Smart Environment Advisor

Inspiration

I once travelled to Beijing, holding extreme interest in this capital city with a long history, but as soon as I got off the plane the haze disappointed me. When we think of where to travel, sometimes only famous attractions are taken into consideration, while some vital features which are likely to largely influence you experience,like weather may be ignored. Our goal is to build a model, which outputs a recommendation level when you enter a location only taking weather and air quality into consideration.

What it does

We take a location as input, display the real time data of weather and air quality(such as temperature, pm2.5) and output a recommendation level.

How we built it

We use past datasets of weather and air quality, take mortality rate as label and use ((machine learning method))(to be edited) to train a model. Then when entering a location we use the api of "air pollution in the world" (https://aqicn.org/api/) to extract the real time information to report the recommendation level with the model previously trained.

Challenges we ran into

find api of the website that provides enough real time data to predict recommendation level
choose the right model and train it
combine html and python code to display the computation result