Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/4650
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dc.contributor.supervisorBaliyan, Niyati-
dc.contributor.authorJindal, Kapil-
dc.date.accessioned2017-08-11T11:21:49Z-
dc.date.available2017-08-11T11:21:49Z-
dc.date.issued2017-08-11-
dc.identifier.urihttp://hdl.handle.net/10266/4650-
dc.descriptionMaster of Engineering -CSEen_US
dc.description.abstractAt the present time, obesity is a serious health problem which causes many diseases such as - diabetes, cancer and heart ailments. Obesity, in turn, is caused by accumulation of excess fat. There are many determinants of obesity, namely, age, weight, height, and Body Mass Index. The value of obesity can be computed in numerous ways, however, they are not generic enough to be applied in every context (such as to a pregnant lady or to an old man) and yet provide accurate results. To this end, we employ the R ensemble prediction model and implement the same using Python interface. It is observed that on an average, the predicted values of obesity are 89.68% accurate, which is an improvement over previous such works. The Ensemble Machine Learning based prediction leverages Generalized Linear Model, Random Forest, and Partial Least Squares. The current work can further be improvised to predict other health parameters and recommend corrective measures based on obesity values.en_US
dc.language.isoenen_US
dc.subjectObesityen_US
dc.subjectMachine Learningen_US
dc.subjectPredictionen_US
dc.titleObesity Prediction using Ensemble Machine Learningen_US
dc.typeThesisen_US
Appears in Collections:Masters Theses@CSED

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