Question

In python-

Create a class defined for Regression. Class attributes are data points for x, y, the slope and the intercept for the regression line. Define an instance method to find the regression line parameters (slope and intercept). Plot all data points on the graph. Plot the regression line on the same plot.

Answer 1

Python Code:

Input Datase:------------------------------------------------------------

	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
5	2.9	56642.0
6	3.0	60150.0
7	3.2	54445.0
8	3.2	64445.0
9	3.7	57189.0
10	3.9	63218.0
11	4.0	55794.0
12	4.0	56957.0
13	4.1	57081.0
14	4.5	61111.0
15	4.9	67938.0
16	5.1	66029.0
17	5.3	83088.0
18	5.9	81363.0
19	6.0	93940.0
20	6.8	91738.0
21	7.1	98273.0
22	7.9	101302.0
23	8.2	113812.0
24	8.7	109431.0
25	9.0	105582.0
26	9.5	116969.0
27	9.6	112635.0
28	10.3	122391.0
29	10.5	121872.0

_---------------------------------------------------------------------------------

# Informaion of employess of company. 30 employees.
# We have employees and their salary. We need to understand
# the correlation between both the columns. We need to predict
# salaries based on the number of experience an employee has
# and will compare it with actual salary.

import pandas as pd
import matplotlib.pyplot as plt
dataset = pd.read_csv('Salary_Data.csv')
dataset.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

x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
# Splitting into Training & Testing
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)

# Dataset Divided into 70 and 30 ratio.

# We dont need to apply Feature Scaling in Regression as
# the Library takes care of the FS itself.
# Below SLR library will take care of FS
# To fit the Simple Linear Regression to the Training Set.
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train, y_train)
# Regressor is the machine which has learnt from the training
# data. Now the regressor is the Machine which will now give results.
# Predicting Test Set Results
y_pred = regressor.predict(x_test)

#To retrieve the intercept:
print("Intercept of regression model :",regressor.intercept_)
#For retrieving the slope:
print("Slope of regression model :",regressor.coef_)

Intercept of regression model : 26816.19224403119 -------------------( intercept and slope ) Slope of regression model

 : [9345.94244312]

#---------------------------------------------------------------------------------------------------------------------

# graph between actual dataset value and predicted by regression model values

df = pd.DataFrame({'Actual Value': y_test.flatten(), 'Predicted Value': y_pred.flatten()})
df1 = df.head(25)
df1.plot(kind='bar',figsize=(12,7))
plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')
plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')
plt.show()

# Visualizing the Training Set Results
plt.scatter(x_train, y_train, color = 'red',label='Data Points')
plt.plot(x_train, regressor.predict(x_train), color = 'blue',label="Regression Line")
plt.title('Salary vs Experience (Training Set)')
plt.legend()
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

# Visualizing the Test Results
plt.scatter(x_test, y_test, color = 'red',label='Data Points')
plt.plot(x_train, regressor.predict(x_train), color = 'blue',label="Regression Line")
plt.title('Salary vs Experience (Test Set)')
plt.legend()
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

Screen shot: