Ola Data Analysis for Dynamic Price Prediction Using Multiple Linear Regression and Random Forest Regression

This research aims to create the most efficient and accurate cab fare prediction system using two machine learning algorithms, Multiple linear Regression algorithm random forest algorithm, compare parameters r-square, Mean Square Error (MSE), Root MSE, RMSLE values evaluate efficiency of algorithm. Considering as group 1 algorithms implemented, 2 process was predict prices get best accuracy algorithms. The should be enough produce exact amount trip before starts. sample size for implementing this work N=10 each considered. calculation done with clincle. pretest analysis kept at 80%. is estimated G-power. Based on statistical significance value calculating r-squared, MSE 0.945 0.266(p>0.05), respectively. give a slightly better rate mean r-squared percentage 71.69%, Random has r-square 71.29%. Through this, made online booking cabs or taxis, than