Treffer: Agricultural Price Forecasting Using Flask Python Application

Title:
Agricultural Price Forecasting Using Flask Python Application
Source:
Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080); Volume 1, Issue 2, (August, 2024); 37-42; 3048-7080; 3049-3056
Publisher Information:
Journal of Intelligent Data Analysis and Computational Statistics (p-ISSN: 3049-3056 e-ISSN: 3048-7080) 2024-07-17
Document Type:
E-Ressource Electronic Resource
Availability:
Open access content. Open access content
Copyright (c) 2024 Journal of Intelligent Data Analysis and Computational Statistics
Note:
application/pdf
English
Other Numbers:
INMAT oai:ojs2.matjournals.net:article/705
1519325652
Contributing Source:
MAT JOURNALS
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1519325652
Database:
OAIster

Weitere Informationen

Flask is a Python web development framework that implements a predictive model for commodity futures. The system gathers data from various commodity futures databases, applies machine learning algorithms to analyze it, and provides users with projections of future price movements. Users can access commodity profiles, current pricing, and predictions for various agricultural products through a web interface. The system utilizes decision tree regression models to forecast future prices by considering underlying factors influencing agricultural markets and historical data. This integration of machine learning algorithms and online technologies enhances the stability and efficiency of market economies. It enables stakeholders to make well-informed decisions in the trade of agricultural commodities, thereby improving overall market operations. By providing accurate and timely information, the system supports better strategic planning and risk management for farmers, traders, and investors. The seamless combination of predictive analytics and user-friendly web interfaces represents a significant advancement in agricultural market forecasting.