Machine Learning Linear Regression example

Linear Regression is a Machine Learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between dependent and independent variables, they are considering and the number of independent variables being used.

                                                                                                                                                                                                                    Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). So, this regression technique finds out a linear relationship between x (input) and y(output). Hence, the name is Linear Regression.

In the figure above, X (input) is the work experience and Y (output) is the salary of a person. The regression line is the best fit line for our model.

import required libraries

import pandas as pd 

import matplotlib.pyplot as plt 

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

read csv file using pandas 

data = pd.read_csv('F:/Machine_learn/machine_learning/Salary_Data.csv')

See the firts 5 data Using

data.head()

	YearsExperience	Salary
0	1.1	39343.0
1	1.3	46205.0
2	1.5	37731.0
3	2.0	43525.0
4	2.2	39891.0

plottting data using matplotlib 

data.plot(x='YearsExperience', y='Salary')

plt.title('Exp vs Salary')

plt.xlabel('YearsExperience')

plt.ylabel('Salary')

plt.show()

                                                                                create x and y 

x = data['YearsExperience'].values.reshape(-1,1)

y = data['Salary'].values

                                          Split data into train and test using train_test_split method

    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

                                                            Training algorithm

Create object regressor

regressor = LinearRegression()

regressor.fit(X_train,y_train)

# intercept_

regressor.intercept_

#slope

regressor.coef_

Prediction our model 

# prediciton

y_pred = regressor.predict(X_test)

y_pred

# Look the actual data and predicted data

df = pd.DataFrame({'Actual':y_test.flatten(), 'predicted':y_pred.flatten()})

print(df)

	Actual	predicted
0	112635.0	115790.210113
1	67938.0	71498.278095
2	113812.0	102596.868661
3	83088.0	75267.804224
4	64445.0	55477.792045
5	57189.0	60189.699707