-
Notifications
You must be signed in to change notification settings - Fork 0
/
poly_regression.py
67 lines (55 loc) · 1.88 KB
/
poly_regression.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
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 4 01:30:56 2019
@author: dipak
"""
# Data Preprocessing Template
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:,1:2].values
y = dataset.iloc[:,2].values
# Splitting the dataset into the Training set and Test set
#from sklearn.cross_validation import train_test_split
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Feature Scaling
"""from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
sc_y = StandardScaler()
y_train = sc_y.fit_transform(y_train)"""
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X,y)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly,y)
lin_reg2 = LinearRegression()
lin_reg2.fit(X_poly,y)
plt.scatter(X,y,color = 'red')
plt.plot(X,lin_reg.predict(X), color = 'blue')
#plt.plot(X,lin_reg2.predict(poly_reg.fit_transform(X)), color = 'green')
plt.title('Truth or Bluff (Linear Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
#X_grid = np.arange(min(X),max(X),0.1)
#X_grid = X_grid.reshape((len(X_grid),1))
plt.scatter(X,y,color = 'red')
#plt.plot(X,lin_reg.predict(X), color = 'blue')
plt.plot(X,lin_reg2.predict(poly_reg.fit_transform(X)), color = 'green')
plt.title('Truth or Bluff (Linear Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
test = np.array([6.5])
test = test.reshape(1,-1)
lin_reg.predict(test)
test = np.array([6.5])
test = test.reshape(1,-1)
lin_reg2.predict(poly_reg.fit_transform(test))