Academic Performance Analysis

A Streamlit-based web application designed to analyze and visualize academic performance data from a college grades dataset (grades.csv). This project provides insights into cumulative GPA, semester-wise GPA trends, grade distribution, and course details, with a bilingual interface (Indonesian/English) for enhanced accessibility. Built with Python, Pandas, and Plotly, it showcases data processing, interactive visualizations, and robust error handling, making it a strong addition to a data science portfolio.

Features

• Cumulative GPA and Total Credits: Displays overall academic performance with cumulative GPA and total credits earned.
• Semester-wise GPA Bar Chart: Visualizes GPA per semester with hover data for total credits, using Plotly for interactivity.
• Grade Distribution Pie Chart: Shows the proportion of grades achieved (A, AB, B, BC) from the dataset, with a note clarifying that only achieved grades are displayed.
• Course Tables: Interactive tables listing courses per semester and per grade, with expandable sections for detailed views.
• Bilingual Interface: Supports Indonesian and English languages for labels and messages, improving usability.
• Data Validation: Includes error handling for missing files, invalid grades (outside A, AB, B, BC, C, D, E), and non-numeric credits, ensuring robustness.

Technologies Used

• Python 3.8+
• Streamlit: For building the interactive web application.
• Pandas: For data processing and analysis.
• Plotly Express: For creating interactive visualizations.
• CSV: Input data stored in grades.csv.

Dataset

The grades.csv file contains academic data with the following columns:

• Semester: The semester number (1–8).
• Kode Mata Kuliah: Course code.
• Mata Kuliah: Course name (Indonesian).
• Nama Mata Kuliah B. Inggris: Course name (English).
• SKS: Credits earned for the course.
• Nilai: Grade received (A, AB, B, BC).

Installation and Setup

1. Clone the repository:

   git clone https://github.com/arianz/academic-performance-analysis.git

2. Navigate to the project directory:

   cd academic-performance-analysis

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run app.py

Requirements

See requirements.txt for dependencies:

streamlit==1.22.0
pandas==2.2.3
plotly==5.14.1

Screenshots

GPA Bar Chart	Grade Distribution Pie Chart