Paper Key : IRJ************970
Author: Aiswarya A,Jaiaditya Nadar,P Selvaraj
Date Published: 12 Nov 2024
Abstract
Yield prediction using models like Regression, Decision Trees, Random Forest, XGBoost, and K-Nearest Neighbors (KNN) is crucial for modern agriculture, aiding in accurate forecasting and decision-making for farmers. This approach involves preprocessing data by removing missing values and creating features from categorical variables. Visual comparisons, shown through bar graphs, make it easy to identify the best-performing algorithms. The system is designed to be accessible, providing clear yield predictions based on inputs like land size, crop type, season, and year. Using advanced models like Random Forest and Gradient Boosting enhances prediction accuracy and resilience to overfitting, while visual indicators simplify complex data, helping farmers and stakeholders select the most suitable model for their needs.
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