Overview#

Business case#

The car rental company from the Predictive Maintenance use case wants to expand their network in order to satisfy new demand from a partnership with an insurance company. To get maximum value from this expansion, they have approached our data team once again. Their goals include being able to:

  • Understand how the current network fits with the observed and anticipated demand.

  • Optimize the vehicle rotation schedule by predicting the demand at each agency location.

  • Evaluate partnerships and/or acquisition locations to expand the network efficiently.

Supporting data#

This use case requires the following three input data sources, available as downloadable archives at the links below:

  • Demand: A record of all road accidents handled by the insurance company spanning four years. The four yearly files total approximately 250k observations. This dataset is a modified version of a French open dataset.

  • Network: A simulated dataset that records information on the current locations of nearly 350 car rental agencies.

  • Partners: A simulated dataset that contains information on potential partners; that is, new locations (garages) to expand the network. The data must be parsed from raw html in order to extract the relevant information.

Workflow overview#

The final Dataiku pipeline is pictured below.

../../../_images/flow34.png

The Flow has the following high-level steps:

  1. Upload the datasets.

  2. Clean the datasets.

  3. Join the datasets based on geographic proximity.

  4. Aggregate the data by geography and station.

Additionally, we will create visualizations using charts and a webapp, to be shared on a dashboard.

Prerequisites#

You should be familiar with:

  • Core Designer learning path

  • The Window recipe

Technical requirements#

Note

For general notes on plugins to Dataiku, please see the reference documentation.