Day 21: Advanced Machine Learning Portfolio Projects

Welcome to Day 21 of the 28-Day Data Science Bootcamp! Today, we are wrapping up Week 3 by taking the machine learning algorithms we have learned throughout the week and applying them to messy, real-world datasets. We are shifting our focus to full ML workflows, model comparison, and the nuanced evaluation thinking that real production work requires . In this video, we will walk through the setup and execution of three advanced portfolio projects: Predicting Heart Disease with K-Nearest Neighbors (KNN): Using a dataset of patient clinical measurements, you will build a classification model to predict heart disease risk . We will explore why accuracy alone isn't the right metric in medical domains where false negatives have serious consequences, and instead evaluate the model using sensitivity and specificity . Predicting Insurance Costs with Linear Regression: Using demographic and health data, you will build a regression model to estimate medical costs for new customers . A key focus here will be model interpretation—you will learn how to translate regression coefficients into plain business language (such as understanding that smokers pay roughly 3x more, controlling for age) . Predicting Stock Market Returns with Random Forests: Using historical S&P 500 price data, you will engineer rolling features and build a random forest classifier to predict tomorrow's market direction . Most importantly, you will learn why naive train/test splits completely fail on temporal data and how to correctly implement backtesting and time-based splits to prevent "look-ahead bias" . By the end of today, you will have advanced, well-documented projects demonstrating your ability to handle complex machine learning pipelines and realistic model evaluation! Open up your Jupyter Notebook, and let's start building!