BIA Case Study - Uber: Applying Machine Learning to Improve the Customer Pickup Experience
Budget: $10 – $30 USD
7 questions - 1 day. Case study, 1-2 paragraph per question but NEED APPLIED ANALYTICS knowledge. Sample question:
3. Develop a list of hypotheses Uber could use to predict a rider's pickup location with information such as the rider's previous trips and current destination, as well as historical patterns related to the pickup location. Augment the case information with your personal Uber experiences to suggest potential hypotheses.
4. Create a quantitative pickup quality metric using attributes derived from the passive, active, and third-party signals available to Uber. Discuss why your selected attributes represent a robust pickup quality metric. What weights would you assign to the features you chose for your pickup model?
5. Based on your pickup quality metric, what actions can Uber operators take to improve the pickup experience?
3. Develop a list of hypotheses Uber could use to predict a rider's pickup location with information such as the rider's previous trips and current destination, as well as historical patterns related to the pickup location. Augment the case information with your personal Uber experiences to suggest potential hypotheses.
4. Create a quantitative pickup quality metric using attributes derived from the passive, active, and third-party signals available to Uber. Discuss why your selected attributes represent a robust pickup quality metric. What weights would you assign to the features you chose for your pickup model?
5. Based on your pickup quality metric, what actions can Uber operators take to improve the pickup experience?