Machine Learning Model Selection: Baseline Accuracy, Logistic Regression, and KNN

Choosing the right machine learning model can feel like guesswork without a structured evaluation framework. This text-based course guides you through establishing a solid baseline and systematically comparing Logistic Regression and K-Nearest Neighbors to make confident, data-driven decisions. You will learn to: Understand the core concept of baseline accuracy and why it serves as the essential benchmark for all predictive models; Explore the inner workings of Logistic Regression and K-Nearest Neighbors through clear written explanations; Apply modern performance evaluation metrics, including precision, recall, F1-score, and ROC-AUC; Practice proper data preparation techniques to avoid common pitfalls like data leakage during model training; Implement model selection workflows using clean Python and Scikit-Learn code examples. You will start with foundational definitions of classification and baseline metrics, progress to implementing both algorithms, and conclude with structured evaluation techniques to select the optimal model for your data. This course is designed for beginners in data science and analytics with no prior modeling experience required. Start reading today to master the essentials of machine learning model selection.