Predicting Pizza Sales with Supervised Machine Learning

Can you predict tomorrow's sales based on today's reservations? Understanding how to frame business questions as machine learning problems is the first step to building successful predictive models. In this written course, you will transition from a beginner to confidently framing, analyzing, and solving supervised learning problems. You will learn how to take raw reservation data and build a predictive pipeline that estimates pizza sales, using clean Python code and efficient array operations. What you'll learn: Learn the core concepts of supervised learning and regression analysis; Structure raw reservation data into clean features and targets using NumPy; Apply modern Python type hints to keep your machine learning code clean; Implement a linear prediction model from scratch; Evaluate the accuracy of your forecasting model using standard performance metrics; Practice translating real-world business problems into structured mathematical formats. The course begins with foundational machine learning terminology and basic definitions before moving to practical implementation. You will then progress through reading step-by-step explanations of data preparation and model evaluation with clear Python code snippets. This course is designed for beginners who want to understand the practical application of machine learning to business forecasting, with no advanced prerequisites. Start reading today to master the fundamentals of supervised learning and build your first predictive sales model.