Data Normalization Machine Learning (original) (raw)

Last Updated : 12 Sep, 2025

Data normalization is a preprocessing method that resizes the range of feature values to a specific scale, usually between 0 and 1. It is a feature scaling technique used to transform data into a standard range. Normalization ensures that features with different scales or units contribute equally to the model and improves the performance of many machine learning algorithms.

Key Features of Normalization:

• Maps the minimum and maximum of a feature to a defined range
• Preserves the relative relationships of the original data
• Useful for algorithms that rely on distance metrics such as k-Nearest Neighbours and clustering

Why do we need Normalization?

Machine learning models often assume that all features contribute equally. Features with different scales can dominate the model’s behavior if not scaled properly. Using normalization, we can:

• **Ensure Equal Contribution of Features: Prevents features with larger scales from dominating models that are sensitive to magnitude such as K-Nearest Neighbours or neural networks.
• **Improve Model Performance: Algorithms that rely on distances or similarities (KNN, K-Means clustering) perform better when features are normalized.
• **Accelerate Convergence: Helps gradient-based algorithms like logistic regression or neural networks converge faster by keeping feature values in a similar range.
• **Maintain Interpretability of Scales: By converting all features to a common range, it’s easier to understand their relative impact on predictions.

Difference Between Normalization and Standardization

Standardization, also called Z-score normalization is a separate technique. It transforms data so that it has a mean of 0 and a standard deviation of 1.

Key Features of Standardization:

• Centers the data around zero
• Scales according to the variability (standard deviation)
• Useful for algorithms that assume Gaussian distributions such as linear regression,logistic regression and neural networks.
• Less sensitive to outliers compared to Min-Max normalization.

Standardization and Normalization are quite similar and confusing lets see the quick differences between them:

**Note: Normalization and Standardization are two distinct feature scaling techniques.

Different Data Normalization Techniques

There are several techniques to normalize data, each transforming values to a common scale in different ways

1. Min-Max Normalization

Min-Max normalization rescales a feature to a specific range, typically [0, 1]:

X_{\text{normalized}} = \frac{X - X_{\text{min}}}{X_{\text{max}} - X_{\text{min}}}

• The minimum value maps to 0
• The maximum value maps to 1
• Other values are scaled proportionally

2. Decimal Scaling

Decimal scaling normalizes data by shifting the decimal point of values:

v' = \frac{v}{10^j}

• j is the smallest integer such that the maximum absolute value of v′ is less than 1

3. Logarithmic Transformation

Log transformation compresses large values and spreads out small values:

X' = \log(X + 1)

• Reduces skewness in data
• Stabilizes variance across features

4. Unit Vector (Vector) Normalization

Scales a data vector to have a magnitude of 1:

X' = \frac{X}{||X||}

• Commonly used in text mining and machine learning algorithms like KNN
• Preserves direction but normalizes magnitude

Implementation in Python

We will demonstrate how to normalize and standardize features in Python using scikit-learn library.

1. Import Required Libraries

We will import the necessary libraries like pandas and scikit-learn.

Python `

import pandas as pd from sklearn.preprocessing import MinMaxScaler, StandardScaler

`

2. Loading the Dataset

We will load the dataset and separate the features from the target variable.

You can download the dataset from here.

• **pd.read_csv('heart.csv'): Reads the CSV file into a DataFrame.
• **drop('target', axis=1): Removes the target column from feature set.
• **df['target']: Selects the target variable for prediction.
• **df.head(): Displays the first 5 rows of the dataset. Python `

df = pd.read_csv('/content/heart.csv') X = df.drop('target', axis=1) y = df['target'] df.head()

`

**Output:

Dataset

3. Normalising the Features

We will normalize selected numeric features to scale them between 0 and 1.

• **MinMaxScaler(): Initializes a Min-Max scaler.
• **fit_transform(X[features]): Learns min and max values from data and scales features.
• **X.copy(): Creates a copy to avoid modifying the original dataset. Python `

features = ['age','trestbps','chol','thalach','oldpeak'] scaler = MinMaxScaler()

X_normalized = X.copy() X_normalized[features] = scaler.fit_transform(X[features]) X_normalized.head()

`

**Output:

Normalization

4. Standardizing the Features

We will standardize the same features to have mean 0 and standard deviation 1.

• **StandardScaler(): Initializes a standard scaler.
• **fit_transform(X[features]): Computes mean and standard deviation, then standardizes the features.

**Note: Standardization is less sensitive to outliers compared to normalization.

Python `

scaler_z = StandardScaler() X_standardized = X.copy() X_standardized[features] = scaler_z.fit_transform(X[features]) X_standardized.head()

`

**Output:

Standardization

Deciding Which Technique to Use

• Use normalization for distance-based models or when a specific range is required.
• Use standardization for algorithms that assume Gaussian distribution or are sensitive to variance.