Advertisement is tricky. Users hate non pertinent ads and companies do not want to waste money on useless advertisements. Having data about app usage for users is a valuable insight that can revolutionize targeted advertisement in-apps (and out).

What it does

We look at, study and cluster app usage profiles for mobile users. We look at frequently used app categories and when a user uses a type of app.

How we built it

We used a machine learning technique called K-means clustering. This method is supervised. A good thing about it, is that we control the number of clusters. The advertizing company can decide how many different campaigns they want and cluster their users in these groups.

Challenges we ran into

the size of data, processing millions of lines is challenging. We wish we had Microsoft or IBM as a sponsor to use a Haddop cluster or other interface to load files into HDFS and use Hive over it.

Accomplishments that we're proud of

We are proud of our visualizations, they are nice and insightfull !!

What we learned

We learned to use the K means implementation of scikit learn as well as loading data in chunks in pandas to avoid memory shortage.

What's next for AdSniper

Rule the world

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