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 import numpy as np

from sklearn.linear_model import LinearRegression

x= np.array([[1,1],[1,2],[3,4],[5,6]])

y = np.dot(x,np.array([1,2])) + 3

reg = LinearRegression().fit(x,y)

reg.score(x,y)reg.predict(np.array([[3,5]]))

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from sklearn.datasets import load_iris

from sklearn.linear_model import LogisticRegression

X,y= load_iris(return_X_y=True)

clf = LogisticRegression(random_state=0).fit(X,y)

clf.predict(X[:2,:])

clf.score(X,y)

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x=[[0],[1],[2],[3]]

y=[0,0,1,1]

from sklearn.neighbors import KNeighborsClassifier

neigh= KNeighborsClassifier(n_neighbors=3)

neigh.fit(x,y)

neigh.predict([[1.1]])

neigh.predict_proba([[0.9]])

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import numpy as np

from sklearn.pipeline import make_pipeline

from sklearn.preprocessing import StandardScaler

from sklearn.svm import SVC

x = np.array([[-1,-1],[-2,-1],[1,1],[2,1]])

y= np.array([1,1,2,2])

clf = make_pipeline(StandardScaler(),SVC(gamma="auto"))

clf.fit(x,y)

clf.predict([[-0.8,-1]])

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import numpy as np

x= np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])

y= np.array([1,1,1,2,2,2])

from sklearn.naive_bayes import GaussianNB

clf= GaussianNB()

clf.fit(x,y)

clf.predict([[-0.8,-1]])