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A robust framework to predict diabetes based different independent attributes. Outlier rejection, filling the missing values, data standardization, K-fold validation, and different Machine Learning (ML) classifiers were used to create optimal model.Finally, optimal model was deployed on a PaaS .

Home Page: https://www.irjet.net/volume8-issue8

Jupyter Notebook 99.34% Python 0.48% HTML 0.17%
data-processing data-visualization diabetes-prediction flask heroku-deployment html-css-javascript machine-learning-algorithms missing-values outlier-detection zscore

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