This project focuses on predicting crop prices and trends to aid farmers, traders, and stakeholders in making informed decisions. By leveraging advanced machine learning techniques and authenticated datasets, the application provides accurate forecasts, insightful analysis, and a user-friendly interface.
- Crop Value Forecasting:
- Forecast prices for around 23 commodities, including various crops.
- Detailed crop forecasts up to the next 12 months.
- Insights on Market Trends:
- Identify top gainers and losers in the current market.
- High Accuracy:
- Crop price prediction with 93-95% accuracy.
- Data-Driven Analysis:
- Powered by datasets from data.gov.in.
- Detailed analysis of crop prices using tables and charts.
- Advanced Machine Learning:
- Predictions are made using Decision Tree Regression techniques.
- Incorporates annual rainfall and Wholesale Price Index (WPI) datasets for robust training.
- User-Friendly Interface:
- Clean and intuitive UI designed with MaterializeCSS.
- Interactive charts powered by Chart.js.
- Backend: Python (3.0 or above), Flask
- Machine Learning: Scikit-Learn, Decision Tree Regressor
- Frontend: MaterializeCSS, Chart.js
To install and run this web application, ensure you have Python (3.0 or above) and pip installed on your system.
- Clone the repository:
git clone https://github.com/DJay2012/Crop_prediction.git
- Navigate to the project directory:
cd Crop_Prediction - Install the required dependencies:
pip install -r requirements.txt
- Run the application:
python app.py
- Open your web browser and go to:
http://127.0.0.1:5000/
(Add screenshots here to showcase the application's UI and features.)
Dhananjay Pathak
Made with 💖 for empowering agriculture through technology. 🌾