A practical tutorial for making a simple machine learning project from A-Z.

In the tutorial, we will study all the subjects (except Business Understanding) that are described in the above diagram(CRISP-DM).
From data processing throw popular machine learning algorithms to data visualization in the end.
We will mainly learn how to use Python's Sklearn package to run 8 supervised ML algorithms and 1 unsupervised algorithm.
Feel free to make pull requests for improvements or contact me with any questions.

preliminary knowledge required:

1. Numpy
2. Pandas
3. basic machine learning theory

Steps:

1. Install Python 3
   
2. pip install -r requirements.txt