A simple and well designed structure is essential for any machine learning project, project template that combines simplicity, best practice for CODE structure and good CODE design. The main idea is that there's much same stuff you do every time when you start our machine learning project, so wrapping all this shared stuff will help you to change just the core idea every time you start our machine learning project.

In order to decrease repeated code shunks, increase the time that can read the code in, flexibility an reusability we used a functional programming structure that focused on split all problems in our project in functions and use that functions many times in many places in the code without repeating the code.

• numpy (The fundamental package for scientific computing with Python)
• pandas (pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.)
• sklearn (TMachine Learning and Data Analysis Library in Python)
• matplotlib (Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python)
• seaborn (Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.)

• Upload the ipynb code file into "Google Colab" or Anaconda "Jupyter Notebook"
• Press "Run All" in the control panel or "Restart Kernel and Run All" to run all code
• In case of run each code cell alone, press the run button that appear at each code cell tents

Any kind of enhancement or contribution is welcomed.