ML-tutorial/poly.py at master · aprameyabharadwaj/ML-tutorial · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Sun May 28 10:57:52 2017

@author: Aprameya
"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values


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)

lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)

plt.scatter(X, y, color = 'red')
plt.plot(X , lin_reg.predict(X), color = 'blue')
plt.title('Truth or bluff lin reg')
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_grid , lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
plt.title('Truth or bluff lin reg')
plt.xlabel('Position Level')
plt.ylabel('Salary')
plt.show()

lin_reg.predict(6.5)
lin_reg_2.predict(poly_reg.fit_transform(6.5))