The Ration Card Type Prediction app is designed to classify households into different ration card types based on several socioeconomic features. It employs a Random Forest Classifier for prediction, enabling users to interactively input household data to receive predictions in real-time.
Features
Data Processing and Label Encoding: Preprocesses data with label encoding to make it compatible with machine learning models.
Machine Learning Model: Uses a Random Forest model for classification.
Interactive UI with Streamlit: A simple and responsive interface allows users to input data and receive predictions.
Google Colab and Local Tunnel for Deployment: The app runs on Google Colab and can be accessed publicly via local tunnel.
Tech Stack
Python: Primary language for data processing, model training, and deployment.
Pandas, Scikit-Learn: Libraries for data handling and machine learning.
Streamlit: For the interactive web app interface.
pyngrok: To expose the app to the web from Google Colab.