Dataset: Predict Students Dropout And Academic Success

About this Dataset

Predict Students Dropout And Academic Success

Dataset Column Descriptions

This dataset includes various features that were known at the time of student enrollment. Below is a description of each column in the dataset:

Marital Status: The marital status of the student (e.g., single, married, divorced).
Application Mode: Refers to the mode or type of application the student submitted to enroll in the course.
Application Order: Indicates the order in which the student applied for the course. For example, whether it was the student’s first, second, or third choice.
Course: The course or degree program the student is enrolled in (e.g., Computer Science, Engineering, etc.).
Daytime/Evening Attendance: Specifies whether the student attends the course during the day or in the evening, representing their attendance schedule.
Previous Qualification: The type of academic qualification the student had before enrolling in the course (e.g., high school diploma, vocational training).
Previous Qualification (Grade): The final grade or score associated with the student's previous qualification.
Nationality: The nationality of the student.
Mother's Qualification: The highest academic qualification attained by the student's mother.
Father's Qualification: The highest academic qualification attained by the student's father.

These features represent important demographic, academic, and socio-economic factors, which are crucial for predicting a student's academic outcome.

Tables

Predict Student Dropout And Academic Success

@kaggle.syedfaizanalii_predict_students_dropout_and_academic_success.predict_student_dropout_and_academic_success

120.41 KB
4424 rows
37 columns


CREATE TABLE predict_student_dropout_and_academic_success (
  "marital_status" BIGINT,
  "application_mode" BIGINT,
  "application_order" BIGINT,
  "course" BIGINT,
  "daytime_evening_attendance" BIGINT,
  "previous_qualification" BIGINT,
  "previous_qualification_grade" DOUBLE,
  "nacionality" BIGINT,
  "mother_s_qualification" BIGINT,
  "father_s_qualification" BIGINT,
  "mother_s_occupation" BIGINT,
  "father_s_occupation" BIGINT,
  "admission_grade" DOUBLE,
  "displaced" BIGINT,
  "educational_special_needs" BIGINT,
  "debtor" BIGINT,
  "tuition_fees_up_to_date" BIGINT,
  "gender" BIGINT,
  "scholarship_holder" BIGINT,
  "age_at_enrollment" BIGINT,
  "international" BIGINT,
  "curricular_units_1st_sem_credited" BIGINT,
  "curricular_units_1st_sem_enrolled" BIGINT,
  "curricular_units_1st_sem_evaluations" BIGINT,
  "curricular_units_1st_sem_approved" BIGINT,
  "curricular_units_1st_sem_grade" DOUBLE,
  "curricular_units_1st_sem_without_evaluations" BIGINT,
  "curricular_units_2nd_sem_credited" BIGINT,
  "curricular_units_2nd_sem_enrolled" BIGINT,
  "curricular_units_2nd_sem_evaluations" BIGINT,
  "curricular_units_2nd_sem_approved" BIGINT,
  "curricular_units_2nd_sem_grade" DOUBLE,
  "curricular_units_2nd_sem_without_evaluations" BIGINT,
  "unemployment_rate" DOUBLE,
  "inflation_rate" DOUBLE,
  "gdp" DOUBLE,
  "target" VARCHAR
);