Synthetic Student Profiles with Academic Outcomes Dataset

Dataset Features

• student_id: Unique identifier for each student.
• school: Attended school (e.g., GP or MS).
• sex: Gender of the student (F/M).
• age: Student's age in years.
• address_type: Urban or Rural home location.
• family_size: Family size (Less than or equal to 3 / Greater than 3).
• parent_status: Parental cohabitation status (Living together / Apart).
• mother_education / father_education: Highest education level completed (e.g., Primary, Secondary, Higher).
• mother_job / father_job: Occupation of the student's parents.
• school_choice_reason: Reason for choosing the school (e.g., Reputation, Proximity).
• guardian: Primary caregiver (e.g., Mother, Father, Other).
• travel_time: Daily travel time to school.
• study_time: Weekly study time outside school.
• class_failures: Number of past class failures.
• school_support / family_support: Extra academic support received at school and from family (Yes/No).
• extra_paid_classes: Attending paid private tutoring (Yes/No).
• activities: Participation in extracurricular activities (Yes/No).
• nursery_school: Attended preschool (Yes/No).
• higher_ed: Desire to pursue higher education (Yes/No).
• internet_access: Access to the internet at home (Yes/No).
• romantic_relationship: Currently in a romantic relationship (Yes/No).
• family_relationship: Quality of family relationships (numeric scale).
• free_time: Amount of free time after school (numeric scale).
• social: Frequency of social activities with peers (numeric scale).
• weekday_alcohol / weekend_alcohol: Alcohol consumption levels on weekdays and weekends.
• health: Current health status (1–5 scale).
• absences: Number of school absences.
• grade_1 / grade_2 / final_grade: First and second period grades and final academic performance.

Distribution

Usage

• Academic Performance Prediction: Predict final grades based on behavioral and background features.
• Feature Importance Analysis: Identify key influences on student success.
• Sociological Insights: Understand the impact of family, relationship, and lifestyle factors on education.
• Model Training: Suitable for classification, regression, and clustering tasks in educational data mining.

Coverage

License

Who Can Use It

• Educational Researchers: For testing interventions and identifying risk factors.
• Data Scientists and ML Practitioners: For building predictive models in education.
• Instructors and Students: For coursework in data analysis, machine learning, and statistics.