Applying the Seaborn library in Python is a way of making attractive statistical visualization. There are some functions that help you to see regressions and predict the trends. I used data data related to some famous brands in the car industry to analyze the relations between their features, including cylinder, horsepower, and weight. For instance, here, I plot weights and miles per gallon (mpg) data. Thanks to sns.regplot you can see the first and second-order regressions between these two parameters. It is evident as cars getting heavier, they consume more fuel. In other words, they travel less for a certain amount of fuel.
