# Polynomial regression
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.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)
'''
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X,y)

from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(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')

# Visualizing the polynomial regression results
plt.scatter(X, y, color='red')
plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color='blue')

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')


# Predicting results
lin_reg.predict(6.5)
lin_reg_2.predict(poly_reg.fit_transform(6.5))