earticle

영어

Machine learning utilizes algorithms to run predictive models that learn from a large dataset in an iterative manner. Predictive models are used in many business applications to gain competitive advantages and understand customers better. This paper concentrates on analyzing New York taxi trips and fares and presenting the methodology we used to address the problem and results reached by building through Azure Machine learning studio. Our practical approach starts with an exploratory analysis of NYC taxi data via Microsoft Power BI. Then more extensive analysis was conducted through Apache Hive data warehouse. Hive was built on top of Hadoop enabling data synopsis, query, and analysis. We implemented Hive queries to create tables in Microsoft Azure blob storage and store the data in external tables. Finally, we conducted our experiment by creating, training and testing the module. The finding and insights pertain to the main variables of our experiment: pick up time, drop off time and tip amount that could be integrated into an application and enhance business by picking the location with the highest tip for example.