🌾 Smart Farming Crop Yield Prediction Dashboard

An interactive dashboard for analyzing and predicting crop yield using agricultural input data. Built with PySpark, Streamlit, Plotly, Pandas, and packaged with Unified Python Packaging (uv).

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🚀 Features

• 📈 Predict crop yield using Linear Regression and Random Forest
• 🧮 Visualize actual vs. predicted yield and residuals
• 📊 Interactive charts, correlation heatmaps, and feature importance
• 🧩 Dynamic filters and dropdowns for granular data exploration
• 🧼 Clean, responsive layout with a smooth UX using Streamlit

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📷 Screenshots

Actual vs Predicted

Nitrogen vs Yield

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🗂️ Project Structure

smart-farm-predic/
│
├── proj.py                  # Main Streamlit dashboard logic
├── main.py                  # Optional entry point (can run proj.py)
├── smart_farming_crop_yield_prediction.csv
├── pyproject.toml           # Project metadata and dependencies (uv)
├── uv.lock                  # Locked dependencies (auto-generated)
├── .venv/                   # Virtual environment (optional)
├── assets/                  # Images and visual assets
└── README.md

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🛠️ Getting Started

Make sure Python >=3.10 and uv are installed.

1. Clone the Repository

git clone https://github.com/Aditya-jaiswal07972/Spark-Smart-Farm-yield-prediction.git
cd smart-farm-predic

2. Install Dependencies with uv

Using a virtual environment:

uv venv
uv pip install

Or install globally:

uv pip install --system

3. Run the App

# Option 1: Directly run the Streamlit app
streamlit run proj.py

# Option 2: Use the CLI entry point
python main.py

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📦 Tech Stack

All dependencies are managed via pyproject.toml. Key packages:

• streamlit – for building the interactive dashboard
• pyspark – for data processing at scale
• pandas, plotly-express, seaborn, matplotlib – for analysis and visualization

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👨‍🌾 Author

Made with ❤️ by Aditya Jaiswal

📄 License

Released under the MIT License. Feel free to fork, use, and contribute!