Drug recommendation system based on analysis of drug reviews using machine learning

Since COVID has appeared, unavailability of genuine clinical assets is at its pinnacle, similar to the lack of subject matter experts and medical services laborers, absence of legitimate hardware and prescriptions and so on. The whole clinical crew is in trouble, which brings about various person’s downfall. Because of inaccessibility, people began taking prescription freely without proper meeting, aggravating the wellbeing than expected. This paper is regarding introduction of a medication recommendation framework, which can definitely lessen experts’ stack. In this exploration, we construct a medication suggestion framework that utilizes patient audits to foresee the opinion utilizing different machine learning classifier such as naïve Bayes classifier which can assist with suggesting the top medication for a given illness by various grouping calculations. The anticipated opinions were assessed by accuracy, review, f1score and accuracy. The outcomes show that with N-gram, Light GBM and naïve bayes we can acquire a 90% accuracy.