The best thing, as per me, is there is documentation available of scikit-learn. So, if I sometimes find it difficult to apply some algorithms, I can check the documentation, which helps me. I like this thing. Scikit-learn also provides many inbuilt datasets so that I can use them for practice purposes. Scikit-learn comes with many machine learning algorithm, which makes easy to me for implementing algorithms. I like that it comes with many data manipulation functions to clean my data according to my requirements.

The best aspect about this framework is the availability of well integrated algorithms within the Python development environment. It is quite easy to install within most Python IDEs and relatively easy to use as well. A lot of tutorials are accessible online which supplements understanding this library allowing to become proficient in machine learning. It was clearly built with a software engineering mindset and nevertheless, it is very flexible for research ventures. Being built on top of multiple math-based and data libraries, scikit-learn allows seamless integration between them all. Being able to use numpy arrays and pandas dataframes within the scikit-learn environment removes the need for additional data transformation. That being said, one should definitely get familiar with this easy to use library if they plan on becoming a data-driven professional. You could build a simple machine learning model with just 10 lines of code! With tons of features like model validation, data splitting for training/testing and various others, scikit-learn's open source approach facilitates a manageable learning curve.

Various machine learning models and easy to adjust parameters. Also easy to apply data transformation prior to fit the model