Apply Machine Learning for Predictive Business Analytics

By the end of this course, learners will be able to analyze customer data, prepare datasets for machine learning, build churn prediction models using R, and evaluate model performance using industry-standard techniques. Learners will also gain the ability to interpret model outputs and apply insights to real-world business decision-making. This course is designed to provide a practical, end-to-end understanding of churn prediction using machine learning in R Studio. Starting with foundational concepts such as data types and exploratory data analysis, the course progressively guides learners through dataset understanding, data preprocessing, and model selection. Hands-on lessons focus on implementing logistic regression, handling missing values, transforming data, and evaluating models using accuracy metrics, ROC curves, and decision trees. Learners benefit from a structured, project-based approach that mirrors real-world data science workflows. Unlike theory-heavy courses, this program emphasizes applied learning with step-by-step R code demonstrations and business-focused interpretation of results. The course is ideal for students, analysts, and professionals seeking to develop practical machine learning skills while understanding how churn prediction delivers measurable value across industries.

By the end of this course, learners will be able to analyze banking and credit systems, apply machine learning techniques for fraud detection, evaluate financial risk using efficiency models, and interpret profitability reports to support data-driven decisions. Learners will gain the ability to assess credit risk, detect fraudulent payment patterns, and evaluate operational efficiency using industry-relevant analytical frameworks. This course provides a practical, end-to-end exploration of financial fraud analytics across banking, credit, and payment systems. Learners progress from foundational banking concepts and credit risk classification to advanced fraud detection, efficiency modeling, and profit-and-loss analysis. The course integrates logistic regression, risk analytics, and Data Envelopment Analysis (DEA) to bridge predictive modeling with operational and financial performance evaluation. What makes this course unique is its combined focus on machine learning, financial efficiency, and real-world fraud decision-making. Instead of treating fraud detection as a standalone modeling task, the course emphasizes interpretability, regulatory relevance, and business impact. Through applied examples and structured analytics workflows, learners develop job-ready skills aligned with roles in financial risk analytics, fraud prevention, and data-driven decision support.

Learners will be able to analyze supply chain demand trends, interpret heatmap visualizations, apply data preparation techniques, and evaluate clustering methods to uncover meaningful demand patterns. By the end of this course, learners will confidently explore demand data, compare visualization approaches, and derive actionable insights to support data-driven supply chain decisions. This course is designed to help learners build practical machine learning–oriented analytical skills specifically for supply chain demand analysis. Learners will progress from understanding foundational supply chain concepts to applying advanced visualization and clustering techniques using heatmaps. Through step-by-step demonstrations, learners will learn how to prepare datasets, validate function inputs, discretize continuous data, and interpret multiple visual outputs effectively. What makes this course unique is its strong focus on visual analytics as a decision-support tool in supply chain management. Rather than emphasizing theory alone, the course demonstrates how real-world demand trends can be explored and compared using multiple analytical perspectives. This hands-on, visualization-driven approach enables learners to bridge the gap between raw data and strategic insight, making the course especially valuable for aspiring data analysts, supply chain professionals, and machine learning practitioners seeking applied, job-relevant skills.