Abstract: This study presents an exploratory data analysis (EDA) and visualization of a vehicle dataset to detect patterns and insights that can inform vehicle performance and trends. We used Python and data analysis libraries like Pandas, Matplotlib, and Scikit-learn to analyse a sample of 1000 rows from the vehicle-1.csv dataset. Our analysis included cleaning, preprocessing, and visualizing the data to identify crucial characteristics and correlations within the dataset.Our findings show significant trends in vehicle attributes and their relationships, providing valuable insights for stakeholders in the automotive industry. Through detailed visualizations, we show how EDA can contribute to understand complex datasets and help data-driven decision-making. This study highlights the significance of thorough data analysis in vehicle data management and sets the stage for future research and applications in predictive analytics and machine learning.
Keywords: Data visualisation, data analysis, Exploratory data analysis, preprocessing.
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