- Developed a data-driven analysis system for evaluating student performance based on attendance, theory, and practical marks.
- Designed customized insights and recommendations to help educators identify patterns and improve academic outcomes.
- Implemented a Streamlit-based interactive dashboard to visualize and interpret key trends.
- Gathered raw student performance data from institutional records.
- Cleaned and structured the dataset using Pandas & NumPy.
- Handled missing values, normalized data, and ensured consistency for accurate analysis.
- Explored data distribution using Matplotlib & Seaborn.
- Applied correlation analysis to identify relationships between attendance, theory, and practical scores.
- Built custom graphs and heatmaps for deeper insight into student performance trends.
- Utilized Scikit-Learn to explore regression models predicting student success.
- Analyzed how attendance impacts academic performance through statistical measures.
- Generated automated recommendations for students and educators based on findings.
- Developed an interactive Streamlit web app for educators to input student data and receive real-time insights.
- Hosted the project on GitHub for version control and collaboration.
- Python 🐍 – Core programming language
- Streamlit 🎛️ – UI for data interaction
- NumPy 🔢 & Pandas 📊 – Data handling
- Pyplot 📈 & Seaborn 🎨 – Visualization and interactive Graph
- Scikit-Learn 🤖 & SciPy 🔬 – Machine learning & statistical modeling
- io & PIL 🖼️ – File handling & image processing
- VS Code 📝 & Jupyter Notebook 📓 – IDEs for coding & analysis
- GitHub 🗄️ – Version control & repository management
- ChatGPT 📝 – Documentation
- Dev S Panchal
- Shail K Patel