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<speak> See here.<break strength="x-strong"/> Here, we used 11 neighbors.<break strength="x-strong"/> So, approximately we got the same accuracy with this.<break strength="x-strong"/> In this project, you can use various other models also.<break strength="x-strong"/> You can use the random forest.<break strength="x-strong"/> You can use logistic regression.<break strength="x-strong"/> You can use support vector machines.<break strength="x-strong"/> You can try different models, and you can predict which model works better. <break strength="x-strong"/> As this is a health domain project, it is the key to have a data set with quality data.<break strength="x-strong"/> Domain expertise people should involve in the implementation of this type of project.<break strength="x-strong"/> Suppose, you don't know the health domain, and you want to build a machine learning or data science project. You deployed it in the cloud.<break strength="x-strong"/> So without domain expertise, you cannot do that. <break strength="x-strong"/>You don't know the different values and different terms used in the health domain. <break strength="x-strong"/> It is good if you gain some knowledge in that field<break strength="strong"/> or else work with some domain expertise people.<break strength="x-strong"/> This project is learning-based. <break strength="x-strong"/>You cannot deploy this project anywhere, because we have a small data set. <break strength="x-strong"/> So, happy learning!<break strength="x-strong"/> Keep learning with us.<break strength="x-strong"/> </speak>