Backend Development 3 min read

Student Training Plan Management System Using Python, Flask, and MySQL

This article introduces a Python‑Flask‑MySQL student training plan management system, detailing its features such as visualized progress, SVD‑based course recommendation, forum discussion, simulated enrollment, project structure, environment setup, step‑by‑step installation, configuration, and usage instructions.

Python Programming Learning Circle
Python Programming Learning Circle
Python Programming Learning Circle
Student Training Plan Management System Using Python, Flask, and MySQL

Project Overview: A student training plan management system built with Python, Flask, and MySQL, offering course recommendation, rating, forum, and simulated course selection modules.

Features:

Visualized training plan showing progress and credits.

Personalized course recommendation using SVD algorithm based on other users' ratings and selections.

Forum for discussion and Q&A.

Simulated course enrollment and withdrawal.

Project Structure:

<code>|-- sql          # SQL scripts
|-- static
|   |-- css
|   |-- images
|   |-- js
|-- templates    # HTML files
|   |-- *.html
|-- utils       # utility functions
|-- config.py
|-- errors.py
|-- main.py</code>

Environment: Python 3.x, MySQL 5.7, Flask 1.0x, NumPy.

Usage Steps:

Download source code.

Install dependencies: pip install Flask<br/>pip install numpy

Initialize database: create database, run schema and insert scripts.

Configure config.py with your MySQL password and database name.

Run the application with python main.py and open localhost:5000 in a browser.

Deployment details are included in the source package.

Several screenshots illustrate the login page, password change, home, plan view, course rating, recommendation, forum, comments, topics, personal center, and admin interfaces.

BackendPythonrecommendationmysqlFlaskSVDweb app
Python Programming Learning Circle
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Python Programming Learning Circle

A global community of Chinese Python developers offering technical articles, columns, original video tutorials, and problem sets. Topics include web full‑stack development, web scraping, data analysis, natural language processing, image processing, machine learning, automated testing, DevOps automation, and big data.

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