AGRICULTURAL PRODUCT PRICE AND CROP CULTIVATION PREDICTION BASED ON DATA SCIENCE TECHNIQUE

Abstract

Agriculture is primarily responsible for increasing the nation's economic contribution across the world. However, due to a lack of implementation of ecosystem control technology, the majority of agricultural lands remain underdeveloped. Crop output is not improving as a result of these issues, which has an impact on the agricultural sector. As a result, agricultural production is increasing as a result of plant yield prediction. To avoid this issue, agricultural industries must use machine learning algorithms to forecast crop yield from a given dataset. The use of supervised machine learning techniques (SMLT) to analyse datasets in order to capture various information such as variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments, and so on. A review among machine learning algorithms was conducted to see which one was the most competent in predicting the best crop. The findings reveal that the suggested machine learning algorithm approach has the greatest accuracy when comparing entropy calculation, precision, recall, F1 Score, sensitivity, specificity, and entropy. Key Words: Algorithms, Decision making, Agriculture, Data Science Techniques.