DeliveryOptimizer is a project developed to optimize delivery logistics using clustering techniques. The goal is to group deliveries efficiently, reducing the time and cost of logistics operations.
- Data Analysis: Import and analyze delivery data.
- Clustering: Group deliveries using the MiniSom model (Self-Organizing Maps).
- MiniSom: MiniSom is a Python implementation of Self-Organizing Maps (SOM), an unsupervised learning technique that maps high-dimensional data into a lower-dimensional space while preserving the data topology. This is useful for identifying patterns and forming clusters of deliveries with similar characteristics.
- Visualization: Generate charts and maps to visualize clusters and optimized routes.
- Route Optimization: Implement algorithms to find the most efficient routes within each cluster.
- Python 3.x
- Jupyter Notebook
- Python Libraries: pandas, numpy, minisom, matplotlib, seaborn
- Clone the repository:
git clone https://github.com/Andriow/DeliveryOptimizer.git
- Navigate to the project directory:
cd DeliveryOptimizer - Install the dependencies:
pip install -r requirements.txt
- Open Jupyter Notebook:
jupyter notebook
- Navigate to the
ClusterFrete.ipynbfile and run all cells to view the analysis and optimization of deliveries.
Contributions are welcome! Feel free to open issues and pull requests.
This project is licensed under the MIT License. See the LICENSE file for more details.