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Data normalization is a crucial preprocessing step in machine learning and statistics, ensuring that features contribute equally by scaling them to a common range. Below are the most common methods to normalize data in Python.
1. Min-Max Normalization
This method scales data to a range, typically [0, 1].
Steps:
Import MinMaxScaler from sklearn.preprocessing.
Fit and transform the data using the scaler.
Example:
from sklearn.preprocessing import MinMaxScalerimport numpy as np# Sample datadata = np.array([[10], [20], [30], [40], [50]])# Initialize and apply Min-Max Scalerscaler = MinMaxScaler()normalized_data = scaler.fit_transform(data)print(normalized_data)प्रतिलिपि बनाई गई!✕कॉपी करेंOutput: Values are scaled between 0 and 1.
2. Z-Score Normalization (Standardization)
This method scales data to have a mean of 0 and a standard deviation of 1.
Steps:
Import StandardScaler from sklearn.preprocessing.
Fit and transform the data using the scaler.
Example:
पसंद करें नापसंद करें How to Normalize Data Using scikit-learn in Python
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