Parameters used= ["cabin_flown","type_traveller", "overall_rating", "seat_comfort_rating",
"cabin_staff_rating", "food_beverages_rating", "inflight_entertainment_rating", "value_money_rating"]
Algorithm used : KNeighborsClassifier from sklearn.neighbors with value of k=14
KNN makes predictions using the training data-set directly.
Predictions are made for a new instance (x) by searching through the entire training set for the K most similar instances (the neighbors) and summarizing the output variable for those K instances.
When KNN is used for classification, the output can be calculated as the class with the highest frequency from the K-most similar instances. Each instance in essence votes for their class and the class with the most votes is taken as the prediction.