https://github.com/easy-ai-tech/student-score-analysis

A data-driven student performance analysis project using UCI dataset (396 students, 33 features). Implements machine learning models (K-means, PCA, Decision Tree, Random Forest, Linear Regression) to analyze academic patterns and predict student scores based on lifestyle, health, and study habits.
https://github.com/easy-ai-tech/student-score-analysis

clustering clustering-algorithm decision-trees feature-engineering learning-management-system linear-regression machine-learning machine-learning-algorithms matplotlib numpy pandas pca pickle prediction prediction-algorithm scikit-learn score seaborn student

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A data-driven student performance analysis project using UCI dataset (396 students, 33 features). Implements machine learning models (K-means, PCA, Decision Tree, Random Forest, Linear Regression) to analyze academic patterns and predict student scores based on lifestyle, health, and study habits.

• Host: GitHub
• URL: https://github.com/easy-ai-tech/student-score-analysis
• Owner: easy-ai-tech
• Created: 2025-01-31T11:33:00.000Z (3 days ago)
• Default Branch: main
• Last Pushed: 2025-02-01T17:28:43.000Z (2 days ago)
• Last Synced: 2025-02-01T18:30:39.738Z (2 days ago)
• Topics: clustering, clustering-algorithm, decision-trees, feature-engineering, learning-management-system, linear-regression, machine-learning, machine-learning-algorithms, matplotlib, numpy, pandas, pca, pickle, prediction, prediction-algorithm, scikit-learn, score, seaborn, student
• Language: Jupyter Notebook
• Homepage:
• Size: 76.2 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Student Score Analysis and Prediction

A comprehensive data analysis and machine learning project focused on analyzing student performance using the UCI student dataset containing 33 features across 396 instances.

## Dataset Features

The analysis includes key student attributes:

- Academic Performance (Grades)

- Attendance (Absences)

- Health Metrics

- Lifestyle Factors:

  - Daily/Weekly Alcohol Consumption

  - Free Time Management

  - Internet Usage

- Academic Factors:

  - Study Time

  - Travel Time to School

## Technical Stack

### Python Libraries

- NumPy: Numerical computations and array operations

- Pandas: Data manipulation and analysis

- Seaborn: Statistical data visualization

- Matplotlib: Creating static, animated, and interactive visualizations

- Scikit-learn: Machine learning implementations

- Pickle: Model serialization

### Machine Learning Models

- K-means Clustering: Student grouping analysis

- Principal Component Analysis (PCA): Dimensionality reduction

- Decision Tree: Classification and prediction

- Random Forest: Ensemble learning for improved accuracy

- Linear Regression: Score prediction

## Analysis Workflow

1. Data Loading and Preprocessing

2. Exploratory Data Analysis

3. Feature Engineering

4. Model Training and Evaluation

5. Performance Prediction

## Goals

- Analyze factors affecting student performance

- Predict student scores based on various features

- Identify key patterns in student behavior and academic performance

- Generate actionable insights for educational improvement

# Author

[Discord](https://discord.gg/TawJX4ue)

[Email](mailto:[email protected])