The problem NeedyRoom solves

-It saves lives!!....Helps you get instant hospital room based on severity of disease. Live status of all the allocated and free rooms and the patients allocated room and waiting to be allocated in different hospitals.

-predicts the diseases of patients from its symptoms in case the doctor is not available at the point for appointment.

-Provides the nearest hospital's available room to the patient in case all the rooms are filled

-Requests the less severe disease patient to swaps rooms with more severe disease patient in case the room is not available in nearby hospitals.

Challenges we ran into

Challenges we ran into-
-Integrating the python machine learning models of keras with nodejs backend.
-Accuracy of ML disease prediction model was not increasing.
How we solved it-
-By creating 2 backends...one of flask and other of nodejs and sending the post request to node and then sending to flask to predict disease and again send to node to output your result.
-We used different ML classifiers like decision tree ,random forest,multinomial naive bayes and finally concluded that decision tree gave the maximum accuracy for any example.