# ignore warnings
import warnings
warnings.filterwarnings("ignore")
import pandas
from sklearn import model_selection
from sklearn.linear_model import LinearRegression
# load data
filename = 'housing.csv'
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(filename, names=names, delim_whitespace=True)
print(); print(dataframe.head())
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = LinearRegression()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(); print(results.mean())
import pandas
from sklearn import model_selection
from sklearn.linear_model import Ridge
# load data
filename = 'housing.csv'
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(filename, names=names, delim_whitespace=True)
print(); print(dataframe.head())
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = Ridge()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(); print(results.mean())
import pandas
from sklearn import model_selection
from sklearn.linear_model import Lasso
# load data
filename = 'housing.csv'
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(filename, names=names, delim_whitespace=True)
print(); print(dataframe.head())
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = Lasso()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(); print(results.mean())
import pandas
from sklearn import model_selection
from sklearn.linear_model import ElasticNet
# load data
filename = 'housing.csv'
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(filename, names=names, delim_whitespace=True)
print(); print(dataframe.head())
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = ElasticNet()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(); print(results.mean())
import pandas
from sklearn import model_selection
from sklearn.neighbors import KNeighborsRegressor
# load data
filename = 'housing.csv'
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(filename, names=names, delim_whitespace=True)
print(); print(dataframe.head())
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = KNeighborsRegressor()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(); print(results.mean())
import pandas
from sklearn import model_selection
from sklearn.tree import DecisionTreeRegressor
# load data
filename = 'housing.csv'
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(filename, names=names, delim_whitespace=True)
print(); print(dataframe.head())
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = DecisionTreeRegressor()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(); print(results.mean())
import pandas
from sklearn import model_selection
from sklearn.svm import SVR
# load data
filename = 'housing.csv'
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
dataframe = pandas.read_csv(filename, names=names, delim_whitespace=True)
print(); print(dataframe.head())
array = dataframe.values
X = array[:,0:13]
Y = array[:,13]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
model = SVR()
scoring = 'neg_mean_absolute_error'
results = model_selection.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)
print(); print(results.mean())