Instruction: Describe the process of preparing a dataset for machine learning models using Pandas, including feature engineering and normalization.
Context: This question assesses the candidate's proficiency in using Pandas for machine learning data preparation, a critical step in the model building process.
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Firstly, data cleaning is fundamental. This step involves handling missing values, which can be tackled in various ways such as imputation, where missing values are replaced with statistical measures like the mean or median for numerical features, and mode for categorical features. It's also crucial to identify and remove duplicate records to ensure the uniqueness of the dataset. For example, in Pandas, df.drop_duplicates() is a straightforward method to remove duplicate rows.
Feature engineering is the next pivotal step. It’s about creating new features from existing ones to better highlight underlying patterns in the data for the model. A common technique I employ is feature extraction, particularly with temporal data. For instance, extracting day, month, and year from a datetime column can...