This project focuses on providing analytical support to Pig E Bank's anti-money laundering compliance department. Through data analytics, the goal is to assess customer risk, transaction risk, and reporting metrics while building and optimizing compliance models. The project addresses various challenges in data analytics and ethics, emphasizing the importance of addressing data-related ethical dilemmas with a strong ethical foundation.

• Describe the characteristics, applications, and limitations of structured and unstructured data
• Identify types of bias in the workplace and suggest methods of control
• Research ethical dilemmas, security/privacy laws, and communicate concerns
• Perform data mining, cleaning, and descriptive statistics
• Build decision trees and regression models for analysis
• Analyze and identify appropriate predictive models
• Describe time series characteristics and implement predictions

Pig E. Bank's Data Set - This dataset was created by CareerFoundry for use in this analysis. The dataset includes information about customer IDs, last names, credit scores, country, gender, age, tenure, balance, number of products, credit cards, activity, estimated salary, and exit status.

• The primary tool used for data analysis and visualization is Microsoft Excel
• The decision tree was created using Canva

The decision tree was created using Canva and can be viewed in the Canva Whiteboard

The Pig E Bank Data Analytics project aims to provide analytical support to the bank's compliance department by assessing risks, building models, and addressing ethical concerns. Through a series of exercises, the project covers topics such as big data, data mining, predictive analysis, and time series forecasting. By leveraging analytical tools and ethical considerations, the project seeks to optimize the bank's compliance program and contribute to ethical data practices.