Practical demonstration of SimpleImputer with CV and for loop

simpleimputer_pipeline_for.py

# import required libraries
from sklearn.ensemble import RandomForestClassifier # can be any classifier of your choice
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.pipeline import Pipeline

# define a list of all strategies to be evaluated
strategies = ['mean', 'median', 'most_frequent', 'constant']

# for loop to evaluate all the strategies
for s in strategies:
	# define modeling pipeline. Replace fill_value parameter with what constant you want to use
	pipeline = Pipeline(steps=[('i', SimpleImputer(strategy=s, fill_value = 0)), ('m', RandomForestClassifier())])
	# define cross-validation criteria
	cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
	# fit and evaluate the model defined in pipeline with cross-validation as defined in cv
	scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv)
	# print the mean accuracy score
	print('%s: %.3f' % (s, np.mean(scores)))