This is a simple project that's meant to demonstrate the impact that hyperparameter tuning and the use of pipelines can have in machine learning.
Through the use of these techniques, we are able to fine-tune machine learning models so that they scale better with data, making overfitting and underfitting less likely to occur and improving the overall accuracy of the algorithm.
The code in this repository is heavily inspired by DataCamp's "Supervised Learning with scikit-learn" course, which is available here: https://www.datacamp.com/courses/supervised-learning-with-scikit-learn. It makes use of the scikit-learn, NumPy and Pandas libraries, as well as gapminder, an external library that consists of a Pandas dataframe.