1 from sklearn.datasets import load_boston
2 boston = load_boston()
3 boston.keys()
boston.target
import pandas as pd
df = pd.DataFrame(boston.data)
df
from sklearn.linear_model import LinearRegression
LineR = LinearRegression()
LineR.fit(x.reshape(-1,1),y)
LineR.coef_
LineR.intercept_
import matplotlib.pyplot as plt
x=boston.data[:,5]
y=boston.target
plt.figure(figsize=(10,6))
plt.scatter(x,y)
plt.plot(x,9.1*x-34,'r')
plt.show()
import matplotlib.pyplot as plt
x = boston.data[:,12].reshape(-1,1)
y = boston.target
plt.figure(figsize=(10,6))
plt.scatter(x,y)
from sklearn.linear_model import LinearRegression
lineR=LinearRegression()
lineR.fit(x,y)
y_pred = lineR.predict(x)
plt.plot(x,y_pred)
plt.show()
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree = 2)
x_poly = poly.fit_transform(x)
lrp = LinearRegression()
lrp.fit(x_poly,y)
y_poly_pred = lrp.predict(x_poly)
plt.scatter(x,y)
plt.scatter(x,y_pred)
plt.scatter(x,y_poly_pred)
plt.show()
