{"id":30206825,"url":"https://github.com/kfrural/dashboard_agro","last_synced_at":"2026-05-15T20:02:22.941Z","repository":{"id":308192196,"uuid":"939564136","full_name":"kfrural/dashboard_agro","owner":"kfrural","description":"Dashboard Agro is a technological platform that integrates several components to support Brazilian agribusiness through data analysis, visualization and forecasts. 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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.\n\n## Technologies Used\n\n- **Python**: Main language for data analysis and model development.\n- **Jupyter Notebooks**: Interactive environment for exploration, documentation, and presentation of results.\n- **Pandas \u0026 NumPy**: Manipulation, cleaning, and transformation of large datasets.\n- **Scikit-learn**: Implementation of machine learning algorithms for prediction and statistical analysis.\n- **Matplotlib, Seaborn \u0026 Plotly**: Graphical visualization and construction of interactive dashboards.\n- **Big Data**: Techniques and tools for efficient processing of large and heterogeneous data volumes.\n- **Docker**: Containerization to facilitate deployment and ensure environment reproducibility.\n- **VS Code**: IDE used for development and integration of project components.\n\n## Analyses Performed\n\n- **Data Exploration and Cleaning**: Handling inconsistencies, filling missing values, and standardizing simulated data.\n- **Descriptive Analysis**: Identifying patterns, historical trends, and correlations between variables relevant to agricultural production.\n- **Feature Engineering**: Creating new attributes to enhance predictive model performance.\n- **Predictive Modeling**: Applying machine learning algorithms to forecast future production, identify risk factors, and highlight improvement opportunities.\n- **Interactive Visualization**: Developing dynamic dashboards that facilitate result interpretation and communication with different audiences.\n- **Model Evaluation**: Using statistical metrics to validate and compare implemented models.\n\n## Differentials\n\n- **Big Data Usage**: Ability to process and analyze large volumes of data, enabling more robust and comprehensive insights.\n- **Intelligent Prediction**: Machine learning models that anticipate scenarios and assist in strategic decision-making.\n- **User-Friendly Interface**: Interactive dashboards and intuitive visualizations to facilitate information access.\n- **Focus on Sustainability**: Support for efficient management of natural resources and promotion of sustainable agricultural practices.\n\n## Target Audience\n\n- Rural producers\n- Researchers and academics\n- Public and private managers in the agricultural sector\n- AgTech companies\n\n## How to Use\n\n1. Clone the repository and install the dependencies listed in `requirements.txt`.\n2. Run the notebooks to explore analyses and visualizations.\n3. Use the dashboards to monitor indicators and perform predictive simulations.\n4. Customize the models according to your needs and regional context.\n\n## Contribution\n\nContributions are welcome! Feel free to open issues, suggest improvements, or submit pull requests.\n\n## License\n\nThis project is licensed under the MIT License. See the LICENSE file for more details.\n\n---\n\n**Dashboard Agro**: Turning data into intelligent decisions for the future of Brazilian agriculture.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkfrural%2Fdashboard_agro","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkfrural%2Fdashboard_agro","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkfrural%2Fdashboard_agro/lists"}