Improving Accuracy for Flight Fare Prediction using Random Forest Algorithm

Abstract

The aim of this study is to develop a flight fare prediction model using the Random Forest algorithm. The model is designed to provide accurate and reliable predictions of flight fares based on several input features such as flight route, departure and arrival times, airline carrier, and other relevant information. The Random Forest algorithm is chosen due to its ability to handle complex datasets and its robustness against overfitting. The training process of the model utilizes a vast collection of past flight prices, and multiple measures such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are utilized to evaluate its performance. The results show that the Random Forest model provides a high degree of accuracy in predicting flight fares and outperforms other machine learning algorithms such as linear regression and support vector regression. The proposed model can be used by airlines and travel agencies to make informed pricing decisions and assist customers in planning their travel budgets.