Abstract:
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, Naïve 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 show that, Random Forest and decision tree models were performed the highest accuracy and precision than
others. Therefore, the Random Forest predictive modeling has been used to predict the determinant factors from small and large datasets.
