Creating a scatter plot is a common task in data visualization and machine learning for exploring the relationship between two variables. Below, we will provide a detailed example using Python, demonstrating how to create a scatter plot. We’ll use `matplotlib`

for visualization and `scikit-learn`

for a simple machine learning example.

### Explanation:

**Data Generation**:`make_regression`

from`scikit-learn`

is used to generate synthetic data for a regression problem. This creates 100 samples with 1 feature and some noise added to the target variable.

**Scatter Plot**:`plt.scatter`

is used to create a scatter plot of the data points. The`X`

values are plotted on the x-axis, and the`y`

values are plotted on the y-axis. The points are colored blue and labeled as ‘Data points’.

**Linear Regression**:- A
`LinearRegression`

model from`scikit-learn`

is fitted to the data using`model.fit(X, y)`

. - The predicted values
`y_pred`

are obtained using`model.predict(X)`

.

- A
**Plotting the Regression Line**:`plt.plot`

is used to plot the regression line. The line is colored red, has a linewidth of 2, and is labeled as ‘Regression line’.

**Enhancing the Plot**:- A title and labels for the x and y axes are added using
`plt.title`

,`plt.xlabel`

, and`plt.ylabel`

. - A legend is added using
`plt.legend`

to differentiate between the data points and the regression line.

- A title and labels for the x and y axes are added using
**Displaying the Plot**:`plt.show`

is used to display the plot.

# Welcome to Scatter Plot Page

You will be redirected to a new page after 10 seconds.

If you don’t want to wait, click here to go to the new page immediately.