Dive into Day 9 of Kaggle's 30 Days of ML Challenge, focusing on building your first machine learning model and understanding model validation. Learn to create a ML model using scikit-learn, work with large datasets, and discover patterns in big data. Explore techniques for measuring model quality through validation, including mean absolute error calculation. Follow along with tutorials and exercises from Kaggle's Intro to ML course, covering topics such as specifying prediction targets, creating predictive features, and evaluating model performance. Gain practical experience in exploratory data analysis, handling missing values, and choosing relevant features for your model. Perfect for aspiring data scientists and ML enthusiasts looking to develop a daily coding habit and enhance their Python-based machine learning skills.