<ul data-eligibleForWebStory="true"><li>New data scientists often face challenges with real-world messy data compared to clean, toy datasets used in training.</li><li>Understanding the true context of missing values is crucial for accurate preprocessing.</li><li>Imputing nulls with zeros in specific cases is more meaningful than using means or medians.</li><li>Choosing between mean and median imputation depends on data distribution, favoring median for skewed data.</li><li>Category-wise null imputation can provide more accurate results than overall imputation.</li><li>Drop_duplicates function may overlook subtle differences and requires thoughtful parameter selection.</li><li>Scaling data using StandardScaler or MinMaxScaler is essential for models sensitive to feature magnitudes.</li><li>Feature engineering helps control the explosion of columns in categorical data, improving model performance and explainability.</li><li>Utilizing feature decomposition algorithms like PCA can manage excessive one-hot encoded columns effectively.</li><li>Careful consideration is needed when removing outliers to avoid discarding valuable insights within the data.</li></ul>

The Preprocessing Survival Guide for New Data Scientists

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