import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# Improting the dataset

dataset = pd.read_csv('Position_Salaries.csv')

X = dataset.iloc[:, 1:2].values

y = dataset.iloc[:, 2].values

# Splitting the dataset into training set and test set

print(X)

"""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)

"""

# Linear Regression

from sklearn.linear_model import LinearRegression

lin_reg = LinearRegression()

lin_reg.fit(X, y)

#Polynomial Regression

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)

print(X_poly)

# Visualizing linear regression result

"""

plt.scatter(X, y, color = 'red')

plt.plot(X, lin_reg.predict(X), color = 'blue')

plt.title('T or B')

plt.xlabel('Position')

plt.ylabel('salary')

plt.show()

"""

# Visualizing polynomial regression result

"""plt.scatter(X, y, color = 'green')

plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'yellow')

plt.title('Truth or Bluff (Polynomial Regression)')

plt.xlabel('Position')

plt.ylabel('salary')

plt.show()

"""

#

print(lin_reg.predict(6.5))

print("Polynomial:")

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