Inspiration

  • We thank the UCI Machine Learning repository for providing the tagged dataset of users , with the sensor data and tagged with activities .

  • The wearable technology market is growing at an overwhelming Compound Annual Growth Rate of 65% and by 2020 the wearable market is estimated to grow till $41 bn .

  • The major chunk of the pie will be harnessed by the data scientists and the analysts who would make sense of the data and provide useful insights into it .

How it works

  • First we train the classifier on the historical data of the user and then based on the sensor data we classify the user's activity and then after classifying the activity , we update the user preference matrix with the ad that was clicked upon .

  • We cluster the ads based on the category and then further refine the recommendation results .

  • We developed a Carousel based interface to showcase the working of our engine.

Challenges I ran into

  • Real time data collection

Accomplishments that I'm proud of

  • A dependable recommendation engine that we extended from the existing collaborative filtering algorithm
  • We used the UCI Machine Learning Repository data , tagged with activity and the sensor data , and we were able to classify among 24 different activities .

What I learned

  • How to extend the collaborative filtering algorithm when each user - preference matrix is a vector rather than just a number indicating the rating.

  • We built a classifier using the data in the UCI Machine Learning Repository telling about the activity based on the sensor data .

What's next for HACKIN

  • We would integrate the user's feedback to improve our recommendations .
  • We would integrate the Internet Of Things and get the sensor data and make the context classification more accurate .
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