This project is a machine learning model that predicts food prices in Mozambique. By leveraging historical data and various features, we aim to provide insights into price trends, which can be invaluable for both consumers and businesses in the food industry.
Explore the project's articles for a deeper understanding:
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Building the Model - This article provides a detailed guide on how to construct the predictive model, making it an ideal resource for understanding the technical aspects. Read the tutorial.
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Understanding the Model - Dive into an in-depth analysis of the model's socioeconomic implications, shedding light on the broader context and real-world applications. Read the analysis.
In order to get started with this project, you'll need Python and a few libraries. You can install the necessary libraries by running:
pip install -r requirements.txtThe dataset used for this project includes historical food price information, along with relevant features such as location, time, and economic indicators. You can find the dataset in the data directory.
• Name: Mozambique - Food Prices
• Source: https://data.humdata.org/dataset/wfp-food-prices-for-mozambique
• License: Creative Commons Attribution for Intergovernmental Organisations
• data/: Contains the dataset used for training and testing.
• notebooks/: Jupyter notebooks for data exploration, model development, and evaluation.
• requirements.txt: A list of Python packages required for this project.
To use the model for making food price predictions, you can follow the example code provided in the notebooks directory. We recommend using Jupyter notebooks for interactive exploration and prediction.
We welcome contributions and bug reports. If you'd like to contribute to this project, please follow the standard GitHub Fork and Pull Request workflow.
This project is licensed under the MIT License. Feel free to use, modify, and distribute this code for your own purposes.