import matplotlib.pyplot as plt
from sklearn import datasets
boston = datasets.load_boston()
x = boston.data
y = boston.target
x = x[y < 50.0]
y = y[y < 50.0]
Linear Regression
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=666)
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)
lin_reg.score(X_test, y_test)
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import GridSearchCV
param_grid = [
{
'weights':['uniform'],
'n_neighbors': [i for i in range(1, 11)]
},
{
'weights':['distance'],
'n_neighbors': [i for i in range(1, 11)],
'p': [i for i in range(1, 6)]
}
]
knn_reg = KNeighborsRegressor()
grid_search = GridSearchCV(knn_reg, param_grid, n_jobs=-1, verbose=1)
grid_search.fit(X_train, y_train)