Analyzing Ola Data for Precise Price Prediction Using XGBoost Technique Comparing with LASSO Regression

Abstract

XGBoost algorithm and Lasso regression and compare r-square, Mean Square Error (MSE), Root MSE, and RMSLE values. The algorithm should be efficient enough to produce the exact fare amount of the trip before the trip starts. The sample size for implementing this work was N=10 for each of the groups considered. It was iterated 20 times for efficient and accurate prediction of cab price prediction with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The sample size calculation was done with clincle. The pretest analysis was kept at 80%. The sample size calculation was done using clincalc. The statistical analysis shows that the significance value for calculating r-squared and MSE was 0.63 and 0.581(p>0.05), respectively. The XGBoost algorithm gives a slightly better accuracy rate with a mean r-squared percentage of 72.62%, and the Lasso regression algorithm has a mean r-square of 70.47%. Through this, the prediction is made for the online booking of cabs or taxis, and the Xgboost algorithm gives a slightly better r-squared value and MSE values than the Lasso regression algorithm.