Practical Data Science using Python - Linear Regression Data Preparation and Analysis 2

The video tutorial explores the distribution of various engine types and their impact on car prices, using linear regression models. It analyzes price variations based on company, fuel type, and car body, utilizing matplotlib for visualization. The tutorial also examines price distribution by engine location and uses scatter plots to find correlations between car features and price. Feature engineering is demonstrated by creating new fields like fuel economy and car range, followed by bivariate analysis to explore relationships between multiple variables.

What are the various types of engine types mentioned in the text?

How does the engine type of OHCS compare to other engine types in terms of sales?

What relationship is suggested between engine price and engine types?

Describe the process used to find the mean car prices for each engine type.

What were the average prices of cars with different engine types?

How does the analysis of car prices differ between diesel and gas cars?

What findings were made regarding the price distribution of cars with front and rear engines?

Explain the correlation found between car features (length, width, height, weight) and price.

What is the significance of the R square value in the context of car height and price?

How is fuel economy calculated based on existing fields in the dataset?