Crop Recommendation System

This repository contains the implementation of a Crop Recommendation System. The system is designed to help farmers choose the most suitable crops for their land based on various environmental and soil parameters. Leveraging machine learning techniques, the system analyzes data inputs such as soil type, weather conditions, and historical crop yield data to provide recommendations.

Features

• Data Collection: Collects data on soil type, weather, and other environmental factors.
• Machine Learning Model: Uses a trained machine learning model to analyze data and provide crop recommendations.
• User Interface: Simple web interface to input data and get recommendations.
• Performance Metrics: Evaluates model performance using accuracy, precision, and recall.

Technologies Used

• Flask: For building the web interface.
• Scikit-Learn: For machine learning model training and deployment.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• HTML/CSS: For building the web front-end.

Getting Started

Prerequisites

• Python 3.8 or higher
• Flask
• Scikit-Learn
• Pandas
• NumPy

Installation

1. Clone the repository:

   git clone https://github.com/delosreyesjohnpaul/Crop-Recommendation-System.git

2. Navigate to the project directory:

   cd Crop-Recommendation-System

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Run the Flask application:

   flask run

2. Open your web browser and navigate to http://127.0.0.1:5000.
3. Input the required data and get crop recommendations.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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