Machine learning with Python

Ferreira, Pedro; Simons, Christopher

Abstract

This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

• Presentation Conference Type: Presentation / Talk
• Conference Name: 2017 Conference of the Association of C and C++ Users (ACCU 2017)
• Start Date: Apr 25, 2017
• End Date: Apr 29, 2017
• Acceptance Date: Jan 10, 2017
• Publication Date: Apr 28, 2017
• Publicly Available Date: Jun 7, 2019
• Peer Reviewed: Peer Reviewed
• Keywords: machine learning, Python, collaborative filtering
• Public URL: https://uwe-repository.worktribe.com/output/888794
• Publisher URL: http://www.accu.org
• Related Public URLs: https://conference.accu.org/site/stories/2017/sessions.html#XMachineLearningwithPythonCaseStudy
• Additional Information: Title of Conference or Conference Proceedings : 2017 Conference of the Association of C and C++ Users (ACCU 2017)