selecting feature is very imp to reduce over-fitting and to increase accuracy

Which features needs to be selected as input to ML and which needs to be removed?. this reusable code snippets helps you to decide which features are important and which are not important

Author: naresh242

featureSelection.py

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+from pandas import read_csv
+from numpy import set_printoptions
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import Normalizer
+from sklearn.preprocessing import Binarizer
+from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import chi2
+from sklearn.feature_selection import RFE
+from sklearn.linear_model import LogisticRegression
+from sklearn.decomposition import PCA
+from sklearn.ensemble import ExtraTreesClassifier
+
+filename='C:/Users/na347632/Desktop/FAI/learning/python_pro_bundle/machine_learning_mastery_with_python/code/chapter_05/pima-indians-diabetes.data.csv'
+names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
+dataframe=read_csv(filename,names=names)
+array=dataframe.values
+X=array[:,0:8]
+Y=array[:,8]
+
+#univariate selection
+test=SelectKBest(score_func=chi2,k=4)
+fit=test.fit(X,Y)
+features=fit.transform(X)
+print(fit.scores_)
+print(features[0:5,:])
+
+# Recursive Feature Elimination
+model=LogisticRegression()
+fit=RFE(model,4)
+featureselect=fit.fit(X,Y)
+print(featureselect.n_features_)
+print(featureselect.support_)
+print(featureselect.ranking_)
+
+#PCA
+pcamo=PCA(n_components=4)
+fit=pcamo.fit(X)
+print(fit.explained_variance_)
+print(fit.components_)
+
+#feature importance using ExtraTreesClassifier
+model=ExtraTreesClassifier()
+model.fit(X,Y)
+print(model.feature_importances_)
+
+
+