EDA is Exploratory Data Analysis which is an initial data analysis process that aims to understand the characteristics, structure, and important components of the dataset before conducting further statistical analysis or predictive modeling. So that EDA makes it easier for us to know the information from the data we have and to help in the further process that we will do on the data.

After doing EDA, the next step is supervised learning where supervised learning is a learning process by giving marked data to the model. The model is then learned and optimized to be able to predict the desired output for unknown data so that we can predict the future related to the data we have.

Study time is one of the factors that affect the grades obtained during the exam. How long and focused we are when studying to understand the material that will be tested later by reading and practicing. Analyzing study hours can make estimates regarding the results that will be obtained later.

1. pandas
2. numpy
3. matplotlib
4. seaborn

The analysis is done by modeling machine learning regression in the form of :

1. Linear Regression
2. Decision Tree Regressor
3. Random Forest Regressor

from each regression modeling can be predicted on the data which is then evaluated on each model to find the best performance.