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Evaluating Imputation Methods to Improve Data Availability in a Software Estimation Dataset
International journal of recent technology and engineering
Missing of partial data is a problem that is prevalent in most of the datasets used for statistical analysis. In this study, we analyzed the missing values in ISBSG R1 2018 dataset and addressed the problem through imputation, a machine learning technique which can increase the availability of data. Additionally, we compare the performance of three imputation methods: Classification and Regression Trees (CART), Polynomial Regression (PR), Predictive Mean Matching (PMM), and Random Forest (RF)doi:10.35940/ijrte.b1025.0982s1119 fatcat:ttdq7ecsabfo5jrh6l4srmgote