book covers the following exciting features:
- The different kinds of recommender systems
- Data wrangling techniques using the pandas library
- Building an IMDB Top 250 Clone
- Building a content based engine to recommend movies based on movie metadata
- Data mining techniques used in building recommenders
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
#Import SVD
from surprise import SVD
#Define the SVD algorithm object
svd = SVD()
#Evaluate the performance in terms of RMSE
evaluate(svd, data, measures=['RMSE'])
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-7).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Samba 4.x Server Software | Windows |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Click on the following link to see the Code in Action:
Rounak Banik Rounak Banik is a Young India Fellow and an ECE graduate from IIT Roorkee. He has worked as a software engineer at Parceed, a New York start-up, and Springboard, an EdTech start-up based in San Francisco and Bangalore. He has also served as a backend development instructor at Acadview, teaching Python and Django to around 35 college students from Delhi and Dehradun.
He is an alumni of Springboard's data science career track. He has given talks at the SciPy India Conference and published popular tutorials on Kaggle and DataCamp.
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