Answer:
c) To identify outliers and anomalies
Step-by-step explanation:
Pre-cleaning steps are important to complete before data cleaning primarily to identify outliers and anomalies. Data pre-cleaning involves initial data assessment and data profiling to understand the quality and characteristics of the dataset. This step helps in identifying and documenting any data anomalies, such as missing values, incorrect data types, outliers, or inconsistencies.
Identifying outliers and anomalies is crucial because they can significantly impact the results of data analysis or machine learning models. Removing or handling outliers appropriately is often necessary to ensure that the data cleaning process is effective and that the final dataset is suitable for analysis or modeling.