https://github.com/kfrural/dashboard_agro
Dashboard Agro is a technological platform that integrates several components to support Brazilian agribusiness through data analysis, visualization and forecasts. This innovative solution was developed to serve three main groups: farmers, researchers and public managers.
https://github.com/kfrural/dashboard_agro
big-data data-analysis predictive-analytics python
Last synced: about 1 month ago
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Dashboard Agro is a technological platform that integrates several components to support Brazilian agribusiness through data analysis, visualization and forecasts. This innovative solution was developed to serve three main groups: farmers, researchers and public managers.
- Host: GitHub
- URL: https://github.com/kfrural/dashboard_agro
- Owner: kfrural
- License: mit
- Created: 2025-02-26T18:37:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-04T15:51:32.000Z (11 months ago)
- Last Synced: 2025-08-13T15:59:57.016Z (10 months ago)
- Topics: big-data, data-analysis, predictive-analytics, python
- Language: Jupyter Notebook
- Homepage:
- Size: 55.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🌱 Dashboard Agro: Analysis and Prediction of Brazilian Agricultural Production
## Purpose
Dashboard Agro is an innovative solution focused on the analysis, visualization, and prediction of agricultural production in Brazil. Its main goal is to provide intelligent tools to support decision-making for producers, researchers, and public managers, promoting greater efficiency, sustainability, and competitiveness in the sector.
## Technologies Used
- **Python**: Main language for data analysis and model development.
- **Jupyter Notebooks**: Interactive environment for exploration, documentation, and presentation of results.
- **Pandas & NumPy**: Manipulation, cleaning, and transformation of large datasets.
- **Scikit-learn**: Implementation of machine learning algorithms for prediction and statistical analysis.
- **Matplotlib, Seaborn & Plotly**: Graphical visualization and construction of interactive dashboards.
- **Big Data**: Techniques and tools for efficient processing of large and heterogeneous data volumes.
- **Docker**: Containerization to facilitate deployment and ensure environment reproducibility.
- **VS Code**: IDE used for development and integration of project components.
## Analyses Performed
- **Data Exploration and Cleaning**: Handling inconsistencies, filling missing values, and standardizing simulated data.
- **Descriptive Analysis**: Identifying patterns, historical trends, and correlations between variables relevant to agricultural production.
- **Feature Engineering**: Creating new attributes to enhance predictive model performance.
- **Predictive Modeling**: Applying machine learning algorithms to forecast future production, identify risk factors, and highlight improvement opportunities.
- **Interactive Visualization**: Developing dynamic dashboards that facilitate result interpretation and communication with different audiences.
- **Model Evaluation**: Using statistical metrics to validate and compare implemented models.
## Differentials
- **Big Data Usage**: Ability to process and analyze large volumes of data, enabling more robust and comprehensive insights.
- **Intelligent Prediction**: Machine learning models that anticipate scenarios and assist in strategic decision-making.
- **User-Friendly Interface**: Interactive dashboards and intuitive visualizations to facilitate information access.
- **Focus on Sustainability**: Support for efficient management of natural resources and promotion of sustainable agricultural practices.
## Target Audience
- Rural producers
- Researchers and academics
- Public and private managers in the agricultural sector
- AgTech companies
## How to Use
1. Clone the repository and install the dependencies listed in `requirements.txt`.
2. Run the notebooks to explore analyses and visualizations.
3. Use the dashboards to monitor indicators and perform predictive simulations.
4. Customize the models according to your needs and regional context.
## Contribution
Contributions are welcome! Feel free to open issues, suggest improvements, or submit pull requests.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.
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**Dashboard Agro**: Turning data into intelligent decisions for the future of Brazilian agriculture.