-
Notifications
You must be signed in to change notification settings - Fork 0
/
Model2_POLY.py
26 lines (20 loc) · 1.07 KB
/
Model2_POLY.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
import numpy as np
from sklearn import linear_model
from sklearn.preprocessing import PolynomialFeatures
from sklearn import metrics
from sklearn.metrics import mean_squared_error, r2_score
def Poly( X_train , y_train , X_test , y_test ):
poly_features = PolynomialFeatures(degree=2)
X_train_poly = poly_features.fit_transform(X_train)
poly_model = linear_model.LinearRegression()
poly_model.fit(X_train_poly, y_train)
y_train_predicted = poly_model.predict(X_train_poly)
prediction = poly_model.predict(poly_features.fit_transform(X_test))
print('Co-efficient of linear regression', poly_model.coef_)
print('Intercept of linear regression model', poly_model.intercept_)
print('Mean Square Error', metrics.mean_squared_error(y_test, prediction))
print('Root Mean Square Error', np.sqrt(metrics.mean_squared_error(y_test, prediction)) )
print("score", r2_score(y_test , prediction))
#print(poly_model.score(X_test , y_test))
#Accuracy = 1 - np.mean(abs((prediction - np.mean(prediction)) / np.mean(prediction)))
#print(Accuracy)