HACKIN

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 .

Built With

• bootstrap
• flask
• jquery
• matlab
• npm
• python-package-index
• scikit-learn