When CCA isn’t a viable possibility, knowledge imputation strategies come to the rescue.
1. Univariate Imputation
This methodology includes filling lacking values with statistical measures just like the imply, median, or mode of the column.
from sklearn.impute import SimpleImputer
# Impute lacking values with the imply
imputer = SimpleImputer(technique='imply')
knowledge['column_name'] = imputer.fit_transform(knowledge[['column_name']])
2. Multivariate Imputation
Extra subtle strategies estimate lacking values primarily based on different variables. Examples embrace:
- k-Nearest Neighbors (KNN) Imputation: Estimates lacking values by averaging the closest neighbors.
- A number of Imputation: Gives a number of estimates and averages the outcomes for accuracy.
from sklearn.impute import KNNImputer
# Impute utilizing KNN
knn_imputer = KNNImputer(n_neighbors=5)
knowledge = knn_imputer.fit_transform(knowledge)
3. Selecting the Proper Methodology
When choosing a technique, take into account the information distribution and context:
- If the information is generally distributed, imply or median imputation works nicely.
- If relationships between variables are important, KNN or a number of imputation is preferable.
