Design and implement your own machine learning model for the following:

You are presented with students data (xAPI-Edu-Data.csv) and you are asked to design and implement a classification model that can predict the performance of students (Low-Level, Middle-Level, High-Level) as defined below.

Experiment with different machine learning algorithms, and report only the best performing model you can get for this task.

Data Information:

Number of Instances: 480 Number of Attributes: 16

The dataset consists of 480 student records and 16 features. The features are classified into three major categories:

(1) Demographic features such as gender and nationality.

(2) Academic background features such as educational stage, grade Level, and section.

(3) Behavioral features such as raised hand-on class, opening resources, answering surveys

by parents, and school satisfaction.

The data set includes also the school attendance and parent participation features.

The students are classified into three numerical intervals based on their total grade/mark:

Low-Level: interval includes values from 0 to 69

Middle-Level: interval includes values from 70 to 89

High-Level: interval includes values from 90-100