what is Feature scaling done before applying K-Mean algorithm?

Feature scaling is performed before applying the K-Means algorithm to ensure that all features contribute equally to the distance computations. When features are on different scales, those with larger magnitudes may dominate the distance calculations, leading to biased results. By scaling the features, each feature contributes proportionally to the distance calculation, ensuring that no single feature dominates. This helps in achieving better cluster formation based on the actual distribution of data points.
Therefore, the correct answer is Option A: in distance calculation it will give the same weights for all features.