Disease prediction model
The model predicts the user based on the symptoms he/she is suffering from. The model uses random forest model and has an accuracy of nearby 75%. We aim to provide the informatin of the disease at the earliest so that measures can be taken and if required medical attention can be paid.
The first step that we took was finding the most appropriate dataset. Afterwards, we cleaned the dataset and trained the random forest model using various python libraries like Numpy, Pandas etc. Next, we designed the layout and built a prototype and hence we worked on the frontend part. Afterwards, integrating backend was a tough part as we used Flask for the same.