ToroGo

Inspiration

We travelled all the way from India and while booking our AirBnb we were overwhelmed with the options available to us. Although, websites like tripadvisor help with reviews of an area, it is often not enough. Toronto welcomes over 40 million visitors annually, and is the leading tourism destination in Canada. So we decided to use the power of data science to help travellers like us solve where to stay.

What it does

Find a Place In our discussions we came up with three important things that users look at while deciding where to live in a new city:

Price Safety Reviews The app requests the user to enter his priority for the above mentioned features and suggests the top 5 suggested places that would suit the user based on data analysis.

Currency Converter Along with that we provide the user easy access to check the live currency exchange rates using XE.com powerful api instantly from the app. It supports searching from 100s of currencies and can instantly give the live exchange rate.

How I built it

We used open datasets available from the Canadian Open Dataset website and Airbnb to build an aggregate score of around 130 sub-areas of Toronto City. We chose three parameters,

Locality Ratings based on Sentiment Analysis of Reviews Safety Ratings based on Crime Data such as Assaults Priceyness Ratings based on the cost of listings of Airbnbs in a Neighborhood.

Challenges I ran into

Data was easy to find but aggregation was tough to do based on the neighborhood level. Getting the heatmap to work based on neighborhood on Android We tried to integrate the Microsoft Azure Chatbot API but were unable to integrate it with the app.