Logistics Optimization with Causal Inference

Overview

This project aims to optimize the placement of Gokada drivers in Lagos, Nigeria, using causal inference and machine learning techniques to reduce the number of unfulfilled delivery requests.

Installation

To install the required dependencies, run:

pip install -r requirements.txt

Usage

Explore data and perform feature engineering using the notebooks in the notebooks directory. Run the scripts in the scripts directory to preprocess data, build causal graphs, train models, and perform optimization. Use the modules in the src directory for a more modular approach. Run tests using: pytest

Project Structure

logistics-optimization-with-causal-inference/
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_feature_engineering.ipynb
│   ├── 03_causal_inference.ipynb
│   ├── 04_model_training.ipynb
│   └── 05_optimization.ipynb
├── scripts/
│   ├── data_preparation.py
│   ├── feature_engineering.py
│   ├── causal_graph.py
│   ├── model_training.py
│   └── optimization.py
├── src/
│   ├── data/
│   │   ├── __init__.py
│   │   ├── load_data.py
│   │   └── preprocess.py
│   ├── features/
│   │   ├── __init__.py
│   │   ├── engineering.py
│   │   └── scaling.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── causal_model.py
│   │   ├── ml_model.py
│   │   └── evaluation.py
│   └── optimization/
│       ├── __init__.py
│       └── placement.py
├── tests/
│   ├── test_data_preparation.py
│   ├── test_feature_engineering.py
│   ├── test_causal_graph.py
│   ├── test_model_training.py
│   └── test_optimization.py
├── .github/
│   └── workflows/
│       └── ci-cd.yml
├── .gitignore
├── requirements.txt
├── README.md
└── setup.py