A Framework for Implementing Prediction Algorithm over Cloud Data as a Procedure for Cloud Data Mining
DOI:
https://doi.org/10.54060/JIEEE/002.02.021Keywords:
Heroku cloud, CatBoost algorithm, prediction model, binary ClassificationAbstract
The cloud has become an important phrase in data storage for many reasons. Cloud services and applications are widespread in many industries including healthcare due to easy access. The limitless quantity of data available on the clouds has triggered the interest of many researchers in the recent past. It has forced us to deploy machine learning for analyzing the data to get insights as well as model building. In this paper, we have built a service on Heroku Cloud which is a cloud platform as a service (PaaS) and has 15 thousand records with 25 features. The data belongs to healthcare and is related to post-surgery complications. The boost prediction algorithm was applied for analysis and implementation was done in python. The results helped us to determine and tune some of the hyperparameters which have correlations with complications and the reported accuracy of training and testing was found to be 91% and 88% respectively.
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