  The Internet has become an essential tool for people in the modern world.
Humans, like all living organisms, have essential requirements for survival.
These include access to atmospheric oxygen, potable water, protective shelter,
and sustenance. The constant flux of the world is making our existence less
complicated. A significant portion of the population utilizes online food
ordering services to have meals delivered to their residences. Although there
are numerous methods for ordering food, customers sometimes experience
disappointment with the food they receive. Our endeavor was to establish a
model that could determine if food is of good or poor quality. We compiled an
extensive dataset of over 1484 online reviews from prominent food ordering
platforms, including Food Panda and HungryNaki. Leveraging the collected data,
a rigorous assessment of various deep learning and machine learning techniques
was performed to determine the most accurate approach for predicting food
quality. Out of all the algorithms evaluated, logistic regression emerged as
the most accurate, achieving an impressive 90.91% accuracy. The review offers
valuable insights that will guide the user in deciding whether or not to order
the food.
