crop-predictor

🚜 A simple web application built using Flask and a trained machine learning model to predict the most suitable crop based on soil and climate conditions. Users input values like nitrogen, phosphorus, rainfall, and temperature to get real-time crop suggestions. 💡 Powered by Python, Flask, HTML/CSS, and a scikit-learn model serialized with Pickle.

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🚀 Features

• User-friendly HTML form for input
• Real-time crop prediction using a trained ML model
• Lightweight and easy to deploy locally or online
• Built with Flask and Pickle for backend model handling

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📁 Files to Include

• app.py – Main Flask application
• templates/myhtml.html – Frontend form (Jinja2 templating)
• model.pkl – Pre-trained machine learning model
• requirements.txt – List of required Python libraries
• README.md – Project documentation

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⚙️ How to Run Locally

1. Clone this repo

   git clone https://github.com/yourusername/crop-predictor.git
   cd crop-predictor

Install dependencies

pip install -r requirements.txt

Run the app python app.py Open your browser and go to http://127.0.0.1:5000/

🧠 Model Input Format The model expects the following numerical inputs:

-Nitrogen (N) -Phosphorus (P) -Potassium (K) -Temperature (°C) -Humidity (%) -pH level -Rainfall (mm)

📦 Requirements Python 3.x Flask scikit-learn numpy pickle

You can install all dependencies using:

pip install -r requirements.txt

🌐 Deployment To make the app publicly available, consider deploying it using:

-Render -Replit -PythonAnywhere

🙌 Acknowledgements -Dataset from Kaggle’s crop recommendation dataset -Model trained using scikit-learn -Flask documentation and tutorials

👤 Author Eman Ihsan

GitHub: @eman06