Student Performance Data Pipeline

A small Python data pipeline that reads student CSV data, classifies grades using OOP, analyzes class-wide performance metrics, and generates automated summary reports (text + Matplotlib charts).

Built as a mini project to practice data pipelines with Pandas, OOP design, and Matplotlib visualization.

Features

• Data Ingestion — Reads and validates CSV datasets using Pandas (handles missing columns, missing values, duplicate rows, and out-of-range scores).
• Grade Classification (OOP) — A Student class and GradeClassifier class automatically compute each student's average and letter grade (A–F), replacing what would otherwise be manual spreadsheet work.
• Performance Analysis — Computes subject averages, overall class average, grade distribution, pass/fail rate, top/bottom performers, and attendance flags.
• Automated Reports — Generates a plain-text summary report plus two Matplotlib charts (grade distribution and subject-wise averages).
• Fully Tested — Unit tests for every module using unittest.

Project Structure

student_performance_pipeline/
│
├── README.md                     # This file
├── requirements.txt               # Python dependencies
├── .gitignore
├── run.py                         # Entry point (CLI)
│
├── data/
│   └── sample_students.csv        # Sample dataset (15 students)
│
├── src/
│   ├── __init__.py
│   ├── data_loader.py             # CSV ingestion + cleaning/validation
│   ├── grade_classifier.py        # Student class + OOP grade classification
│   ├── analyzer.py                # Performance metrics / class statistics
│   ├── report_generator.py        # Text report + Matplotlib charts
│   └── main.py                    # Pipeline orchestration + CLI args
│
├── tests/
│   ├── __init__.py
│   ├── test_data_loader.py
│   ├── test_grade_classifier.py
│   ├── test_analyzer.py
│   └── test_report_generator.py
│
└── output/                        # Generated reports/charts land here
    ├── grade_distribution.png
    ├── subject_averages.png
    └── summary_report.txt

Setup

# 1. Clone the repo
git clone <your-repo-url>
cd student_performance_pipeline

# 2. Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate      # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

Usage

Run the pipeline on the included sample dataset:

python run.py

Or specify a custom input file / output folder:

python run.py --input data/sample_students.csv --output output

This will print a summary to the console and generate the following inside output/:

• summary_report.txt — full text breakdown of class performance
• grade_distribution.png — bar chart of how many students got each grade
• subject_averages.png — bar chart of average score per subject

Expected CSV format

Your input CSV must contain these columns:

Column	Description
student_id	Unique student identifier
name	Student name
math	Math score (0–100)
science	Science score (0–100)
english	English score (0–100)
history	History score (0–100)
attendance_percentage	Attendance percentage (0–100)

Running Tests

Tests are written with unittest and can be run with either unittest or pytest:

# Using unittest
python -m unittest discover tests

# Using pytest
pytest tests/ -v

Grading Logic

Average Score	Grade
85 – 100	A
70 – 84	B
55 – 69	C
40 – 54	D
Below 40	F

Possible Future Improvements

• Support for exporting the summary report as PDF
• A simple web dashboard (Flask/Streamlit) instead of static charts
• Configurable grade bands via a config file instead of hardcoded values
• Support for multiple CSV files / merging datasets over time