# -*- coding: utf-8 -*-
"""
Created on Tue Mar 24 20:02:04 2020

@author: vanda
"""
# Simple linear regression

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:, :-1].values # Matrix of Independent variable / Features (2D)
y = dataset.iloc[:, 1].values # Vector of Dependent Variable (1D)

# 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 = 1/3, random_state = 0)
"""Randon_state = 0 makes all the random factor influencing the algo as zero, 
or else each run will bring different answers."""

# Feature Scaling
# We will not do Feature Scaling in this case as the libraries of SLR will take care of this
"""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)"""


# Fitting Simple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
"""fit method is used to fit SLR to the training set, that means our SLR learnt
the correlation between the salary and years of experience"""

# Predicting the Test Set results
y_pred = regressor.predict(X_test)

# Visualizing the Training set results
plt.scatter(X_train, y_train, color ='red')
plt.plot(X_train, regressor.predict(X_train), color = 'blue') #regression line
plt.title('Salary vs Experience (Training set)')
plt.xlabel('Years of experience')
plt.ylabel('Salary')
plt.show()

# Visualizing the Test set results
plt.scatter(X_test, y_test, color ='red')
plt.plot(X_train, regressor.predict(X_train), color = 'blue') #regression line
plt.title('Salary vs Experience (Test set)')
plt.xlabel('Years of experience')
plt.ylabel('Salary')
plt.show()













