AI-ML Based Price Prediction for Agri-Horticultural Commodities

Category: Software
Organization: Ministry of Consumer Affairs, Food and Public Distribution

Abstract

The project proposes developing an AI-ML Based Price Prediction Model for agri-horticultural commodities using Python. The model will forecast prices of essential commodities like pulses and vegetables.

Features

• Time Series Analysis: Use ARIMA models and other machine learning algorithms to predict price trends.
• Market Data Integration: Incorporate real-time market data, historical prices, and supply chain factors into the model.
• Decision Support System: Provide actionable insights for market interventions and buffer stock management.

Expected Outcome: Accurate price predictions will help stabilize markets, prevent price volatility, and support strategic planning for both farmers and government agencies.

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