Wishful Thinking

Inspiration

We found Project Gutenberg and wanted to implement a project that utilized the books found there.

What it does

It takes a user-submitted text file and generates a recommendation from the dataset based on text and style analysis, as well as predict the genre of the file.

How we built it

We used Python to build the back-end algorithms and parsers and Flask to create a front-end UI.

Challenges we ran into

Being our first Hackathon, we ran into many challenges with the organization of our work. Several times we had to rewrite parts of our code to improve speed and reduce errors.

Accomplishments that we're proud of

We managed to create a working algorithm that parses a large dataset and converts it into a more manageable vector that could quantitatively measure similarity in books.

What we learned

We learned about machine learning in the form of a Naive Bayes model to make the genre prediction and the challenges behind implementing it. We also learned how to use Flask to build a front-end, and using object-oriented programming aspects of Python.

What's next for Wishful Thinking

Implement a more rigorous recommendation algorithm such as by including sentiment analysis and artificial intelligence to make better, more accurate recommendations.

Built With

• flask
• github
• python
• scikit-learn