2 Replies to “Standardization vs. Normalization: What’s the Difference?”

1. Tsk tsk. You show an example of standardization but you use the term “normalize”. Following that is an example of normalization which you then call normalization. Aside from that mislabeling of the standardization example I found this to be a clear explanation. Thank you.

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   1. Hi Steve…Thank you for pointing out the terminology issue. It’s important to use the correct terms to avoid confusion. Here’s the corrected explanation with proper terminology for standardization and normalization, along with the appropriate example code.

      ### Standardization vs. Normalization

      – **Standardization**: This process rescales the data to have a mean of 0 and a standard deviation of 1. It is also known as Z-score normalization.
      – **Normalization**: This process rescales the data to a fixed range, typically [0, 1].

      ### Corrected Example for Standardization

      In this example, we will standardize the data:

      “`python
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Dropout

      # Load your dataset
      dt = pd.read_excel(“/content/DATA.xlsx”)
      dt.head()

      # Split the data into features (X) and target (Y)
      X = dt.iloc[:, 0:20]
      Y = dt.iloc[:, 21]

      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=100)

      # Standardize the data
      scaler = StandardScaler()
      X_train_scaled = scaler.fit_transform(X_train)
      X_test_scaled = scaler.transform(X_test)

      # Define the neural network model
      model = Sequential([
      Dense(32, activation=’relu’, input_shape=(X_train.shape[1],)),
      Dropout(0.5),
      Dense(16, activation=’relu’),
      Dropout(0.5),
      Dense(1, activation=’sigmoid’)
      ])

      # Compile the model
      model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])

      # Train the model
      history = model.fit(X_train_scaled, y_train, epochs=250, batch_size=10, validation_split=0.2)

      # Evaluate the model on the test data
      loss, accuracy = model.evaluate(X_test_scaled, y_test)
      print(‘Test Loss:’, loss)
      print(‘Test Accuracy:’, accuracy)

      # Make predictions on new data
      Xpred = pd.read_excel(‘New_data.xlsx’)
      Xpred_scaled = scaler.transform(Xpred) # Standardize the new data
      predicted_probabilities = model.predict(Xpred_scaled)
      predicted_classes = (predicted_probabilities > 0.5).astype(int)

      # Add predictions to the dataframe
      Xpred[‘yes/no’] = predicted_classes
      Xpred[‘prob’] = predicted_probabilities

      # Save the results to a new Excel file
      Xpred.to_excel(‘New_prob.xlsx’, index=False)

      # Display the first 50 rows of the new dataframe with predictions
      print(Xpred.head(50))
      “`

      ### Corrected Example for Normalization

      In this example, we will normalize the data to the range [0, 1]:

      “`python
      from sklearn.preprocessing import MinMaxScaler

      # Normalize the data
      scaler = MinMaxScaler()
      X_train_normalized = scaler.fit_transform(X_train)
      X_test_normalized = scaler.transform(X_test)

      # Define the neural network model
      model = Sequential([
      Dense(32, activation=’relu’, input_shape=(X_train.shape[1],)),
      Dropout(0.5),
      Dense(16, activation=’relu’),
      Dropout(0.5),
      Dense(1, activation=’sigmoid’)
      ])

      # Compile the model
      model.compile(optimizer=’adam’, loss=’binary_crossentropy’, metrics=[‘accuracy’])

      # Train the model
      history = model.fit(X_train_normalized, y_train, epochs=250, batch_size=10, validation_split=0.2)

      # Evaluate the model on the test data
      loss, accuracy = model.evaluate(X_test_normalized, y_test)
      print(‘Test Loss:’, loss)
      print(‘Test Accuracy:’, accuracy)

      # Make predictions on new data
      Xpred = pd.read_excel(‘New_data.xlsx’)
      Xpred_normalized = scaler.transform(Xpred) # Normalize the new data
      predicted_probabilities = model.predict(Xpred_normalized)
      predicted_classes = (predicted_probabilities > 0.5).astype(int)

      # Add predictions to the dataframe
      Xpred[‘yes/no’] = predicted_classes
      Xpred[‘prob’] = predicted_probabilities

      # Save the results to a new Excel file
      Xpred.to_excel(‘New_prob.xlsx’, index=False)

      # Display the first 50 rows of the new dataframe with predictions
      print(Xpred.head(50))
      “`

      ### Summary of Corrections

      – **Standardization**: Adjusts data to have a mean of 0 and a standard deviation of 1.
      – **Normalization**: Adjusts data to a specific range, typically [0, 1].

      Using the correct terminology ensures clarity and better understanding of the preprocessing steps. Thank you for highlighting the need for these corrections.

      Reply