This research aims to create the most efficient and accurate cab fare prediction system using two machine learning algorithms, the Multiple linear Regression algorithm and the random forest algorithm, and compare parameters r-square, Mean Square Error (MSE), Root MSE, and RMSLE values to evaluate the efficiency of two machine learning algorithm. Considering Multiple linear Regression as group 1 and random forest algorithms as implemented, the 2 group process was to predict prices and get the best accuracy to compare algorithms. 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 group considered. The sample size calculation was done with clincle. The pretest analysis was kept at 80%. The sample size is estimated using G-power. Based on the statistical analysis significance value for calculating r-squared, MSE was 0.945 and 0.266(p>0.05), respectively. The Multiple linear algorithms give a slightly better accuracy rate with a mean r-squared percentage of 71.69%, and the Random forest algorithm has a mean r-square of 71.29%. Through this, prediction is made for online booking of cabs or taxis, and the Multiple linear algorithms give a slightly better r-squared value than the Random forest algorithm.

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.

The paper aims to create a most efficient and accurate cab fare prediction system using machine learning algorithms and comparing them. The machine learning algorithms are Random forest algorithm and Linear regression and comparing the r-square, mean square error (MSE), Root MSE and Root Mean Squared Logarithmic Error (RMSLE) values. We implement the Random forest and linear regression algorithms to predict the prices of the system and to get the best accuracy when comparing both the algorithms. The algorithms should be efficient to predict the prices of the trips before the starting of the trip. The sample size considered for this work is N=10 for each of the groups considered. Totally it was iterated 20 times for efficient and accurate analysis on prediction of price with G-power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The sample size calculation was done with clincle. Based on the statistical analysis the significance value for calculating the r-square was found to be 0.034. The Random forest algorithm gives a slightly better accuracy rate with a mean r-square percentage of 71.67% and the linear regression has mean r-square value of 70.57%. By this process, the prediction is done for the price prediction of the online cab rental system and the Random forest algorithm gives a better r-square value compared to the Linear regression algorithm.