Discuss the various techniques and algorithms available in Azure Machine Learning for feature engineering and explain how they contribute to improving model performance.

In Azure Machine Learning (AML), feature engineering refers to the process of transforming raw data into meaningful features that can enhance the performance of machine learning models. AML offers various techniques and algorithms for feature engineering, each with its specific benefits. Let's explore some of these techniques and how they contribute to improving model performance: 1. Data Cleaning and Preprocessing: * Missing Data Handling: AML provides methods for imputing missing values in datasets, such as mean imputation or regression imputation. By addressing missing data, models can avoid biases and improve performance. * Data Scaling and Normalization: AML offers algorithms like MinMaxScaler and StandardScaler to scale numerical features, ensuring that all features contribute equally to model training and preventing dominant features from overshadowing others. 2. Feature Extraction and Transformation: * Text Analytics: AML provides text processing capabilities like tokenization, stop-word removal, and n-gram generation for converting text....