How do you handle missing or incomplete data in a dataset?

Handling missing or incomplete data in a dataset is a critical step in the data preprocessing phase of any data analytics or machine learning project. Here’s a systematic approach to managing such data effectively:

1. Identify Missing Data
Inspection: Use tools (e.g., Pandas in Python or Power BI) to inspect the dataset for missing values.Example: isnull() in Python or null-checking functions in SQL.
Patterns of Missingness: MCAR (Missing Completely at Random): Missing values are independent of other variables.
MAR (Missing at Random): Missing values depend on observed data.
MNAR (Missing Not at Random): Missing values depend on unobserved data.

2. Analyze the Impact
Determine how much data is missing: Simpler strategies like deletion may work if the proportion is small.
If substantial, imputing or advanced techniques are necessary.
Assess which variables are affected and their importance to the analysis.

3. Strategies to Handle Missing Data
a. Removal of Missing Data
Listwise Deletion: Remove rows with missing values. Suitable when the dataset is large, and missing data is minimal.
Column Removal: Remove columns with excessive missing values if they are not crucial.