Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You can find large scale recommender systems in retail, video on demand, or music streaming. Machine learning algorithms in recommender systems are typically classified into two categories — content based and collaborative filtering methods although modern recommenders combine both approaches. Content based methods are based on similarity of item attributes and collaborative methods calculate similarity from interactions. This project is aimed at building machine learning skills for application in a variety of projects in the new future.
Most problems exist in data form , and are usually noisy data. There’s need to make useful data insights for businesses in order to improve efficiency and reduce poverty.
Solution statement
Machine learning recommender systems help with more robust data insights that enable online recommendations for a lot of business operations such as transaction, streaming, data capture, and selling.
Project Objective
Build a backend model
Build our own database
Build front end applications
Team members
NAME: Abdul Jalil Zakaria
NAME: Adwoa Serwaa Osei-Akoto
NAME: Alice Asiedu
NAME: Allen Kpentey
NAME: Bernd Opoku Boadu
NAME: Elias Dzwobo
NAME: Hafiz Adjei
NAME: Kelvin Tichana
NAME: Princess Balogun
NAME: Styve Lekane
NAME: Wanjiru Kinyanjui
Comentários