Split Data for Machine Learning

The video tutorial covers data splitting techniques in machine learning, including train-test split and cross-validation using K-Fold. It demonstrates how to import data using pandas, manually split data, and create synthetic datasets. The tutorial also explains the importance of maintaining separate datasets for training, validation, and testing to ensure model accuracy and avoid overfitting. Additionally, it outlines the data engineering workflow, emphasizing data collection, feature engineering, and hyperparameter optimization.

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