33 lines
967 B
Python
33 lines
967 B
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.cross_validation 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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cm = confusion_matrix(y_test, y_pred)
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