/Academic-Performance-Forecasting-System

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Academic Performance Forecasting System

This project aims to forecast academic performance based on student demographic and environmental factors. The system utilizes a machine learning model to predict student grades, allowing educational institutions to identify at-risk students and provide targeted interventions.

Dataset

The dataset used for this project is student_data.csv, which contains information about students' demographics, family background, study habits, and academic performance. The dataset includes the following columns:

  • school: Student's school (binary: 'GP' for Gabriel Pereira or 'MS' for Mousinho da Silveira)
  • sex: Student's gender (binary: 'F' for female or 'M' for male)
  • age: Student's age (numeric: from 15 to 22)
  • address: Student's home address type (binary: 'U' for urban or 'R' for rural)
  • famsize: Family size (binary: 'LE3' for less or equal to 3 or 'GT3' for greater than 3)
  • Pstatus: Parent's cohabitation status (binary: 'T' for living together or 'A' for apart)
  • Medu: Mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
  • Fedu: Father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
  • Mjob: Mother's job
  • Fjob: Father's job
  • reason: Reason to choose this school
  • guardian: Student's guardian
  • traveltime: Home to school travel time
  • studytime: Weekly study time
  • failures: Number of past class failures
  • schoolsup: Extra educational support
  • famsup: Family educational support
  • paid: Extra paid classes within the course subject
  • activities: Extra-curricular activities
  • nursery: Attended nursery school
  • higher: Wants to take higher education
  • internet: Internet access at home
  • romantic: In a romantic relationship
  • famrel: Quality of family relationships
  • freetime: Free time after school
  • goout: Going out with friends
  • Dalc: Workday alcohol consumption
  • Walc: Weekend alcohol consumption
  • health: Current health status
  • absences: Number of school absences
  • G1: First period grade (numeric: from 0 to 20)
  • G2: Second period grade (numeric: from 0 to 20)
  • G3: Final grade (numeric: from 0 to 20, output target)

Usage

  1. Clone the Repository:

    git clone https://github.com/am-nimrah/Academic-Performance-Forecasting-System.git

  2. Install Dependencies:

    pip install -r requirements.txt

  3. Run the Code:

    python academic_performance_forecasting.py

Results

The model achieved a Mean Squared Error (MSE) of approximately 3.49 when evaluated on the test set, indicating good performance in predicting student grades.

Future Improvements

  • Feature engineering to extract more meaningful information from the existing features.
  • Experiment with different machine learning algorithms and hyperparameter tuning to improve prediction accuracy.
  • Incorporate additional datasets or features that may influence academic performance, such as socio-economic factors or extracurricular activities.

License

This project is licensed under the MIT License - see the LICENSE file for details.