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P. 335
# Predicted values
y_pred = slope * data_x + intercept
# Plot data and regression line
plt.scatter(data_x, data_y)
plt.plot(data_x, y_pred, color='red')
# Add labels and title
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('Simple Linear Regression')
# Show the plot
plt.show()
# Print slope and intercept
print("The slope of the regression line is: {:.2f}".format(slope))
print("The intercept of the regression line is: {:.2f}".format(intercept))
Output:
Simple Linear Regression
9
8
7
Y values 6
5
4
3
2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0
X values
The slope of the regression line is: 0.21
The intercept of the regression line is: 4.56
In Program 1,
● The program imports NumPy for numerical calculations and Matplotlib’s pyplot module for plotting.
● Sample data for both the independent variable (x) and the dependent variable (y) is provided.
● Mean, standard deviation, covariance, and slope of the data are computed to understand their relationships.
Machine Learning Algorithms 333

