Feature selection is a pre-processing procedure in machine learning which seeks to extract model predictors from a Large datasets to improve prediction accuracy. It is the most important technique that we use in this project. Feature selection algorithms extract essential features of the datasets and filter out the possible ones which affect the accuracy of the estimation methods. However, as the number of observations increases, the size of the feature expands, posing considerable computation and prediction accuracy problems for many traditional feature selection processes. In software engineering effort estimation, there are several machine learning ,feature selection, bio inspired algorithms strategies that have been studied. The selection approach suggested by jaya algorithm significantly reduces the data size that is the number of attributes and minimizes redundancy then reduces the dataset to achieve the low error rate compared with typical machine learning algorithms. China, Desharnais, Kemer and Maxwell are some of the datasets Used.
₹10000 (INR)
NON IEEE -2022