This is a regression notebook, demonstrating a simple analysis of transistor data with the motive to prove Moore's Law, along with a regression model for prediction.
Moore’s law, a prediction made by American engineer Gordon Moore in 1965 that the number of transistors per silicon chip doubles every year.
This trend can be seen when there is an exponential growth trend.
The following is the link to the dataset, made by LazyProgrammer (https://www.linkedin.com/company/lazyprogrammer.me/):
https://raw.githubusercontent.com/lazyprogrammer/machine_learning_examples/master/tf2.0/moore.csv