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Github Link: https://github.com/Jagadeesh-Surendran/python-_machine_problems/blob/main/Problem_6%3A%20Perform%20feature%20selection%20using%20Recursive%20Feature%20Elimination%20(RFE)
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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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