Handling Missing Values – Imputation

from sklearn.impute import SimpleImputer
import numpy as np

X = np.array([[1, 2], [np.nan, 3], [7, np.nan]])

# Mean imputation
mean_imp = SimpleImputer(strategy='mean')
X_mean = mean_imp.fit_transform(X)

# Median imputation
median_imp = SimpleImputer(strategy='median')
X_median = median_imp.fit_transform(X)

print("Mean Imputed:\n", X_mean)
print("Median Imputed:\n", X_median)

Imputation replaces missing values (np.nan) with statistical estimates. SimpleImputer supports strategies such as mean, median, most_frequent, and constant. The choice depends on data distribution and model sensitivity.

Live Demo: Mean & Median Imputation

Feature 1Feature 2