Student-Marks-Prediction-With-Machine-Learning

I have some information about students like:

1. the number of courses they have opted for

2. the average time studied per day by students

3. marks obtained by students

1. The effect of number of courses on the marks of a student by scattering the plot

According to the above data visualization, we can say that the number of courses may not affect the marks of a student if the student is studying for more time daily

2.The relationship between the time a studied daily and the marks scored by the student

We can see that there is a linear relationship between the time studied and the marks obtained. This means the more time students spend studying, the better they can score.

3. I found the correlation between the marks scored by the students and the other two columns in the data.

So the time_studied column is more correlated with the marks column than the other column.

4. The I splitted the dataset into training and testing.

5. SO , I trained the Model using LinearRegression algorithm.

6. So lastly I have given input values as time_study and number_courses and predicted the output as Marks of the Student.

So this is how you can predict the marks of a student with machine learning using Python.

Summary:

So this is how I have solved the problem of student marks prediction with machine learning. It is a good regression problem as it easy to solve and understand.