Flight fare prediction model predicts the fare or the price of the flight given the travel date, arrival time, source, destination, the stopage and the airline preferred by the customer.
The objective of this project is to predict flight prices given the various parameters. Data used in this article is publicly available at Kaggle. This will be a regression problem since the target or dependent variable is the price (continuous numeric value).
Regression:
Regression is a supervised machine learning technique which helps in finding the correlation between variables and enables us to predict the continous output variable based on one or more input variables. It's mainly used for prediction, forecasting and time-series modelling and used to determine relationship between variables.
Types of regression:
1.Linear regression
2.Logistic regression
3.Polynomial Regression
4.Ridge Regression
5.Lasso Regression
6.Bayesian Linear Regression