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dataset = pd.read_csv('Salaries.csv')
X = dataset.iloc[:,1:2].values
Y = dataset.iloc[:,2].values

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from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X,Y)

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plt.scatter(X , Y, color = 'red')
plt.plot(X , lin_reg_2.predict( poly_reg.fit_transform(X)), color = 'blue')
plt.title("Polynomial Regression")
plt.xlabel("Position level")
plt.ylabel("Salary")
plt.show()

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plt.scatter(X , Y, color = 'red')
plt.plot(X , lin_reg.predict(X), color = 'blue')
plt.title("Linear Regression")
plt.xlabel("Position level")
plt.ylabel("Salary")
plt.show()

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from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

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y_pred = classifier.predict(X_test)

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from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)

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from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

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from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

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dataset = pd.read_csv('Purchase.csv')
X = dataset.iloc[:, [1, 2]].values
y = dataset.iloc[:, 3].values