Predictive analytics approaches for software effort estimation: A review

lowast, A G Priya Varshini; K, Anitha Kumari

doi:10.17485/ijst/v13i21.573

Cited by 23 publications

(8 citation statements)

References 26 publications

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“…The goal of linear regression is to identify the weights (w0 and w1) that produce the line that fits the input data the best (i.e. x features) [8].…”

Section: Linear Regressionmentioning

confidence: 99%

Predictive Data Analysis Using Linear Regression and Random Forest

Ogunleye¹

2023

Data Integrity and Data Governance

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A statistical technique called predictive analysis (or analytics) makes use of machine learning and computers to find patterns in data and forecasts future actions. It is now preferred to go beyond descriptive analytics in order to learn whether training initiatives are effective and how they may be enhanced. Data from the past as well as the present can be used in predictive analysis to make predictions about what might occur in the future. Businesses can improve upcoming learning projects by taking actionable action after identifying the potential risks or possibilities. This chapter compares two predictive analysis models used in the predictive analysis of data: the Generalized Linear Model with Linear Regression (LR) and the Decision Trees with Random Forest (RF). With an RMSE (Root Mean Square Error) of 0.0264965 and an arithmetic mean for all errors of 0.016056967, Linear Regression did better in this analysis than Random Forest, which had an RMSE of 0.117875 and an arithmetic mean for all errors of 0.07062315. Through the hyper-parameter tuning procedure, these percentage errors can still be decreased. The combined strategy of combining LR and RF predictions, by averaging, nevertheless produced even more accurate predictions and will overcome the danger of over-fitting and producing incorrect predictions by individual algorithms, depending on the quality of data used for the training.

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“…The goal of linear regression is to identify the weights (w0 and w1) that produce the line that fits the input data the best (i.e. x features) [8].…”

Section: Linear Regressionmentioning

confidence: 99%

Predictive Data Analysis Using Linear Regression and Random Forest

Ogunleye¹

2023

Data Integrity and Data Governance

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“…Several single models were combined into an ensemble technique by Priya Varshini, A.G., and others. Averaging, weighted averaging, bagging, boosting, and stacking were among the ensemble estimation strategies explored [11]. Random Forest-Based Stacked Ensembling Approach is the name of the method.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning approach for Software Effort Estimation using Combination of Principal Component Regression and Neural Network

Varshini¹,

Kumari

Bhalavaishnavi³

et al. 2022

J. Phys.: Conf. Ser.

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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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“…The Mean Squared Error (MSE) was used to calculate the homogeneity of a numerical sample. The MSE can be calculated as [22].…”

Section: Decision Treementioning

confidence: 99%

Machine Learning in Predicting Lattice Constant of Cubic Perovskite Oxides

Poudel¹,

Bhusal²,

Bhurtel

et al. 2022

J. Nep. Phys. Soc.

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A sample of 3,115 data of perovskite oxides in the form of ABO3 (A and B being the cations) was taken for this study of the application of machine learning in predicting the lattice constants (a determining factor in material design). The ANN, DT, RF, KNN, and SVM models were used to predict the lattice constants of perovskites because machine learning techniques have been phenomenal in uncovering crystal structures in the field of material research in recent years. These models used properties like ionic radii, formation energy, and band gap as input features. The R2 score was used to assess the regression model’s performance. The Random Forest Regression Model outperforms all other regression models regarding dataset and features.

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Predictive analytics approaches for software effort estimation: A review

Cited by 23 publications

References 26 publications

Predictive Data Analysis Using Linear Regression and Random Forest

Predictive Data Analysis Using Linear Regression and Random Forest

Machine Learning approach for Software Effort Estimation using Combination of Principal Component Regression and Neural Network

Machine Learning in Predicting Lattice Constant of Cubic Perovskite Oxides

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