Result: Inferential statistics and visualisation analysis of automobile industry.

The manuscript aims to present the analysis and visualization of automobiles using various visualization tools available in python. Exploratory data analysis is done to gain insights about the various aspects of the data. It is applied to filter the data available from redundancies and also follows a sequential set of steps to explore the data in the best possible manner. It enables the user to understand the data by gaining knowledge about relationship among variables of the data. Now as the automobile data is gigantic, it first needs to be analysed to produce a good result. For the same, authors have used python as it has rich sets of libraries like pandas, matplotlib, seaborn, etc to efficiently perform various functions pertaining to data visualization and analytics. Current manuscript illustrates various kinds of charts that enables user to draw useful conclusions. Some of the conclusions drawn through the analytics are like Toyota producing highest number of cars followed by Rissian. Data analysis also reveal that customers prefer gas fuel over diesel and BMW cars enjoy high sale owing to high peak-rmp in comparison to Audi. [ABSTRACT FROM AUTHOR]

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