Data exploration is not typically carried out during the data preparation stage of a model development process. Instead, it belongs to the data understanding stage. This stage involves studying the relationship between the dependent variable and independent variables, as well as the correlation between different features. It is a crucial step in understanding the structure and patterns within the data, which can inform the subsequent stages of model development.

Choice A is incorrect. Data acquisition is a crucial part of the data preparation stage in model development. It involves gathering relevant data from various sources, which will be used to build and test the model.

Choice B is incorrect. Data cleaning, also known as data cleansing or scrubbing, is another essential activity during this stage. It involves detecting and correcting (or removing) corrupt or inaccurate records from a dataset.

Choice D is incorrect. Sample selection refers to the process of choosing a subset of a population for investigation; it's an integral part of the data preparation stage as it helps in ensuring that the model can be generalized to apply to broader contexts beyond just the sample itself.

Things to Remember

• Data preparation is a crucial stage in model development where the raw data is transformed into a format suitable for analysis and modeling.
• Data preprocessing techniques such as normalization, standardization, and feature scaling are often applied during the data preparation stage to improve the performance of the model.
• Imputation of missing values, handling outliers, and encoding categorical variables are also common tasks carried out during data preparation.
• Feature engineering, which involves creating new features from existing data to