from sklearn.datasets import load_boston

boston_data = load_boston()

X = boston_data.data

y = boston_data.target

from sklearn.linear_model import LinearRegression

model = LinearRegression()

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)

model.fit(X_train, y_train)

model.score(X_train, y_train)

from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X_train, y_train, cv = 5)

scores

from sklearn.metrics import explained_variance_score

explained_variance_score(y_test, model.predict(X_test))

from sklearn.metrics import mean_absolute_error

mean_absolute_error(y_test, model.predict(X_test))

from sklearn.metrics import median_absolute_error

median_absolute_error(y_test, model.predict(X_test))

from sklearn.metrics import r2_score

r2_score(y_test, model.predict(X_test))

from sklearn.preprocessing import PolynomialFeatures, StandardScaler

from sklearn.pipeline import make_pipeline

pipe = make_pipeline(StandardScaler(), PolynomialFeatures(degree = 3), LinearRegression())

pipe.fit(X_train, y_train)

pipe.score(X_train, y_train)

pipe.score(X_test, y_test)

pipe.steps[2][1].coef_

pipe.steps[2][1].coef_.max(), pipe.steps[2][1].coef_.min(), pipe.steps[2][1].coef_.std()

##

from sklearn.linear_model import Ridge

pipe = make_pipeline(StandardScaler(), PolynomialFeatures(degree = 3), Ridge())

pipe.fit(X_train, y_train)

pipe.score(X_train, y_train)

pipe.score(X_test, y_test)

pipe.steps[2][1].coef_.max(), pipe.steps[2][1].coef_.min(), pipe.steps[2][1].coef_.std()

##

from sklearn.linear_model import Lasso

pipe = make_pipeline(StandardScaler(), PolynomialFeatures(degree = 3), Lasso())

pipe.fit(X_train, y_train)

pipe.score(X_train, y_train)

pipe.score(X_test, y_test)

pipe.steps[2][1].coef_.max(), pipe.steps[2][1].coef_.min(), pipe.steps[2][1].coef_.std()

##

from sklearn.linear_model import ElasticNet

pipe = make_pipeline(StandardScaler(), PolynomialFeatures(degree = 3), ElasticNet())

pipe.fit(X_train, y_train)

pipe.score(X_train, y_train)

pipe.score(X_test, y_test)

pipe.steps[2][1].coef_.max(), pipe.steps[2][1].coef_.min(), pipe.steps[2][1].coef_.std()

####

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn import datasets

from sklearn import linear_model

dataset = datasets.load_iris()

dataset.data.shape

dataset.target.shape

holdout_percent = 0.4

X_train, X_test, y_train, y_test = train_test_split(dataset.data, \

dataset.target, test_size = holdout_percent, random_state = 42)

model = linear_model.LogisticRegression(penalty = 'l2', dual = False, tol = 0.0001, \

C = 1.0, fit_intercept = True).fit(X_train, y_train)

model.score(X_train, y_train)

from sklearn.metrics import accuracy_score

accuracy_score(y_test, model.predict(X_test))

from sklearn.metrics import cohen_kappa_score

cohen_kappa_score(y_test, model.predict(X_test))

from sklearn.metrics import confusion_matrix

confusion_matrix(y_test, model.predict(X_test))

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