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.
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 jobFjob
: Father's jobreason
: Reason to choose this schoolguardian
: Student's guardiantraveltime
: Home to school travel timestudytime
: Weekly study timefailures
: Number of past class failuresschoolsup
: Extra educational supportfamsup
: Family educational supportpaid
: Extra paid classes within the course subjectactivities
: Extra-curricular activitiesnursery
: Attended nursery schoolhigher
: Wants to take higher educationinternet
: Internet access at homeromantic
: In a romantic relationshipfamrel
: Quality of family relationshipsfreetime
: Free time after schoolgoout
: Going out with friendsDalc
: Workday alcohol consumptionWalc
: Weekend alcohol consumptionhealth
: Current health statusabsences
: Number of school absencesG1
: 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)
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Clone the Repository:
git clone https://github.com/am-nimrah/Academic-Performance-Forecasting-System.git
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Install Dependencies:
pip install -r requirements.txt
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Run the Code:
python academic_performance_forecasting.py
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.
- 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.
This project is licensed under the MIT License - see the LICENSE file for details.