In a couple years we are worried about the housing market and want to be able to make the best choice (with ease) when selecting a home to live in.

What it does

  • When looking for housing options, our application gives our user significant information by hovering over the city of interest in Virginia. The attributes for our city are crime rate percentage, average interest rate, walk score, price, Income to debt ratio, and average credit score. We also have the cities location based on longitude/latitude.

How we built it

  • Python scraping scripts
  • Python librarians: Panda (manipulation of data), Numpy (array/matrix operations), Beautiful-Soup (scraping tools), requests(http requests), MicroStrategy API

Challenges we ran into

  • Finding and Cleaning large amounts of Virginia data sets

Accomplishments that we're proud of

  • Finding/cleaning current-to-date attributes(crime rate, interest rates, etc.) for our data sets
  • Scrapped large data sets from multiple sources online
  • Integrating MicroStrategy's API with our application
  • Built a local website to display charts/graphs of our datasets using HTML/CSS

What we learned

  • Using beautiful-soup to scrape information from websites online
  • Learning and integrating MicroStrategy's API with out application

What's next for MLHcribs

  • Getting more data and implementing this application for cities worldwide
  • Adding time vs attribute maps to indicate trends in that specific city when hovering over that city
  • Put our data sets in some type of data base (using SQL) so that when certain attributes are updated or added, the information used on the HyperCards would update as well
  • A way to compare cities (using different weights: ex. Price may be weighted more than walk score). Could be customizable based on user preference

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