🌾 Crop-Prediction-Project

This project uses machine learning to recommend the most suitable crop to grow based on environmental and soil conditions. It's built with a trained model and deployed via a simple web app interface using Flask.

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🚀 Features

• Predicts the best crop to grow using input features such as nitrogen, phosphorous, potassium, temperature, humidity, pH, and rainfall.
• Trained using scikit-learn with multiple ML algorithms.
• Simple and responsive UI using Flask.

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🧪 How to Run

Step 1- Clone the Repository

git clone https://github.com/your-username/Crop-Prediction-Project.git
cd Crop-Prediction-Project

Step 2- Create a Virtual Environment

python -m venv venv

Step 3- Install the requirements

pip install -r requirements.txt

Step 4- Train the Model

python model.py

Step 5- Run the Flask App

python app.py