Alignment of Machine Learning Technology with CRM Strategy

1-Abstract This project report outlines the application of machine learning (ML) models to analyze a synthetic customer payment dataset within a customer relationship management (CRM) framework. The primary objective is to demonstrate how data-analytic thinking and robust MLOps (Machine Learning Operations) methodologies can enhance CRM strategy, specifically in identifying customer churn indicators from transactional data. The dataset, comprising ten customer records and six attributes, mirrors realistic data quality challenges, including missing values, outliers, inconsistent formatting, and logical inconsistencies. The methodology leverages concepts from Data Science for Business to structure the analytical approach, applies statistical learning techniques from An Introduction to Statistical Learning for model development (such as classification), and uses practical implementation strategies detailed in Python Machine Learning for data preprocessing, model training, deployment,...