Student-Result-Visualization

This project is part of the online internship offered by GUVI - HCL. The goal is to analyze student academic performance data and create an interactive dashboard to visualize key insights like average scores, pass percentages, grade distribution, and overall trends.(guvi project 1)

Project Structure

student-result-visualization/ │ ├── student_performance_dataset.csv # Raw dataset ├── cleaned_final_student_data.csv # Cleaned dataset ├── student_performance_analysis.ipynb # Python (Colab) preprocessing code ├── student_result_dashboard.twbx # Tableau dashboard (packaged workbook) ├── requirements.txt # Python library dependencies └── README.md # Project description

Key Features of the Dashboard

• Subject-wise average score comparison
• Term-wise score progression for selected students
• Pass percentage and student count
• Grade distribution across terms
• Histograms for pass/fail distribution in Term 1, Term 2, and Overall
• Heatmap for correlation between subjects

Tools & Technologies

• Python (Pandas, NumPy, etc.) for data cleaning
• Google Colab for preprocessing
• Tableau for interactive dashboard visualization
• GitHub for version control

Data Preprocessing Steps

1. Cleaned missing and inconsistent values
2. Standardized grades and marks
3. Added calculated columns (like PassFlag, Average, Term-wise Total)
4. Exported cleaned data for visualization

Sample Insights

• Overall student performance trends
• Subjects with highest and lowest average scores
• Comparison between Term 1 and Term 2 results
• Percentage of students passing per subject

How to Use

1. Open student_performance_analysis.ipynb in Google Colab to view data preprocessing.
2. Open student_result_dashboard.twbx in Tableau Desktop/Public to interact with the dashboard.
3. Tableau Dashboard File: Book1.twb
   Open this in Tableau Public to interact with the dashboard.

Feel free to star the repo if you find it useful or want to reuse the dashboard template.