1. Predicting Demographics through smartphone sensors Itay Hazan Dr. Asaf Shabtai 1

2. The Challenge Reveal Mobile Users Gender o Design Algorithm for Gender prediction o Make it Quick Learner (Day 1) o Ensure low battery consumption o Ask for no special permissions 2

3. Our Method D esign AI Combined Model o Using Normal Permissioned sensors Application Names (activity manager) Network Traffic Amounts (TrafficStats) Accelerometer records (SensorManager) o Preprocess & Summarize the data o Machine learning Methods 3

4. Installed Applications 40 different categories o Sports and Productivity categories are a good discriminator for gender o Two types of Categories features, amount & share Total Amount Non-free applications o 63 out of 208 (30.3%) male users purchased at least one application, while only 12 of 158 (7.6%) female purchased at least one applications Heavy applications. (50MB+) o We noticed that only 14 out of 158 (8.8%) female users have at least one heavy application installed, while 59 of 208 (28.3%) AverageStDev Female33.36714.178 Male46.65321.87 4

5. Results 5