UBER TRIPS DATA ANALYTICS USING PYTHON BIG DATA TOOLS AND MAP VISUALIZATION

This study focused on analyzing Uber trip data using Python-based data analytics techniques and map visualization methods. The dataset containing trip information such as pickup time and location coordinates was processed and analyzed to understand ride demand patterns. Python libraries were used to clean the dataset, extract useful features, and perform exploratory data analysis to gain meaningful insights. During the preprocessing stage, the dataset was organized by removing missing or incorrect values and converting the time data into a proper format. Features such as hour, day, and month were extracted to analyze trip trends across different time intervals. These steps helped improve the accuracy and reliability of the analysis results. The analytical results showed that Uber ride demand is higher during evening peak hours and during weekends. This pattern is mainly due to work commuting and increased social or recreational activities during these times. Identifying these patterns helps in understanding passenger behavior and demand fluctuations in ride-sharing services. Map visualization techniques were also used to display pickup locations and identify areas with high ride demand. Big data frameworks such as Apache Spark and Apache Hadoop can further enhance the ability to process and analyze large volumes of transportation data efficiently.

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