Data scientist | Researcher | Artist


Shiny Data Product on Vehicle Purchase Behavior

I used the nested multinomial logit module that I developed for my dissertation to develop this data product to predict what kind of vehicle would someone likely purchase in the future, depending on their characteristics, and the infrastructure availability. 

I designed three tabs of results, with the input options in drop down menu on the left-hand side.

The user can choose their attributes in the drop-down menu: the options include (a) the year they want to purchase the car, (b) what kind of driving pattern they typically have, (c) how do they feel about new technologies in general, and their recharging availability at home and work. 

There are two kinds of scenarios I developed for this exercise. One is the baseline scenario, where the government does not invest in any public charging stations, and a "good infrastructure" scenario, where there is more initiative to install public chargers and other alternative fuel stations. These two tabs show the top three car technologies that the consumer would be more willing to purchase giving the circumstances. The third tab gives time series plots of vehicle purchases of this consumer for these two scenarios, i.e., it shows the purchase probability of all the vehicle technologies.

Timeseries plots

Timeseries plots

The live data product link can be found here.  And, the github repository for the R code is here.

The goal of this exercise is to show how your attributes and behavior can influence your vehicle technology purchase, and also, how incentives such as public charger installation can help promote the cleaner cars into the vehicle mix.