linear_regression.py

# -*- coding: utf-8 -*-
"""
Created on Fri Jul 26 18:04:25 2019

@author: ajayc
"""

# 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('Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].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 = 1/3, 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)"""

# Fitting Simple Linear regression in Training Set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)

# Predicting salaries of test data
y_pred = regressor.predict(X_test)

# Visualising Training Set Results
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()

# Visualising Test Set Results
plt.scatter(X_test, y_test, 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()