AI-ML Based Price Prediction for Agri-Horticultural Commodities

This project aims to develop an AI-ML based model for predicting the prices of essential agri-horticultural commodities such as pulses and vegetables (onion, potato, wheat, rice, etc.). The system helps the Department of Consumer Affairs analyze price trends and optimize buffer stock release decisions.

Project Structure

The project is organized into three main folders:

• frontend: React + Vite application with Material UI
• backend: Flask API with MongoDB integration and ML models
• database: MongoDB schemas and initialization scripts

Features

1. Dashboard

   • Current Market Prices
   • Price Forecast & Trends
   • Market Volatility & Risk Indicator
   • Alerts Section

2. Buffer Stocks Page

   • Overview of Buffer Stock Levels
   • Recent & Upcoming Stock Releases
   • Price Stabilization Measures & Interventions

3. AI Chatbot

   • Suggests insights and helps users with price trends

Tech Stack

Frontend

• React + Vite
• Material UI
• Recharts for data visualization

Backend

• Flask/FastAPI
• MongoDB Cloud
• Machine Learning Models (LSTM, XGBoost)

Data Sources

• Current Market Price: DCA - CEDA
• Price Deviation from Seasonal Average: Data.gov.in
• Supply Side (Stock Levels in Buffer Storage): FCI
• Crop Production Estimates: DES Agri
• Inflation and Economic Indicators: RBI

Getting Started

Prerequisites

• Node.js (v16+)
• Python (v3.8+)
• MongoDB

Installation

1. Clone the repository

   git clone https://github.com/yourusername/price-prediction-project.git
   cd price-prediction-project