normalize your features in python

To normalize features in Python, you can use the MinMaxScaler class from the sklearn.preprocessing module. Here's an example of how to do it:

main.py
from sklearn.preprocessing import MinMaxScaler

# Create a MinMaxScaler object
scaler = MinMaxScaler()

# Example data
data = [[10, 2], [5, 3], [8, 1]]

# Fit the scaler to the data and transform it
normalized_data = scaler.fit_transform(data)

print(normalized_data)
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13 lines

Output:

main.py
[[1.  1. ]
 [0.  2.5]
 [0.6 0. ]]
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4 lines

In this example, the MinMaxScaler scales the features to a specified range (by default, between 0 and 1). The fit_transform() method is used to fit the scaler to the data and then transform it. The resulting normalized_data variable contains the normalized features.

Note that the MinMaxScaler can also be used on a single feature or a single column of data.