Initial system architecture design
Table of contents
This week's challenge task interim report focuses on causal inference using real-world Nigeria Gokada delivery driver service data. Gokada is one of the largest last-mile delivery services in Nigeria.
Current business challenges center on the causal inference of our client Gokada, Nigeria's largest last-mile delivery service. Gokada Works is partnered with motorbike owners and drivers to deliver parcels across Lagos, Nigeria. Gokada has completed more than a million deliveries in less than a year with a fleet of over 1200 riders. One key issue Gokada has faced as it expands its service is the suboptimal placement of pilots (Gokada calls their motor drivers pilots) and clients who want to use Gokada to send their parcels. As a result, a large number of delivery requests have gone unfulfilled.
This objective of this project is very straightforward: design and build a robust, reliable, large-scale It is working on its data to help it understand the primary causes of unfulfilled requests, as well as come up with solutions that recommend driver locations that increase the fraction of complete orders. Since drivers are paid based on the number of requests they accept, this solution will help Gokada business grow both in terms of client satisfaction and increased business volume.
There are two datasets available for this project.
The first one is the table that contains information about the completed orders
Column Non-Null Count Dtype
0 Trip ID 536020 non-null int64 1 Trip Origin 536020 non-null address 2 Trip Destination 536020 non-null address 3 Trip Start Time 534369 non-null timestamp 4 Trip End Time 536019 non-null timestamp
The second one is the table that contains delivery requests by clients (completed and unfulfilled)
Column Non-Null Count Dtype
0 id 1557740 non-null int64
1 order_id 1557740 non-null int64
2 driver_id 1557740 non-null int64
3 driver_action 1557740 non-null object 4 lat 1557740 non-null float64 5 lng 1557740 non-null float64 6 created_at 0 non-null float64 7 updated_at 0 non-null float64
Pip
Python 3.8 or above
You can find the full list of requirements in the requirements.txt file
We highly recommend you create a new virtual environment and install every required modules and libraries on the virtual environment.
The detailed use and implementation of the pipelines drivers location optimization with Causal Inference usage can all be found in this screenshots folder as image files.
All the notebooks that are used in this project including EDA, data cleaning and summarization along with some machine learning model generations are found here in the Notebooks folder.
All the scripts and modules used for this project relating to interactions drivers location optimization with Causal Inference and other frameworks along with default parameters and values used will be found here, in the scripts folder.
All the drivers location optimization with Causal Inference are found here in the strategies folder.
All the unit and integration tests are found here in the tests folder.