Problem_12: Handle missing values in a dataset using Scikit-learn

Veröffentlicht am: 01 Januar 1970
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Problem_12:
Handle missing values in a dataset using Scikit-learn

#pythoncode :

import numpy as np
from sklearn.impute import SimpleImputer

Sample data with missing values
X = np.array([[1, 2], [np.nan, 3], [7, 6]])

Handle missing values
imputer = SimpleImputer(strategy='mean')
X_imputed = imputer.fit_transform(X)

print(X_imputed)

Explanation: This code trains a random forest classifier on the Iris dataset, splits the data, and evaluates the model's accuracy.

#sample_output :

[[1. 2. ]
[4. 3. ]
[7. 6. ]]

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