72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
# Importing the Libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importing the dataset
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dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')
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X = dataset.iloc[:, [2, 3]].values
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y = dataset.iloc[:, 4].values
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# Splitting the dataset into the Training set and Test set
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
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# Feature Scaling
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from sklearn.preprocessing import StandardScaler
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sc = StandardScaler()
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X_train = sc.fit_transform(X_train)
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X_test = sc.transform(X_test)
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# Fitting Logistic Regression to the Training set
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from sklearn.linear_model import LogisticRegression
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classifier = LogisticRegression()
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classifier.fit(X_train, y_train)
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# Predicting the Test set results
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y_pred = classifier.predict(X_test)
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# Making the Confusion Matrix
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import classification_report
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cm = confusion_matrix(y_test, y_pred)
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print(cm) # print confusion_matrix
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print(classification_report(y_test, y_pred)) # print classification report
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#Visualization
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from matplotlib.colors import ListedColormap
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X_set,y_set=X_train,y_train
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X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
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np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
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plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
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alpha = 0.75, cmap = ListedColormap(('red', 'green')))
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plt.xlim(X1.min(),X1.max())
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plt.ylim(X2.min(),X2.max())
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for i,j in enumerate(np. unique(y_set)):
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plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
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c = ListedColormap(('red', 'green'))(i), label=j)
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plt. title(' LOGISTIC(Training set)')
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plt. xlabel(' Age')
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plt. ylabel(' Estimated Salary')
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plt. legend()
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plt. show()
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X_set,y_set=X_test,y_test
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X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
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np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
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plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
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alpha = 0.75, cmap = ListedColormap(('red', 'green')))
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plt.xlim(X1.min(),X1.max())
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plt.ylim(X2.min(),X2.max())
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for i,j in enumerate(np. unique(y_set)):
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plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
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c = ListedColormap(('red', 'green'))(i), label=j)
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plt. title(' LOGISTIC(Test set)')
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plt. xlabel(' Age')
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plt. ylabel(' Estimated Salary')
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plt. legend()
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plt. show()
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