Results

Clusters in New York for a Thursday on April at 10 PM

https://htmlpreview.github.io/?https://raw.githubusercontent.com/AnisZakari/Uber_pickup_Challenge/main/april_Friday_22.html

Animation of the Clusters in New York for a Thursday on April (24h animation)

https://htmlpreview.github.io/?https://raw.githubusercontent.com/AnisZakari/Uber_pickup_Challenge/main/april_Friday_animated.html

Project 🚧¶

One of the main pain point that Uber's team found is that sometimes drivers are not around when users need them. For example, a user might be in San Francisco's Financial District whereas Uber drivers are looking for customers in Castro.

Eventhough both neighborhood are not that far away, users would still have to wait 10 to 15 minutes before being picked-up, which is too long. Uber's research shows that users accept to wait 5-7 minutes, otherwise they would cancel their ride.

Therefore, Uber's data team would like to work on a project where their app would recommend hot-zones in major cities to be in at any given time of day.

Goals 🎯

Uber already has data about pickups in major cities. Your objective is to create algorithms that will determine where are the hot-zones that drivers should be in. Therefore you will:

Create an algorithm to find hot zones Visualize results on a nice dashboard

Workflow

• Data Preprocessing
• Clustering with DBScan
• Custom Visualization with Plotly
• Deployment of a Dashboard with Dash