Delivery Market Modeling

DoorDash: Exploring delivery pipeline and trip route data to uncover patterns, insights, and key questions.

DoorDash is a local commerce platform connecting merchants, customers, and delivery couriers. They aim to build the infrastructure for local delivery, through a combination of large-scale logistics operations and cutting-edge engineering and technology capabilities.

Assignment

The Data team builds intelligence and capabilities using this data that helps them to widen the gap between the quality, efficiency, and profitability of their business versus their competitors.

The company wants to understand and process the data coming out of data engineering pipelines:

• Clean, sanitize and manipulate data to get useful features out of raw fields;
• Make sense of the raw data and help the data science team to build forecasting models on it.

Data Description

delhivery_data.csv contains records of deliveries prepared by the data engineering department for modeling and has the following columns:

• data – tells whether the data is testing or training data
• trip_creation_time – Timestamp of trip creation
• route_schedule_uuid – unique Id for a particular route schedule
• route_type – transportation type:
  • FTL – Full Truck Load: FTL shipments get to the destination sooner, as the truck is making no other pickups or drop-offs along the way
  • Carting – handling system consisting of small vehicles (carts)
• trip_uuid - unique ID given to a particular trip (A trip may include different source and destination centers)
• source_center – ID of a trip origin point
• source_name – the name of a trip origin point
• destination_center – destination ID
• destination_name – destination Name
• od_start_time – trip start time
• od_end_time – trip end time
• start_scan_to_end_scan – time taken to deliver from source to destination
• is_cutoff – unknown field
• cutoff_factor – unknown field
• cutoff_timestamp – unknown field
• actual_distance_to_destination – the distance in kilometers between the source and destination
• actual_time – actual time taken to complete the delivery (cumulative)
• osrm_time – an open-source routing engine time calculator which computes the shortest path between points in a given map (given usual traffic) and gives the time estimate (cumulative)
• osrm_distance – an open-source routing engine that computes the distance of the shortest path between points in a given map (cumulative)
• factor – unknown field
• segment_actual_time – time taken by a segment of the package delivery
• segment_osrm_time – the OSRM segment time
• segment_osrm_distance – the OSRM distance for a segment
• segment_factor – unknown field

Practicalities

Analyze the provided data and provide insights to the best of your abilities. Use statistical tests to support your claims. Include the relevant tables/graphs/visualization to explain what you have learned about the market. Make sure that the solution reflects your entire thought process including the preparation of data - it is more important how the code is structured rather than just the final result or plot.