mistir-ketema0/Logistic-optimization-with-casual-inference

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. # 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: ```bash 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 ```bash 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 ```