International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Agricultural Product Price Prediction using Deep Learning Models |
|---|---|
| ABSTRACT | This study develops a machine learning workflow to predict agricultural product prices using daily market data from Indian government portals like AGMARKNET and UPag. Given the high volatility of commodity prices, accurate forecasting is critical for optimizing farmers' profits and supporting policymakers. The research applies Support Vector Machine (SVM) and Gradient Boosting models to analyze historical trends and market dynamics using an 80/20 train-test data split. The results demonstrate that SVM models outperform other approaches by effectively capturing complex, non-linear patterns and temporal dependencies, making them highly reliable tools for practical real-time agricultural economic forecasting. |
| AUTHOR | BINDUSHREE T N, CHETHAN M S M. Tech Student, Dept. of CSE, SIET, Tumkur, India Assistant Professor, Dept. of CSE, SIET, Tumkur, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406020 |
| pdf/20_Agricultural Product Price Prediction using Deep Learning Models.pdf | |
| KEYWORDS | |
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