A demo project on Data Ethics using Excel and Jupyter Notebooks to clean, merge, analyze and summarize the fictitious data of a fictitious bank Pig E. Bank.
Pig E. Bank is a fictional anti-money laundering project. As a new data analyst hire, my role is to provide analytical support to its anti-money-laundering compliance department.
Identify the main risk factors that are contributing to customer loss and create a decision tree based on the analysis.
The dataset for this project was supplied by CareerFoundry. Accessed from here.
Please note that this data set is fictitious for the learning purposes of the CareerFoundry Data Analytics course.
Language: Python
Libraries: Pandas, NumPy, Matplotlib and Seaborn
Software: Jupyter Notebooks, Excel, Canva
The distribution by age group for active customers (label: Age_Exited0) and exited customers (label: Age_Exited1).
