***my name is Mohamed Sobhi and I'm study in faculty in navigation science and space technology**** 
Classification with OpenML bank Churn dataset using scikit-learn random forest and Grid Search Cross Validation *********************
Package: scikit-learn ********************** *****************Algorithm: Decision Tree Model ****************** *******************
Dataset: bank Churn Dataset******************* ***Model selection: using Grid Search Cross Validation (GSCV)****

Dataset

I'm using dataset from Kaggle.

Kaggle link.
https://www.kaggle.com/mathchi/churn-for-bank-customers

Problem Statement.
As we know, it is much more expensive to sign in a new client than keeping an existing one.
It is advantageous for banks to know what leads a client towards the decision to leave the company.
Churn prevention allows companies to develop loyalty programs and retention campaigns to keep as many customers as possible.
The solution is divided into the following sections:
Data understanding and exploration.
Data cleaning.
Data preparation.
Building model.
Evaluate model.