# 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.model_selection 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)"""

#linear regression model
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) 
 
#visualising the linear regression results.
plt.scatter(X,y,color='red')
plt.plot(X,lin_reg.predict(X),color='black')
plt.title('truth or bluff (linear regression model)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show() 

#visualising the polynomial regression results.
X_grid=np.arange(min(X),max(X)+1,0.1)
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 (Polynomial regression model)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

lin_reg.predict(6.5)  #prediction using linear regression.

lin_reg_2.predict(poly_reg.fit_transform(np.array([[6.5]]))) #prediction using polynomial regression.