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Abstract: Machine learning is a technique of optimizing a performance criterion using example data and past experience. Data in machine learning plays a key role, and machine leaning tools are used to discover and learn knowledge from the datasets stored.
The purpose of this research is to build a model that can predict the determinant factors for crop production status using machine learning techniques as a means of visualizing the data. In order to conduct this research supervised machine learning techniques were employed. For the purpose of this research, the datasets were collected from selected region agricultural offices.
The data sets used for the training and testing of the predictive model is 10,000 instances with 41 regular attributes. As a result, for identifying the determinant factors Rapid Miner machine learning tool was used. In order to find the best predictive modeling technique different experiments were conducted using Random Forest, Decision tree, NaÃ¯ve Bays and ID3 predictive models. To validate the predictive performance of the selected models split and cross validation testing methods was used.
As the findings of this research shows that, Random Forest and decision tree models were performed the highest accuracy and precision than others. Therefore, the Random Forest predictive modeling have been used to predict the determinate factors form small and large datasets.
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