Analyzing and Predicting Card Purchases with R

Understanding customer purchasing behavior is a critical asset for any modern business. This text-based course guides you through the process of transforming raw transaction data into actionable predictive insights using the R programming language. You will transition from a beginner to a confident data analyst capable of preparing customer datasets, engineering relevant features, and training machine learning models to predict future card purchases. What you'll learn: - Understand foundational R syntax, data structures, and the modern tidyverse ecosystem for data manipulation. - Clean and preprocess raw transaction records to handle missing values and outliers effectively. - Engineer predictive features from raw customer profiles and historical purchase behaviors. - Build and evaluate classification models using modern R modeling workflows to forecast future transactions. - Interpret model performance metrics such as accuracy, precision, and recall to optimize your predictions. The course begins with core R concepts and data handling basics before moving step-by-step through exploratory data analysis, feature engineering, and predictive modeling. You will practice through clear, written explanations and structured code snippets that build your skills progressively. This course is designed for beginners, aspiring data analysts, and business professionals looking to learn predictive analytics without any prior R experience. Start reading today to unlock the power of predictive customer analytics using R.