Machine Learning Project: Ames Housing Dataset Analysis
Employed comprehensive data preprocessing techniques, encompassing handling missing values, encoding categorical variables, and feature engineering. Implemented advanced feature selection and outlier detection strategies to refine model accuracy. Leveraged cross-validation for robust hyperparameter tuning and finalized a model for precise house sale price predictions on test data.