Python Code : Machine Learining - Simple Linear Regression
## Importing the libraries
import numpy as npimport matplotlib.pyplot as pltimport pandas as pd## Importing the dataset
dataset = pd.read_csv('Salary_Data.csv')X = dataset.iloc[:, :-1].valuesy = dataset.iloc[:, -1].values## Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)## Training the Simple Linear Regression model on the Training set
from sklearn.linear_model import LinearRegressionregressor = 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 = '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()
## 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()
Sample Data
Experience Salary
1.1 39343
1.3 46205
1.5 37731
2 43525
2.2 39891
2.9 56642
3 60150
3.2 54445
3.2 64445
3.7 57189
Comments (1)
Learn Linear regression concept:
https://www.techtreeblogs.in/blog/view_page.php?id=2f93fa3f-ca52-4c61-ba7a-e85630bc8884
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