Alexander Hess
6333f1af1e
- clean the raw data given by the undisclosed meal delivery platform:
+ keep data only for the three target citis:
* Bordeaux
* Lyon
* Paris
+ merge duplicates
* it appears as redundant addresses were created
for each order by the same customer
=> significant reduction in the number of addresses
* propagate the merges to the other tables
that reference records merged away
+ cast data types and keep their scopes narrow
+ normalize the data
+ remove obvious outliers
+ adjust/discard unplausible values
- map the cleaned data onto the ORM models
- store the cleaned data in a new database schema
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| .github/workflows | ||
| docs | ||
| migrations | ||
| notebooks | ||
| src/urban_meal_delivery | ||
| tests | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| alembic.ini | ||
| LICENSE.txt | ||
| noxfile.py | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
| setup.cfg | ||
Urban Meal Delivery
This repository holds code analyzing the data of an undisclosed urban meal delivery platform operating in France from January 2016 to January 2017. The goal is to optimize the platform's delivery process involving independent couriers.
Structure
The analysis is structured into three aspects that iteratively build on each other.
Real-time Demand Forecasting
Predictive Routing
Shift & Capacity Planning
Installation & Contribution
To play with the code developed for the analyses,
you can clone the project with git
and install the contained urban-meal-delivery package
and all its dependencies
in a virtual environment
with poetry:
git clone https://github.com/webartifex/urban-meal-delivery.git
and
poetry install --extras research
The --extras option is necessary as the non-develop dependencies
are structured in the pyproject.toml file
into dependencies related to only the urban-meal-delivery source code package
and dependencies used to run the Jupyter environment
with the analyses.
Contributions are welcome. Use the issues tab. The project is licensed under the MIT license.