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

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