Artificial Intelligence is a superset of Machine Learning and Deep learning, used to build intelligent systems that can solve problems. Software Effort Estimation is used to predict the number of hours of work required to complete the task. It is difficult and a challenging task to forecast Software Effort in the project during initial stages, due to several uncertainties. Software Effort Estimation helps in planning, scheduling, budgeting a project. Various experiments were proposed to predict effort alike expert judgment, analogy based estimations, regression estimations, classification approaches, deep learning algorithms. In this paper, comparison of deepnet, neuralnet, support vector machine and random forest algorithms were carried out and the results show that random forest outperforms other algorithms because of its robustness and capacity to handle large datasets. Evaluation metrics deliberated are Mean Absolute Error, Root Mean Squared Error, Mean Square Error and R-Squared.
For software projects that deploy vital tasks, it is difficult to estimate the effort of a project. In order to anticipate a few hours of labour effort (either time or personal) to deploy or maintain the software programme, software measurement points must be used. It is difficult to predict the behavior of an application that is engaged in software development during the initial phases of the effort. The hybrid model technique is used in this paper. It is necessary to apply Supervised Learning techniques in the Machine Learning algorithm. It is further subdivided into different types, such as linear regression, logistic regression, SVM (Support Vector Machine) algorithm, Naive Bayes algorithm, PCR(Principal Component Regression) algorithm, (Neural Network)NNET algorithm, KNN (K-Nearest Neighbour) algorithm, K-means, Random Forest algorithm, Dimensionality reduction algorithms, Gradient boosting algorithm, and Ada Boosting algorithm, to name a few examples. PCR algorithm and the nnet algorithm have been utilised for hybrid method, as shown above. Predictions from JM1/Software have been used to create this data collection. With 10886 unique instances and 18 unique traits, this is a very large amount. Metrics for evaluating this system include Mean Absolute Error (MAE), Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), Percentage of Predictive Accuracy (PRED), and R-squared. Compared to single model approaches based on machine learning algorithms techniques, the proposed hybrid using principal component regression and neural networks produced the best results, as demonstrated by the results.
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