The analysis imports the historical automobile sales dataset into a pandas DataFrame, including variables like date, recession indicators, sales, GDP, unemployment rates, consumer confidence, seasonality weights, vehicle prices, advertising expenditures, vehicle types, and market competition.
This step helps in identifying patterns and relationships within the data, such as sales trends over time and the impact of economic indicators on automobile sales.
Various visualization tools are used to illustrate data insights, including Matplotlib and Seaborn for line charts, scatter plots, and bar graphs, and Folium for interactive maps displaying geographical sales distributions.