What is the difference between StandardScaler and MinMaxScaler in Scikit-learn and when would you use each

Question

With the help of code can i know What is the difference between StandardScaler and MinMaxScaler in Scikit-learn, and when would you use each?

Answer 1

StandardScaler standardizes features by removing the mean and scaling to unit variance, while MinMaxScaler scales features to a fixed range (0 to 1), making StandardScaler ideal for normally distributed data and MinMaxScaler better for bounded feature ranges or non-Gaussian data.

Here is the code snippet given below:

In the above code we are using the following techniques:

• StandardScaler (Z-score normalization):

  • Transforms data to zero mean and unit variance.
  • Use when data is normally distributed or required for algorithms sensitive to scale (e.g., PCA, SVM, Logistic Regression).

• MinMaxScaler (Min-Max Normalization):

  • Rescales features to a fixed range [0, 1] (or custom).
  • Use when preserving relative relationships is important, such as in Neural Networks and Gradient-based Models (e.g., KNN, Deep Learning, LSTMs).

Hence, use StandardScaler for normal-distributed data and models requiring standardized inputs, while MinMaxScaler is ideal for bounded range applications and preserving distribution.