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https://github.com/ksmooi/mscs_ml_student_performance
This project predicts students' final grades (G3) using machine learning based on their demographics and background.
https://github.com/ksmooi/mscs_ml_student_performance
classification ensemble machine-learning sklearn
Last synced: about 1 month ago
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This project predicts students' final grades (G3) using machine learning based on their demographics and background.
- Host: GitHub
- URL: https://github.com/ksmooi/mscs_ml_student_performance
- Owner: ksmooi
- Created: 2024-07-22T12:44:20.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-23T13:16:08.000Z (4 months ago)
- Last Synced: 2024-09-28T05:09:23.080Z (about 2 months ago)
- Topics: classification, ensemble, machine-learning, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 6.81 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Predicting Student Performance: An Analytical Approach
This repository contains the code and analysis for predicting student performance based on various factors using multiple machine learning algorithms.
## [Project Overview](notebooks/student_performance_insights.ipynb)
The objective of this project is to predict students' final grades (G3) based on their demographic information, family background, educational support, extracurricular activities, and personal attributes.
## [Dataset](notebooks/student_performance_insights.ipynb)
The dataset includes information about students such as:
- Demographics: school, sex, age, address
- Family Background: famsize, Pstatus, Medu, Fedu, Mjob, Fjob, guardian
- Educational Support: traveltime, studytime, failures, schoolsup, famsup, paid, activities, nursery, higher, internet, romantic
- Personal Information: famrel, freetime, goout, Dalc, Walc, health, absences
- Grades: G1, G2, G3
## [Exploratory Data Analysis (EDA)](notebooks/student_performance_insights.ipynb)
The EDA involves:
- Visualizing the distribution of numerical features
- Visualizing categorical features
- Analyzing feature relationships with the target variable (G3)
## [Model Selection](notebooks/student_performance_insights.ipynb)
We evaluated several machine learning algorithms to find the best model for predicting student performance:
1. Logistic Regression
2. Decision Tree Classifier
3. Support Vector Machine
4. Random Forest Classifier
5. AdaBoost Classifier
6. Gradient Boosting Classifier
7. K Neighbors Classifier
8. Gaussian Naive Bayes
## [StudentPerformanceAnalyzer Class](notebooks/student_performance_insights.ipynb)
This class is designed to:
- Load the dataset and initialize models
- Preprocess data by encoding, splitting into train/test, and standardizing features
- Train various classifiers and evaluate their metrics
- Compare model performance
- Plot feature importances for selected models
## [Model Training and Evaluation](notebooks/student_performance_insights.ipynb)
Each model was trained on the training data and evaluated on the testing data. Evaluation metrics include accuracy, precision, recall, and F1 score.
## [Analysis Results](notebooks/student_performance_insights.ipynb)
### Model Performance
- **Gradient Boosting Classifier**: Highest accuracy (0.515385)
- **Random Forest Classifier**: Moderate accuracy (0.446154)
- **Decision Tree Classifier**: Lower accuracy (0.384615)
- **Other Models**: Showed lower performance
### Feature Importance
- **Gradient Boosting Classifier**: Top features are G2, G1, absences, age, and free time
- **Random Forest and Decision Tree Classifiers**: Similar top features, emphasizing G1, G2, absences, free time, and parental education
## [Insights and Recommendations](notebooks/student_performance_insights.ipynb)
1. **Importance of Continuous Assessment**:
- G1 and G2 are crucial for predicting G3.
- Implement regular assessments and feedback.
2. **Attendance and Engagement**:
- Absences and free time impact performance.
- Improve attendance and participation programs.
3. **Family Background**:
- Parental education affects performance.
- Engage families and provide support for equal opportunities.
4. **Holistic Approach**:
- Address academic, social, and emotional needs for student success.
## [References](notebooks/student_performance_insights.ipynb)
- Project: [Jupyter Notebook](notebooks/student_performance_insights.ipynb)
- Dataset: [Kaggle: Student Performance Data Set](https://www.kaggle.com/datasets/larsen0966/student-performance-data-set/data)
- Additional Analysis: [Kaggle Notebook: Annual Grades Average 5-Level Classification](https://www.kaggle.com/code/sharonyaroshetsky/annual-grades-average-5-level-classification#Outliers-Detection)