EDA and Classification with Logistic Regression and KNN

Healthcare data holds critical insights, but extracting meaningful patterns requires structured analysis. Understanding how to clean, explore, and model medical datasets is a fundamental skill for aspiring data scientists. In this written guide, you will transition from raw data to predictive insights. You will learn how to perform thorough exploratory data analysis (EDA), understand feature distributions, and apply classification algorithms like Logistic Regression and K-Nearest Neighbors (KNN) to a real-world breast cancer dataset. What you'll learn: - Understand foundational data science concepts and the classification pipeline - Analyze feature distributions and identify patterns using exploratory data analysis - Prepare and preprocess medical datasets for machine learning models - Implement Logistic Regression and K-Nearest Neighbors (KNN) algorithms - Evaluate model performance using key metrics like precision, recall, and F1-score - Compare and select the best classification model for healthcare predictions The course begins with essential terminology and data exploration techniques before guiding you through feature engineering and model implementation using clean, structured Python code snippets. This course is designed for beginners who want to build a strong foundation in classification tasks, with no advanced prerequisites required. Start exploring medical data and building your first classification models today.