SMJajoo/PIMA-data-modeling-and-analysis

This project looks at the effectiveness of SICE imputation technique in supporting binary classification performance of the Logisitic Regression in the context of decision support for the healthcare sector where accurate predictive models have the potential to improve patient outcomes by promoting access to care. Processing methodologies are assesed with reference to the relevant Accuracy and False Positive metrics. Analysis and comparison of the results suggest that imputation alone can likely impact classifier performance only marginally in situations where the data set is of sufficient size.

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