import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

dataset = pd.read_csv('data/Salary_Data.csv')
x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)

regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)

plt.scatter(x_train, y_train, color = 'red')
plt.plot(x_train, regressor.predict(x_train), color = 'blue')
plt.title('Salary vs Experience( Training Set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

plt.scatter(x_test, y_test, color = 'red')
plt.plot(x_test, y_pred, color = 'blue')
plt.title('Salary vs Experience( Test Set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

# to get the regressor coefficient and intercept
print(regressor.coef_)
print(regressor.intercept_)

# get prediction for a given value
print(regressor.predict([[12]]))
