Simple Linear Regression
Problem statement: Find a relation between the independent variable and dependent variable
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Variables:
Independent Variables : years_of_service
Dependent Variable : salary
#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#Change the working directory and set it as current console's working directory
#Importing the Dataset
dataset = pd.read_csv('Salaries.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
#Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3)
#Fitting Simple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
#Predicting the Test set results
y_pred = regressor.predict(X_test)
#Visualising the Training set results
plt.scatter(X_train, y_train, color = 'green')
plt.plot(X_train, regressor.predict(X_train), color = "red")
plt.title('Experience vs Salary (train set)')
plt.xlabel('experience')
plt.ylabel('salary')
plt.show()
#Visualising the Test set results
plt.scatter(X_test, y_test, color = 'red')
plt.plot(X_train, regressor.predict(X_train), color = 'yellow')
plt.title('Experience vs Salary (test set)')
plt.xlabel('experience')
plt.ylabel('salary')
plt.show()
