How it works:
- Imputes missing values by modeling each feature with missing values as a function of the other features.
- Uses regression (e.g., linear, decision trees) to predict missing values iteratively.
- Repeats the process multiple times until convergence or a specified number of iterations.
Strengths:
- Considers relationships between features, leading to more accurate imputations when variables are interdependent.
- Flexible: allows for various estimators (e.g., linear regression, Bayesian Ridge, etc.).
- Handles both numerical and categorical data (with proper preprocessing).
Weaknesses:
- Computationally intensive, especially for large datasets or high dimensions.
- Sensitive to model choice and may overfit if not tuned well.
- Requires careful handling of categorical data (e.g., encoding).
Use case:
- When the dataset has complex relationships between features and sufficient computational resources.
- Particularly useful for datasets with strong correlations between features.

How it works:
- Replaces missing values with the mean (or median) of the k nearest neighbors, identified based on similarity in feature space (e.g., Euclidean distance).
Strengths:
- Simple and intuitive to understand.
- Captures local patterns in the data.
- Works well for smaller datasets and when missing data is sparse.
Weaknesses:
- Ignores feature-to-feature relationships beyond the local neighborhood.
- Computationally expensive for large datasets (distance calculations grow with size).
- Sensitive to scaling of features and the choice of k.
Use case:
- Suitable for smaller datasets with low-dimensional numerical features.
- Works well when missing values are relatively sparse.

Feature	Iterative Imputer	KNN Imputer
Methodology	Regression-based (iterative)	Distance-based (nearest neighbors)
Feature Correlation	Considers relationships	Does not explicitly consider relationships
Data Type	Numerical and categorical (with preprocessing)	Primarily numerical data
Complexity	Computationally expensive	Computationally simple (small datasets)
Scalability	Less scalable for large datasets	Better suited for smaller datasets
Parameter Sensitivity	Sensitive to model choice	Sensitive to choice of `k` and scaling

Use Iterative Imputer for datasets where features are strongly correlated or interdependent, and computational resources are not a constraint.
Use KNN Imputer for simpler tasks with fewer features or when interpretability and computational efficiency are more important.

Iterative Imputer V/S KNN Imputer