Analyzing Ola Data for Predicting Price Based Trip Distance Using Random Forest and Linear Regression Analysis

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