ANALYSIS OF DATA ON TRIPS TO THE METROPOLIS AND FORECASTING THE COST OF THE TRIP
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Annotation: This article examines a set of data on taxi rides in the metropolis in order to determine the factors that affect the price of the trip. Machine learning models are used: linear regression, polynomial regression, decision trees, ensemble learning, and the random forest method. During the comparison, it turned out that the optimal model is begging . A method for determining the predictive estimate of the cost of a trip is obtained.
Keywords: taxi service cost forecasting, machine learning model, linear regression, polynomial regression, decision tree model, random forest method, ensemble learning, begging, boosting
For citation: Dzerzhinskij R.I., Lapshin I.A., Anosov T.E. Analysis of data on trips to the metropolis and forecasting the cost of the trip // Electronic Scientific Journal IT-Standard. – 2021. – No. 4. – pp. .