Diabetes Prognostication Using Machine Learning

Data mining involves the extraction of meaningful information from data, and its applications span various domains such as finance, retail, medicine, and agriculture. Within the realm of agriculture, data mining proves invaluable for analyzing both living (biotic) and non-living (abiotic) factors. In the context of India, agriculture plays a pivotal role in the economy and employment sector. A prevalent issue among Indian farmers is the inadequate selection of crops based on soil requirements, resulting in significant productivity setbacks. Precision agriculture emerges as a solution to this challenge, employing modern farming techniques that leverage research data on soil characteristics, types, and crop yields. Precision agriculture assists farmers in making informed decisions by recommending suitable crops based on site-specific parameters, ultimately mitigating the risk of erroneous crop choices and enhancing overall productivity. This paper addresses the farmers’ crop selection predicament by proposing a recommendation system. The system utilizes an ensemble model with a majority voting technique, incorporating Random Tree, CHAID, K-Nearest Neighbor, and Naive Bayes as learners. The goal is to recommend crops with high accuracy and efficiency tailored to specific site parameters.