With the rise in leaks of our personal information, most of us are well-educated about the dos and don’ts of protecting our personal data. However, we don’t always realize that the “innocuous” data that we allow companies to collect can still be used to gather valuable insight into our daily lives.
I will discuss how I used Data Science and Machine Learning techniques on my personal location tracking data to infer where I live, work, shop, and vacation. Knowing these significant locations, I was able to create a queryable record of my location at any time and day (for example: at home, at work, on vacation, away from home). This compilation of my history then enabled me to answer questions about average commute times, days when I did not follow my usual routine, and to predict, for example, what days and times I would most likely be at the grocery store.
I conclude the presentation with some thoughts on how this approach could allow businesses and organizations to subtly change the ways they interact with us, while we remain none-the-wiser.
Lisa Chang is a Data Scientist and Software Engineer at Praxis Engineering. She enjoys playing with data and teaching Data Science to others. In the past, she worked in the engine oil, fiber optics, nuclear, and semiconductor industries before she discovered computers and began solving Natural Language problems. She is still hoping to become someone who knows a lot about one thing (but so far has only succeeded in knowing a little about a lot of things).