Student Performance Analyzer (Python & Streamlit)

A professional, interactive Data Science dashboard built using Python, Streamlit, and Pandas. This application allows users to upload student datasets and instantly generate actionable academic insights through automated grading and ranking.

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Project Overview

The Student Performance Analyzer automates the manual process of calculating grades and identifying student performance trends. It demonstrates a complete data science workflow—from dynamic data ingestion and cleaning to interactive visualization.

Key focuses include:

• Dynamic Data Processing using Pandas
• Interactive Dashboarding with Streamlit
• Defensive Programming (input validation for numeric vs. text columns)
• Data-Driven Logic (automated grading using Pandas binning)
• Modern UI/UX Design

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Key Features

• Universal File Support: Upload any CSV or Excel dataset.
• Dynamic Column Mapping: Automatically detects and filters numeric subject columns and text-based name columns.
• Automated Grading: Assigns letter grades (A–F) based on averages using Pandas cut().
• Performance Tracking:
  • Top 10 Performers: Real-time leaderboard using .nlargest().
  • Failing Students Detector: Instantly flags students requiring academic attention.
• Class Metrics: Summary cards for Total Students, Class Average, and Fail Count.

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Tech Stack

• Python
• Streamlit (Web Interface)
• Pandas (Data Manipulation)

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Project Structure

Student-Performance-Analyzer/
│
├── main.py                # Core application logic and UI
├── requirements.txt       # List of dependencies
│
└── data/
    └── student_sample.csv # Sample dataset for testing

• main.py → Handles file uploads, sidebar configuration, and UI rendering
• requirements.txt → Ensures consistent environment setup

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Installation & Usage

1. Clone the repository:

git clone https://github.com/your-username/Student-Performance-Analyzer.git

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

streamlit run main.py

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Purpose of This Project

This project demonstrates my ability to:

• Build robust, end-to-end Data Science dashboards
• Implement flexible logic that handles varied user inputs
• Turn raw data into actionable visual insights
• Apply data cleaning and feature engineering in real-world scenarios

It reflects my focus on building data-driven solutions that provide immediate value to users.

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Developer

Muhammad Bazil