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- from sklearn.datasets import make_classification
- from sklearn.model_selection import train_test_split
- from sklearn.preprocessing import MinMaxScaler
- from sklearn.linear_model import LogisticRegression
- from sklearn.metrics import accuracy_score
- X,y = make_classification(n_samples=1000,n_features=20,n_informative=15,n_redundant=5,random_state=5)
- scaler = MinMaxScaler()
- X = scaler.fit_transform(X)
- X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.33,random_state=1)
- model=LogisticRegression()
- model.fit(X_train,y_train)
- yhat=model.predict(X_test)
- accuracy=accuracy_score(y_test,yhat)
- print('Accuracy: %.3f'%(accuracy*100))
- X,y = make_classification(n_samples=1000,n_features=20,n_informative=15,n_redundant=5,random_state=5)
- X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.33,random_state=1)
- scaler = MinMaxScaler()
- scaler.fit(X_train)
- X_train = scaler.transform(X_train)
- X_test = scaler.transform(X_test)
- model=LogisticRegression()
- model.fit(X_train,y_train)
- yhat=model.predict(X_test)
- accuracy=accuracy_score(y_test,yhat)
- print('Accuracy: %.3f'%(accuracy*100))
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