Supervised machine learning

Summary

first imported necessary libraries, including pandas, numpy, matplotlib, and scikit-learn.

than loaded and explored the dataset, which contains information about students' study hours and their corresponding scores.

than visualized the relationship between study hours and scores using a scatter plot, which revealed a clear positive linear correlation.

and prepared the data for training by separating it into independent variable (study hours) and dependent variable (scores).

after that, split the data into training and testing sets, with 80% used for training and 20% for testing.

than trained a simple linear regression model using scikit-learn's LinearRegression class.

I made predictions on the testing data using the trained model.

I calculated the predicted score for a student who studies for 9.25 hours per day.

To visualize the results, I created a scatter plot of the data points and overlaid the regression line generated by the model.