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This serves as a simple demonstration of prediction and recommendation systems.  \n\n🚀 **Live Demo**: [FarmIQ on Streamlit](https://farmiq.streamlit.app/)  \n\n## Features  \n✅ **Crop Recommendation** – Suggests the best crops based on soil and climate data.  \n✅ **Fertilizer Recommendation** – Provides optimal fertilizer recommendations for better yield.  \n✅ **Streamlit Frontend** – A simple, interactive UI for users to input data and receive predictions.  \n✅ **SQLite Database** – Stores historical data for insights and analysis.  \n✅ **Machine Learning Models** – Uses trained models for accurate recommendations.  \n\n## Tech Stack  \n- **Frontend**: Streamlit  \n- **Backend**: Python  \n- **Database**: SQLite  \n- **Machine Learning**: Scikit-learn  \n- **Version Control**: GitHub  \n\n## Installation \u0026 Setup  \n```bash\n# Clone the repository\ngit clone https://github.com/Mugilan1309/FarmIQ.git\ncd FarmIQ\n\n# Install dependencies\npip install -r requirements.txt\n\n# Run the Streamlit app (Ensure all files are in the same directory)\nstreamlit run app.py\n```\n© 2025 Mugilan1309 | FarmIQ\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmugilan1309%2Ffarmiq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmugilan1309%2Ffarmiq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmugilan1309%2Ffarmiq/lists"}