Machine learning, Cost estimation, Prediction, Weka, Algorithms, Classification, Prediction models.
 S. Kumari and S. Pushkar, "Cuckoo search based hybrid models for improving the accuracy of software effort estimation," Microsystem Technologies, vol. 24, pp. 4767-4774, 2018. Available at: https://doi.org/10.1007/s00542-018-3871-9.
 P. Pospieszny, B. Czarnacka-Chrobot, and A. Kobylinski, "An effective approach for software project effort and duration estimation with machine learning algorithms," The Journal of Systems & Software, vol. 137, pp. 184–196, 2018. Available at: https://doi.org/10.1016/j.jss.2017.11.066.
 K. Langsari, R. Sarno, and Sholiq, "Optimizing effort parameter of COCOMO II using particle swarm optimization method," Telkomnika, vol. 16, pp. 2208-2216, 2018. Available at: https://doi.org/10.12928/telkomnika.v16i5.9703.
 I. Attarzadeh and S. H. Ow, "Improving estimation accuracy of the COCOMO II using an adaptive fuzzy logic model," presented at the 2011 IEEE International Conference on Fuzzy Systems, Taipei, Taiwan, 2011.
 R. Litoriya, N. Sharma, and D. A. Kothari, "Incorporating cost driver substitution to improve the effort using Agile COCOMO II," presented at the 2012 CSI Sixth International Conference on Software Engineering, 2012.
 R. Saljoughinejad and V. Khatibi, "A new optimized hybrid model based On COCOMO to increase the accuracy of software cost estimation," Journal of Advances in Computer Engineering and Technology, vol. 4, pp. 27-40, 2018.
 Z. Chen, T. Menzies, D. Port, and B. Boehm, "Feature subset selection can improve software cost estimation accuracy," ACM SIGSOFT Software Engineering Notes, vol. 30, pp. 1-6, 2005. Available at: https://doi.org/10.1145/1082983.1083171.
 Z. A. Khalifelu and F. S. Gharehchopogh, "Comparison and evaluation of data mining techniques with algorithmic models in software cost estimation," Procedia Technology, vol. 1, pp. 65-71, 2012. Available at: https://doi.org/10.1016/j.protcy.2012.02.013.
 P. A. Whigham, C. A. Owen, and S. G. Macdonell, "A baseline model for software effort estimation," ACM Transactions on Software Engineering and Methodology, vol. 24, pp. 1-11, 2015. Available at: https://doi.org/10.1145/2738037.
 Y. Masoudi-Sobhanzadeh, H. Motieghader, and A. Masoudi-Nejad, "Feature select: A software for feature selection based on machine learning approaches," BMC Bioinformatics, vol. 20, pp. 1-17, 2019. Available at: https://doi.org/10.1186/s12859-019-2754-0.
 V. Vig and A. Kaur, "Test effort estimation and prediction of traditional and rapid release models using machine learning algorithms," Journal of Intelligent & Fuzzy Systems, vol. 35, pp. 1657-1669, 2018. Available at: https://doi.org/10.3233/jifs-169703.
 A. Khalid, M. A. Latif, and M. Adnan, "An approach to estimate the duration of software project through machine learning techniques," Gomal University Journal of Research, vol. 33, pp. 1-13, 2017.
 T.-H. Yeh and S. Deng, "Application of machine learning methods to cost estimation of product life cycle," International Journal of Computer Integrated Manufacturing, vol. 25, pp. 340-352, 2012. Available at: https://doi.org/10.1080/0951192x.2011.645381.
 M. D. Ganggayah, N. A. Taib, Y. C. Har, P. Lio, and S. K. Dhillon, "Predicting factors for survival of breast cancer patients using machine learning techniques," BMC Medical Informatics and Decision Making, vol. 19, pp. 1-17, 2019. Available at: https://doi.org/10.1186/s12911-019-0801-4.
 P. Pandey, "Analysis of the techniques for software cost estimation," presented at the 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT), Rohtak, India, 2013.
 J. Rahikkala, S. Hyrynsalmi, V. Leppänen, and I. Porres, "The role of organisational phenomena in software cost estimation: A case study of supporting and hindering factors," E-Informatica Software Engineering Journal, vol. 12, pp. 167–198, 2018.
 M. Vyas, A. Bohra, D. C. Lamba, and A. Vyas, "A review on software cost and effort estimation techniques for agile development process," International Journal of Recent Research Aspects, vol. 5, pp. 612-618, 2016.
 S. A. Woznicki, J. Baynes, S. Panlasigui, M. Mehaffey, and A. Neale, "Development of a spatially complete floodplain map of the conterminous United States using random forest," Science of the Total Environment, vol. 647, pp. 942-953, 2019. Available at: https://doi.org/10.1016/j.scitotenv.2018.07.353.
 S. Kalmegh, "Analysis of weka data mining algorithm reptree, simple cart and randomtree for classification of Indian news," International Journal of Innovative Science, Engineering & Technology, vol. 2, pp. 438-446, 2015.
 S.-A. Blaifi, S. Moulahoum, R. Benkercha, B. Taghezouit, and A. Saim, "M5P model tree based fast fuzzy maximum power point tracker," Solar Energy, vol. 163, pp. 405-424, 2018. Available at: https://doi.org/10.1016/j.solener.2018.01.071.
 T. Rajasekaran, P. Jayasheelan, and K. S. Preethaa, "Predictive analysis in agriculture to improve the crop productivity using zeroR algorithm," International Journal of Computer Science and Engineering Communications, vol. 4, pp. 1397-1401, 2016.
 P. Singh and S. Agrawal, "Node localization in wireless sensor networks using the M5P tree and SMOreg algorithms," presented at the 2013 5th International Conference and Computational Intelligence and Communication Networks. IEEE, 2013.