In this project, we explore the degree students’ academic performance dataset. Several techniques for data exploration, manipulation, transformation, and visualization are applied using the R programming language to obtain insights regarding the various factors that affect student grades.
The dataset contains 923 rows and 33 columns, that includes 16 integer datatype values, 9 string datatype values and 8 Boolean datatype values. The data problems and unique properties related to the dataset have been thoroughly studied.
The data is appropriately preprocessed and deep analyses have been conducted. Proper commenting, variable naming conventions and indentation standards have also been followed for all the R programming code.
Overall, there are 9 analysis questions, each with several analysis. Some additional features have also been added to improve the preprocessing and analyzing of data. Thus, several factors were identified to be related to the student’s grades, which can help universities to identify problems among the students and help them attain better grades to bring out their potential.