- We were inspired to make this project after thinking about ways to level the playing field between those in poor and rich school districts
- Takes in a list of keywords (interests and skills)
- Outputs a list of URLs and summaries of the webpages deemed most similar to the user’s interests
- Calculates Wu-Palmer Similarity and Levenshtein Distances to assess how similar a website is to the wants of the user
- Uses Beautiful Soup in order to get a summary of the activity (generally) as well as the time it takes to complete the course and who the course is offered by for Coursera
- We built the project using
Python
,Beautiful Soup
,NLTK
,HMNI
,FuzzyWuzzy
, andFlutter
- We had trouble in implementing the code that used Levenshtein distances and Palmer similarity, leading to words that had nothing to do with each other being assigned high similarity values.
- We also had trouble with the UI as the version of
TensorFlow
that HMNI uses was incompatible with mac(the system that our frontend member was using)
- Each member was working with tools they were not familiar with, yet we still completed the product
- We were able to overcome an issue where
TensorFlow
would not work with macOS - The UI is intuitive and minimalistic
- We learned a lot about web scraping, website interactions, and measuring abstract concepts like context-empowered text similarity
- In Progress: Using multithreading to run Selenium search processes simultaneously
- Subsequently running
BeautifulSoup
sub processes simultaneously - Effect: Greatly reduce runtime
- Subsequently running
- Use of Machine Learning to more accurately find the description of any activity no matter the website
- Use of Machine Learning to compare user tags for skills and interests through categorizing them by field or topic and then choosing the more specific one
- ML model will be a “Bag-of-words” model → Train using words correlated to category
- Ex. “python” in skills replaces “coding language” in interests for the combined skills-interests array
- Main Goal: Host
Flask
API online on services likeAWS
and deploy flutter app toiOS
,Android
,Web
, andWindows