# -*- coding: utf-8 -*-
"""
Created on Thu Sep 12 15:47:19 2019

@author: AKSHITA SHARMA
"""
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)

#Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X,y)

#fitting Polynomial REgression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg2=LinearRegression()
lin_reg2.fit(X_poly,y)

#Visualising the Linear Regression results
plt.scatter(X,y, color='red')
plt.plot(X,lin_reg.predict(X),color = 'blue')
plt.title('Truth or Bluff (Linear Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

#Visualising the Polynomial Regresssion result
plt.scatter(X,y, color='red')
plt.plot(X,lin_reg2.predict(X_poly),color = 'blue')
plt.title('Truth or Bluff (Polynomial Regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

#Predicting a new result with linear Regression
lin_reg.predict(np.array([[6.5]]))

#Predicting a new result with Polynomial Regression
lin_reg2.predict(poly_reg.fit_transform(np.array([[6.5]])))
