Prediction of students' academic status

🏫 Student Academic Status Prediction

In this problem, you must implement a classification model capable of predicting a student's academic status (Target) using a provided dataset. The dataset is organized in a CSV file, and the model's performance will be evaluated using the F1-score.

🔹 Dataset

The dataset contains the following columns:

• SampleID: Unique identifier for each student
• Marital status: Student’s marital status (Single, Married, Other)
• Application mode: Application method (Online, In person, Other)
• Application order: Order of application (numeric)
• Course: The course the student enrolled in
• Daytime/evening attendance: Attendance type (Daytime/Evening)
• Previous qualification: Previous education level (High School, College)
• Nacionality: Student's nationality
• Mother's qualification: Mother's education level
• Father's qualification: Father's education level
• Mother's occupation: Mother's occupation
• Father's occupation: Father's occupation
• Displaced: Whether the student is displaced/away from home (Yes/No)
• Educational special needs: Special educational needs (Yes/No)
• Debtor: Whether the student has unpaid fees (Yes/No)
• Tuition fees up to date: Whether tuition fees are up to date (Yes/No)
• Gender: Student’s gender (Male/Female)
• Scholarship holder: Whether the student receives a scholarship (Yes/No)
• Age at enrollment: Age when enrolling
• International: International student status (Yes/No)
• Curricular units 1st sem (credited): Credited units in the first semester
• Curricular units 1st sem (enrolled): Enrolled units in the first semester
• Curricular units 1st sem (evaluations): Evaluations completed in the first semester
• Curricular units 1st sem (approved): Approved units in the first semester
• Curricular units 1st sem (grade): Grades obtained in the first semester
• Curricular units 1st sem (without evaluations): Units without evaluations in the first semester
• Curricular units 2nd sem (credited): Credited units in the second semester
• Curricular units 2nd sem (enrolled): Enrolled units in the second semester
• Curricular units 2nd sem (evaluations): Evaluations completed in the second semester
• Curricular units 2nd sem (approved): Approved units in the second semester
• Curricular units 2nd sem (grade): Grades obtained in the second semester
• Curricular units 2nd sem (without evaluations): Units without evaluations in the second semester
• Unemployment rate: Unemployment rate in the student’s region
• Inflation rate: Inflation rate
• GDP: Gross Domestic Product
• Target: The target variable, representing the student's academic status (Dropout, Enrolled, Graduate)

📊 Dataset Notes

• Target field: Target
• The model must be trained on train.csv and evaluated on test.csv.

⚙️ Evaluation Criteria

• Performance Metric: F1-score (the higher, the better)

📨 Submission Format

The submission file must be a CSV with exactly two columns:

🔹 Example

Important: Each row in the CSV represents the prediction for one student in the test set.

📝 Note

The score is based on the F1-score obtained by the model on the test set.
To achieve maximum points, the F1-score must be at least 0.75, while values below 0.5 receive 0 points.
Intermediate values are scaled proportionally.

🗂️ Useful Resources

• Starter Kit – a template to help you start the task