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https://github.com/pidgey0403/game-recommender

Western AI 2021-2022: A video game recommendation engine built using the Natural Language Processing library, sPacy.
https://github.com/pidgey0403/game-recommender

ajax html javascript jquery pandas papaparse python spacy-nlp

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Western AI 2021-2022: A video game recommendation engine built using the Natural Language Processing library, sPacy.

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README

        

# 👾 Game-Reccomender 🎮
### A video game recommendation engine built using the power of natural language processing (NLP) and AI

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This app was designed as part of Western AI's 2021-2022 Projects portfolio. Gamers of all ages are unified in their struggle to find new titles to play that will scratch their itch for ingenuinity, and provide hundreds of hours of entertainment. Users simply input a previously played game title and will receive several new video game suggestions based on thousands of gamer reviews.

https://user-images.githubusercontent.com/79562052/179383362-9ce1d757-1d13-4fda-99cc-9f2429d23f79.mov

### 🧑‍💻 The Team

This full-stack web application was made by:
- [Rhea Mangat](https://github.com/rheamangat) - Project manager
- [Gabby Niamat](https://github.com/pidgey0403) - sPacy back-end and front-end developer
- [Rohit Purandhara](https://github.com/Rohit-Purandhara) - sPacy back-end developer
- [Robyn Berdan](https://github.com/RobynBerdan) - front-end developer

### 💻 Technologies & Frameworks

- [Figma](https://www.figma.com/): Website design and wireframing was done using this collaborative graphics prototyping tool.
- [sPacy](https://spacy.io/): The backbone of all functionality of the web application; sPacy is an open-source library for advanced natural language processing, which allowed us to process tens of thousands of user reviews for use in the recommendation engine.
- [pandas](https://pandas.pydata.org/): Data manipulation and analysis of raw files of user reviews using this Python library.
- [jQuery](https://jquery.com/): Event handlers and information transfer from back-end to front-end was facilitated through the use of the JavaScript library.
- [ajax](https://api.jquery.com/category/ajax/): The asynchronous retrieval of cleaned user reviews and tokens from the AI library to display on the front-end side of the web application.
- [Git](https://git-scm.com/) and [GitHub](https://github.com/): Version control for source code history and cloud storage for collaboration with all team members was facilitated using Git and GitHub.