# Data Preprocessing Template

# 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:-1].values
y = dataset.iloc[:, 2].values
degree = 4
# 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.reshape(-1,1))"""

#Simple Linear Regression Model
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X,y)

#Polynomial regression model
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=degree)
X_poly = poly_reg.fit_transform(X)
lin_reg2 = LinearRegression()
lin_reg2.fit(X_poly,y)

#Visualizing Simple Linear Regression Model
plt.scatter(X,y,color="red")
plt.plot(X,lin_reg.predict(X),color="blue")
plt.xlabel("Job Level (1-10)")
plt.ylabel("Salary")
plt.title("Level Vs Salary (Simple Linear Regression)")
plt.show()


#Visualizing Polynomial Regression Model
plt.scatter(X,y,color="red")
plt.plot(X,lin_reg2.predict(poly_reg.fit_transform(X)),color="blue")
plt.xlabel("Job Level (1-10)")
plt.ylabel("Salary")
plt.title("Level Vs Salary (Polynomial Regression degree {})".format(degree))
plt.show()