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with a focus on regression, feature engineering, and tree-based models.\n\n## Key Topics Covered\n\n### I Exploring Data with Regression Models\n- Supervised learning: classification vs. regression  \n- Train-test split vs. cross-validation techniques  \n- Sequential feature selection for housing price prediction  \n- Managing model complexity with numeric features  \n- One-hot encoding for categorical data  \n- Interpreting coefficients in linear regression models  \n- Polynomial regression for capturing non-linear relationships  \n\n### II Skills for Better Modeling\n- Building and using pipelines for data transformation  \n- Detecting and overcoming perfect multicollinearity using Lasso regression  \n- Feature scaling and optimizing penalized regression models  \n- Comparative guide to imputation techniques: simple, iterative, and kNN imputation  \n\n### III Tree-Based Models in Data Science\n- Overview of tree-based regression models with visualization  \n- Practical use of ordinal 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