Capstone Project 2: Student Report Card Generator in Python

In this final capstone project of the intermediate curriculum, we unite all major technical competencies acquired across this course—Data Structures, File Handling with JSON & CSV, Object-Oriented Design, Exception Safety, Statistical Metrics, and Visual Chart Generation with Matplotlib—to build an automated Academic Report Card & Performance Analytics Generator.

1. System Architecture & Objectives

Our system automates academic transcript generation for schools and universities:

1. 1
   Data Ingestion: Parses student profiles, subjects, and examination marks from structured JSON or CSV datasets.
2. 2
   Academic Analytics Engine:

• Computes Total Marks, Percentage, Weighted GPA (on a 10.0 scale), and Letter Grades (A+, A, B, C, F).
• Calculates Class Averages per subject for benchmark comparison.

1. 1
   Multi-Format Publishing:

• Generates a formatted ASCII/Text Official Academic Transcript.
• Generates a visual performance comparison chart (report_card_<roll_no>.png) plotting individual student scores alongside the class average using Matplotlib.

2. Complete Project Implementation

3. Sample Execution Output

Multiple Choice Questions

1. In this project, which Python built-in function ensures that a student fails if ANY subject score is below 40?

A. any() B. all(score >= 40.0 for score in marks.values()) C. min(marks.values()) D. filter() Answer: B Explanation: all() returns True only if every single condition in the iterable evaluates to True; if any subject score is under 40, is_passed evaluates to False.

2. How does the application calculate the class average benchmark for each academic subject?

A. By asking teachers for an estimate B. By summing subject marks across all students and dividing by the total student count C. By selecting the highest mark D. By generating random numbers Answer: B Explanation: _calculate_class_averages() sums the scores per subject across the student objects and divides by student count.

3. Which Matplotlib method adds the horizontal dashed line representing the 40% passing threshold?

A. ax.draw_horizontal() B. ax.axhline(40.0, linestyle="--") C. ax.line_x(40) D. ax.threshold() Answer: B Explanation: ax.axhline(y) plots a horizontal reference line spanning the full width of the axes.

4. What is the purpose of os.makedirs(output_dir, exist_ok=True) before saving the chart image?

A. To format the file as PDF B. To guarantee that the destination directory exists without raising an error if it was already created C. To delete old report cards D. To clear system memory Answer: B Explanation: os.makedirs(..., exist_ok=True) safely creates nested destination directories if they don't exist, preventing FileNotFoundError.

5. Why is plt.close(fig) invoked after saving each student's chart?

A. To prevent memory leaks and figure accumulation in RAM during batch processing B. To shut down the Python interpreter C. To encrypt the PNG file D. To commit the database transaction Answer: A Explanation: Closing figures in batch image generators releases canvas memory buffers, preventing excessive RAM consumption.