Simple Linear Regression

• 0. Data Preprocessing
  • 0.1 Importing the libraries
  • 0.2 Importing the dataset
  • 0.3 Check if any null value
  • 0.4 Split into X & y
  • 0.5 Splitting the dataset into the Training set and Test set
• 1. Training the Simple Linear Regression model on the Training set
  • 1.1 Score
• 2. Predicting the Test set results
  • 2.1 Compare predicted results
• 3. Visualising
  • 3.1 Visualising the Training set results
• 3.1 Visualising the Test set results

0. Data Preprocessing

0.1 Importing the libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

0.2 Importing the dataset

dataset = pd.read_csv('Salary_Data.csv')
dataset

	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

0.3 Check if any null value

dataset.isna().sum()

YearsExperience    0
Salary             0
dtype: int64

dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30 entries, 0 to 29
Data columns (total 2 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   YearsExperience  30 non-null     float64
 1   Salary           30 non-null     float64
dtypes: float64(2)
memory usage: 608.0 bytes

0.4 Split into X & y

X = dataset.drop('Salary', axis=1)
X

	YearsExperience
0	1.1
1	1.3
2	1.5
3	2.0
4	2.2
5	2.9
6	3.0
7	3.2
8	3.2
9	3.7
10	3.9
11	4.0
12	4.0
13	4.1
14	4.5
15	4.9
16	5.1
17	5.3
18	5.9
19	6.0
20	6.8
21	7.1
22	7.9
23	8.2
24	8.7
25	9.0
26	9.5
27	9.6
28	10.3
29	10.5

y = dataset['Salary']
y

0      39343.0
1      46205.0
2      37731.0
3      43525.0
4      39891.0
5      56642.0
6      60150.0
7      54445.0
8      64445.0
9      57189.0
10     63218.0
11     55794.0
12     56957.0
13     57081.0
14     61111.0
15     67938.0
16     66029.0
17     83088.0
18     81363.0
19     93940.0
20     91738.0
21     98273.0
22    101302.0
23    113812.0
24    109431.0
25    105582.0
26    116969.0
27    112635.0
28    122391.0
29    121872.0
Name: Salary, dtype: float64

0.5 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 = 0.3, random_state = 0)

1. Training the Simple Linear Regression model on the Training set

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

LinearRegression()

1.1 Score

regressor.score(X_test,y_test)

0.9740993407213511

2. Predicting the Test set results

y_pred = regressor.predict(X_test)

d = {'y_pred': y_pred, 'y_test': y_test}

2.1 Compare predicted results

pd.DataFrame(d)

	y_pred	y_test
2	40817.783270	37731.0
28	123188.082589	122391.0
13	65154.462615	57081.0
10	63282.410357	63218.0
26	115699.873560	116969.0
24	108211.664531	109431.0
27	116635.899689	112635.0
11	64218.436486	55794.0
17	76386.776158	83088.0

3. Visualising

3.1 Visualising the 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()

3.1 Visualising the 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 (Test set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()