SafeDining

Finds safe (crime free) places to eat near you

Transcript

1. Ahmad Haider Insight Data Science 2016
2. Personal Story ™  11 pm@ Nov 2013: Preparing for final exams ™  Hungry. Pizza cravings! ™  Drive with friends to the nearest Papa Johns ™  Mugged at Papa Johns parking lot. Car stolen L ™  Insight project: Can I predict the safety rating of restaurants?
3. Provide real time location safety ratings of restaurants SafeDining
4. I want to eat Pizza at 2+ rated restaurant within 2 miles of my location pRest pRest pRest pRest pRest pRest pRest 2. Relative Safety Index 2. Relative Safety Index 2. Relative Safety Index 2. Relative Safety Index p1 p2 p3 p4 p5 p1 p2 p3 p4 p1 p2 p1 p2 p3 p4 p5 p6 p7 p8 p1 Crime Location Crime Probability 1. pRest 1. pRest 1. pRest 1. pRest pRest Crime Probability at restaurant
5. Workflow Crime dataset Yelp dataset Preprocessing Defining classification problem Feature Engineering Choosing a model Validation Python Pandas Regular expr. Multiclass 24 classes (hour of day) Standardization PCA Logistic Regression scikit-learn 10-fold cross validation Log loss score 2009-2015 Yelp search API
6. ™  Multiclass Classification –  Predict the hour at which crime happens at given location –  Features: Location (lat., long.), Address, Day, Week, Month, Year –  Labels: 24 classes (hour of day) –  Logistic Regression –  Cross entropy loss measure = 2.95 Algorithm
7. Theft Residential Burglary Robbery Assault Source: mylocalcrime.com Validation
8. •  Ahmad Haider •  PhD in “Measurement of energy landscapes of biological interactions using boltzmann sampling” •  Georgia Tech •  Love hiking and reading fiction/non-fiction About Me