Student Analytics Dashboard API

A full-stack web application for uploading student grade data (CSV) and performing automated analytics — averages, trends, per-student breakdowns, class rankings, and linear regression predictions.

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Technologies Used

Backend

• Python, FastAPI, Uvicorn
• SQLAlchemy (async), asyncpg
• PostgreSQL (JSONB storage)
• Pandas, NumPy, scikit-learn
• Pydantic, python-dotenv

Frontend

• React (Vite)
• Recharts
• Axios

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Setup & Installation

Prerequisites

• Python 3.12+
• Node.js
• PostgreSQL

Clone the repo

git clone https://github.com/DaringSVM20/student-analytics-dashboard.git
cd student-analytics-dashboard

Sample Data

A sample dataset is included at the root of the repo. Use test_data.csv to test the upload and analytics features immediately after setup.

Backend

cd backend
python -m venv venv
venv\Scripts\Activate.ps1
pip install -r requirements.txt

Create a .env file in backend/:

DATABASE_URL=postgresql+asyncpg://postgres:YOUR_PASSWORD@127.0.0.1:5432/student_analytics

Create the database:

psql -U postgres -c "CREATE DATABASE student_analytics;"

Run the backend:

uvicorn main:app --reload

API at http://127.0.0.1:8000 — Swagger docs at http://127.0.0.1:8000/docs.

Frontend

cd frontend
npm install
npm run dev

Frontend at http://localhost:5173.

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Project Structure

student-analytics/
├── backend/
│   ├── main.py              # FastAPI app, CORS, lifespan
│   ├── database.py          # Async SQLAlchemy engine and session
│   ├── models.py            # Dataset DB model
│   ├── schemas.py           # Pydantic request/response schemas
│   ├── test_data.csv        # Sample dataset for testing
│   ├── routers/
│   │   └── datasets.py      # All API endpoints
│   └── services/
│       └── analytics.py     # Averages, trends, predictions, rankings
└── frontend/
    └── src/
        ├── App.jsx
        ├── components/
        │   ├── UploadDataset.jsx
        │   ├── DatasetList.jsx
        │   └── AnalyticsDashboard.jsx
        └── services/
            └── api.js

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Preview of Dashboard

frontend.-.Google.Chrome.2026-06-12.21-45-57.mp4

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What I Learned

• Setting up an async Python backend with FastAPI and SQLAlchemy
• Connecting to PostgreSQL via asyncpg and handling Windows-specific quirks (127.0.0.1 vs localhost, URL-encoding special characters in passwords)
• Storing flexible CSV data as JSONB in PostgreSQL without rigid schemas
• Using Pandas for data processing and scikit-learn for linear regression predictions
• Building a React frontend with Recharts for bar, line, and radar visualizations
• FastAPI dependency injection for database sessions
• Pydantic schemas for separating API response shapes from DB models
• Managing virtual environments and secrets securely with dotenv

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MVP Status & Roadmap

Current MVP — Complete

• CSV upload and storage
• Subject averages (bar chart)
• Score trends across students (line chart)
• Per-student breakdown with rankings and pass/fail status
• Student radar profile chart
• Linear regression predictions per subject
• Delete datasets

Future Roadmap

• User authentication and per-user dataset isolation
• Support for varied CSV formats with auto column mapping
• Advanced ML models (classification, clustering)
• Export analytics as PDF
• Real-time data updates
• LMS integration