A Python-based project for analyzing and visualizing student academic performance, including subject-wise trends, pass/fail rates, and performance distribution.
This project focuses on analyzing students' overall performance including exam marks, co-curricular activities, and other performance indicators. Using Python-based data analysis, it extracts meaningful insights and presents them visually to help educators and students identify strengths, weaknesses, and opportunities for improvement.
- Python (Pandas, NumPy, Scikit-learn)
- Visualization (Matplotlib, Seaborn, Plotly)
- Jupyter Notebook for analysis workflow
- Data Cleaning & Preprocessing (handling missing values, normalization)
- Marks distribution analysis (subject-wise and overall)
- Pass/Fail ratio and class average
- Activity-based performance evaluation
- Correlation between marks and activities
- Identification of toppers and weak performers
- Visualizations for easy interpretation (bar graphs, heatmaps, pie charts)
- The dataset includes student marks, subject scores, and activity participation records.
- For demo purposes, a sample CSV dataset is provided in the
data/folder. - Example columns:
Student_IDNameMathsScienceEnglishActivities_ScoreOverall_Score
-Run Jupyter Notebook:
-jupyter notebook
-Open notebooks/02_exploratory_analysis.ipynb to explore the analysis.
-Some example outputs:
-📊 Bar Chart: Subject-wise performance distribution
-🔥 Heatmap: Correlation between marks and activities
-🥇 Leaderboard: Top 5 performers
-📝 Summary Report: Class average, median, pass/fail ratio
Machine Learning models for predicting student performance
Automated personalized student report cards
Integration with a web dashboard (Streamlit/Dash)
Contributions are always welcome!
Fork this repo
Create a new branch (feature-xyz)
Commit changes
Open a Pull Request
📜 License
This project is licensed under the MIT License.