The main objective of this Python project is to Analysis & Prediction the final grade of Portuguese high school students. This is Machine Learning Project and The algorithms used to implement this project are Linear Regression, ElasticNet Regression, Random Forest, Extra Trees, SVM, Gradient Boosted, Baseline.
The problem statement can be defined as follows: Considering the data set containing the attributes of 396 Portuguese students, the available functions of the data set were used and classification algorithms were defined to determine whether the student performed well in the final qualifying exam.
Description of the data set
These data reflect the performance of secondary school students in two Portuguese schools. Information attributes (student grades, demographic, social, and school-related characteristics) were collected using school reports and questionnaires. There are two sets of indicators for two different subjects: mathematics (math) and Portuguese (por). [Cortez and Silva, 2008], two data sets were modeled under binary / five-level classification and regression tasks. Important Note: The target attribute G3 has a strong correlation with the G2 and G1 attributes.
As universities are prestigious universities, it is a matter of great concern that students stay at these universities. It was found that the majority of students dropped out of university in the first year due to a lack of adequate support for undergraduate courses. For this reason, the first year of a bachelor’s degree is called the “make or break” year. Without finding any support for mastering the course and its complexity, you can lower the student’s motivation and cause him to drop out of the course.
There is a great need to develop an appropriate solution to help students stay in higher education. Early grade forecasting is one of the solutions aimed at monitoring the progress of students in the undergraduate courses of the university and leads to the improvement of the learning process of students on the basis of projected grades.
The use of machine learning with the extraction of educational information improves the learning process of students. Various models can be developed to estimate a student’s grades in registered courses, which will provide valuable information to make it easier for students to stay in those courses. This information can be used for the early identification of students at risk, based on which the system can recommend teachers to pay special attention to those students. This information will also help predict the grades of students in different courses to better monitor their performance in a way that will improve student retention in universities.
Using different packages, such as cuffs, seaborn, and matplotlib, to analyze the data set to predict the final price (G3), to display the data graphically or graphically along with various attributes.
Download the Complete Student Grade Analysis & Prediction Machine Learning Python Project Source code, Report.
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