Data Analysis

In this project, I explored various datasets available within Seaborn, ultimately selecting the ‘taxis’ dataset to conduct a detailed analysis. This dataset, comprising over 6400 entries, detailed aspects of taxi rides such as pickup and dropoff times, passenger counts, distances, fares, and more. After cleaning and preparing the data by dropping rows with missing values and handling duplicates, I performed exploratory data analysis (EDA) to uncover insights into ride distributions, correlations between key variables, and patterns in ride frequency across different times and days. Utilizing Python libraries such as Pandas for data manipulation, Matplotlib, and Seaborn for visualization, this analysis aimed to provide a comprehensive overview of taxi ride dynamics captured in the dataset. GitHub project