Laptop Rating Decision Tree Analysis
This project took the data from an unofficially made CSV file detailing the features and ratings of almost 1000 laptops. After putting this CSV through a python script that turned the data into a data tree, it was analyzed to determine how laptops were rated, and what this could say about the quality of the laptops themselves as well as the audience who purchased them.
The laptops in the data were analyzed by the program through many factors; in this specific data tree, the most relevant features were GPU, RAM, Screen Size, Price, and Processor. Then, these factors were analyzed to see if they played a part in giving the laptop a rating of >70 or <70 out of 100.
Ultimately, the laptop ratings showed that there were three audiences: those who had a small budget and were satisfied with what served their needs, those who had a large budget and would be happy with their purchase due to how much they had spent, and those who expected more out of a moderate budget but were disappointed in the end.