• G.Lakshmidurga
• D Anveshini
• Kommalapati Haasika
• Triveedi Anjali
• Penumetcha Varshita , Shaik Shabana

Since more than half of the country people are depending on agriculture for its subsistence yield Random forests are versatile, very fast but coarse machine learning algorithm that performs both classification and regression tasks. The study results will be helps the farmer to understand their goods in how much they output before growing on Agriculture field. User given Input: Area & Soil Type. Some Machine Learning algorithms have been trained over the dataset to predict crop yield like SVM (Support Vector Machine commit[]).To predict the crop yield, various ML algorithms like Support Vector Machine (SVM), Artificial Neural Network(ANN), Random Forest(RF) and Multivariate Linear Regression (MLR) and K-Nearest Neighbour(KNN). Random Forest had the best performance of 95% accuracy among them.

Predictive Analytics, Agriculture, Crop Yield Forecasting, Machine Learning, Data Analysis, Agricultural Technology, Precision Farming, Big Data, Remote Sensing, Environmental Factors, Soil Quality, Weather Prediction

"PREDICTIVE ANALYTICS IN AGRICULTURE ENHANCING CROP YIELDING FORECASTING – MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.194-202, November-2024, Available :http://www.jetir.org/papers/JETIRGO06019.pdf

"PREDICTIVE ANALYTICS IN AGRICULTURE ENHANCING CROP YIELDING FORECASTING – MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp194-202, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06019.pdf