Online ISSN: 2515-8260

Crop Value Forecasting using Decision Tree Regressor and Model s

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AkshayPrassanna S, B A Harshanand, B Srishti, Chaitanya R, KirubakaranNithiyaSoundari, SwathiSriram, V Manoj Kumar, VarshithaChennamsetti, Venkateshwaran G, Dr.Pramod Kumar Maurya


Abstract – Machine Learning is an emerging research field which can be used for the analysis of crop price prediction and accurately provide solutions for the same. We can use this system as a backhand while we decide what a farmer should plant while considering factors such as annual rainfall, WPI and so on which is provided from the dataset and produce a logical conclusion on which products would give a more reliable outcome. The performance between Random forest ensemble learning and decision tree regressor is compared and it has been observed that the Random Forest Ensemble learning method gives a higher accuracy. In this system there are 23 crops whose information can be accessed upon for deciding collaborated with a simple user friendly UI

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