In data analysis, `NaN` (an acronym for 'Not a Number') is a special floating-point value used computationally to represent missing or undefined numerical data. For example, when `pandas` loads a CSV file containing empty fields or explicitly written `NA` entries, it automatically parses and represents them as `NaN`, serving as the programmatic indicator for missing values.

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In data science, missing values (often represented programmatically as `NaN`) occur when entries in a dataset are empty or undefined, resulting from missing data collection or parsing errors. These incomplete records are a persistent challenge in data preprocessing and must be deliberately handled before training a model, typically through either imputation (estimating replacement values) or deletion (discarding the incomplete rows or columns).

Missing Values

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NaN (Not a Number)

When dealing with missing values in a dataset, deletion is a straightforward handling strategy that involves entirely removing the rows or columns that contain `NaN` or empty entries. While this method successfully eliminates missing data, it carries the risk of discarding otherwise valuable information.

Data Deletion for Missing Values

Imputation is a critical data science strategy used to handle missing values by replacing them with estimated substitutions rather than discarding the data entirely. The specific estimation heuristic depends on the context; for instance, missing numerical data might be replaced by the column's mean, while missing categorical entries might be treated as their own distinct category.

Data Imputation

Before data can be fed into any machine learning model, its overall quality must be evaluated and improved. Real-world datasets are often plagued by flaws such as extreme outliers, faulty sensor measurements, and human recording errors. Addressing these discrepancies is an essential part of the data preparation process to ensure the model learns from reliable information.

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