Simple Linear Regression Model in 7 Steps with Python

Simple Linear Regression is the simplest machine learning model used in summarizing and study relationships between two quantitative variables. The linear regression equation can be represented below.

We have a dataset called Salary_Data.csv.You can reach the dataset from the link below in the reference. The dataset contains the year experiences and the salary of 30 people. Our goal is to create a model for this dataset. Firstly open the folders in the file explorer in your Spyder IDE. The necessary points are clearly described in comments blocks (#) of the code

Step#1

#NumPy is the fundamental package for scientific computing with Python. It contains among other things:
#1 Libraries importes
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Step#2

'''
# Single selections using iloc and DataFrame
# Rows:
data.iloc[0] # first row of data frame (Aleshia Tomkiewicz) - Note a Series data type output.
data.iloc[1] # second row of data frame (Evan Zigomalas)
data.iloc[-1] # last row of data frame (Mi Richan)
# Columns:
data.iloc[:,0] # first column of data frame (first_name)
data.iloc[:,1] # second column of data frame (last_name)
data.iloc[:,-1] # last column of data frame (id)
''''
#2 Importing the dataset: 
#The iloc indexer for Pandas Dataframe is used for integer-location #based indexing / selection by position.
#X is a matrix and matrix of features and matrix of independent #variable
#Y is a vector and vector of the independent variable.
dataset = pd.read_csv("Salary_Data.csv")
X= dataset.iloc[:, :-1].values
Y=dataset.iloc[:, 1].values
import com.helpers.TestBase;

Step #3:

#3 Splitting the data into training sets and test sets: 
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=1)

Step #4:

#4 Fitting the Simple Linear Regression to the Training Set 
# In library from sklearn we import the class which is linear #regression class: 
from sklearn.linear_model import LinearRegression
#Create an object called regressor... Ordinary least squares Linear #Regression.
regressor = LinearRegression()
#Fit the linear regression model to the training set... We use the #fit method
#the arguments of the fit method will be training sets 
regressor.fit(X_train,Y_train)

Step #5:

#5 Predicting the Test set Results: 
#Lets put all the predicted salaries into a single vector. 
# Use the prediction method for some observations. 
#These predictions are applied to test data X and we will get the predicted Y #values… 
Y_pred = regressor.predict(X_test)
#Compare the y_pred vs Y_test

Step #6:

#6 Visualizing the training set results: 
#import matplotlib.pyplot as plt (for plotting the graph)
#Real Values
plt.scatter(X_train, Y_train, color='Blue') 
plt.plot(X_train, regressor.predict(X_train), color='Red' )
#Plot Regression Line(Predicted values)
plt.title('Salary vs Experience (Traininig set)')
plt.xlabel('Years of experience')
plt.ylabel('Salary')
plt.show()

Step #7:

#7 Visualizing the test set results: 
#Test set
plt.scatter(X_test, Y_test, color='Blue') 
plt.plot(X_train, regressor.predict(X_train), color='Red' ) 
#The regressor trained on training set 
plt.title('Salary vs Experience (Test set)')
plt.xlabel('Years of experience')
plt.ylabel('Salary')
plt.show()
#The dot values are test values. The linear regression line willbe #the same as the previous one.

The simple linear regression learns with training dataset and we check the model in the test dataset. It looks good. See you at the next article about the machine learning models.

Reference:

sametgirgin/Machine-Learning-Models

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