This project aims to predict the Standardized Precipitation Index (SPI12) for the next year using historical precipitation and SPI data. Additionally, it offers language selection functionality, allowing users to choose from multiple Indian languages for the interface. The translations are provided using the Googletrans Python library. The frontend of the application is developed using Streamlit, offering a user-friendly web app experience. Furthermore, the project integrates map visualization using Folium.
- Prediction of SPI12 for the next year based on historical data using FbProphet.
- Language selection for the interface, supporting various Indian languages.
- Translation of interface elements using the Googletrans Python library.
- User-friendly web app interface built with Streamlit.
- Integration of map visualization using Folium for enhanced data representation.
- Utilization of Pandas and Scikit-learn for data processing and machine learning tasks.
- Pandas: Data manipulation and preprocessing.
- Scikit-learn: Machine learning model training and prediction.
- Streamlit: Web app development framework for creating the frontend.
- Folium: Python library for map visualization.
- Googletrans: Python library for language translation using Google Translate.