Inspiration
We have teammates who only knew python so using Flask and sklearn so undertaking the AtomBank property valuation project made sense. Additionally, this project tested our skills in nearly all areas, from backend server programming and building machine learning models with python to using javascript to manipulate HTML objects in the DOM.
What it does
Allows users to input various information about their property, and to obtain a valuation. The application also displays data on other properties in the same postcode for comparison.
How we built it
Flask, sklearn, javascript, google maps API, web scraping
Challenges we ran into
effectively linking the machine learning model with the Flask application and eventually the frontend. Also allocation in order to maximize efficiency required a lot of thought but we managed this well in the end
Accomplishments that we're proud of
Our application works as intended!!!!
What we learned
Nearly everything we used (mentioned above) was new to most of us so we all got hands on experience with new languages, libraries, etc
What's next for Property Valuation Predictor
displaying similar properties to a search, improving accuracy, expanding outside of durham, making UI nicer for users
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