Flight Ticket Prediction Using Gradient Boosting Regressor Compared With Linear Regression

Aim: The purpose of this project is to predict airfare for ticket bookings using the Gradient Boosting. Regression device learning is the set of rules as opposed to a brand new linear regression. Materials and Methods: New Linear Regression Algorithm (with a sample size of ten) and Gradient Boosted Regression (with a sample size of ten). These algorithms are calculated on this picture using a total of 20 examples for the algorithm, and there are two firms that are used to calculate them. The size of the sample was determined to be 10, and it was compared with a group using a G Power value of 80%. Results: Values achieved in terms of accuracy are decided by Gradient Boosting regression (82.5%), as opposed to new Linear Regression (62.5%).8%. This is because Gradient Boosting regression is more accurate than new linear regression. In a test with one tail, the statistically significant difference between the new linear regression algorithm and the Gradient Boosting Regressor was found to be 0.00. This result was obtained With in Significance level of p 0.05. Conclusion: After going through all of the methods, it has been determined that the airfare forecast is more accurate than the brand new linear regression. This was revealed after going through all of the procedures.