学生就业预测系统：基于 Flask 和机器学习的智能化就业指导平台

This project is a student employment prediction system built using Flask framework and machine learning technology, with MySQL for data storage. It analyzes multi-dimensional student data (academic performance, skills, internships, etc.) to predict job offer probability, providing personalized guidance for students and data support for school employment management departments. Key technologies include Flask, scikit-learn, pandas, MySQL, and Bootstrap.

Background

College graduate employment is a focus of education and society. With enrollment expansion and fierce competition, traditional experience-based guidance lacks data support and personalization. ML offers a solution via historical data analysis.

Core Needs

• Personalized employment probability prediction
• Identification of key employment-influencing factors
• Improvement suggestions for high-risk students
• Data management (entry, query, statistics)
• Visualization of results and insights

Architecture

Three-layer Web app structure:

1. Presentation layer: Flask template-rendered Web interface
2. Business logic layer: Flask routes/views + ML model integration
3. Data layer: MySQL storage

Tech Stack

• Web: Flask
• ML: scikit-learn
• Data processing: pandas, numpy
• DB: MySQL
• Frontend: HTML5 + Bootstrap
• Visualization: matplotlib

Selection Reasons

Flask integrates well with Python data science tools; MySQL provides reliable storage.

Data Management

Supports CRUD for student info (basic, academic, practice, employment) with single/batch entry.

Prediction Model

• Feature Engineering: Extracts academic (GPA, ranking), skill (certificates), practice (internships), and background (gender, major) features.
• Model Selection: Compares logistic regression, random forest, SVM, gradient boosting tree; uses cross-validation and grid search for optimization.
• Evaluation: Uses accuracy, precision/recall, F1-score, ROC-AUC, confusion matrix.

Prediction Service

Steps: Input → feature encoding/standardization → model prediction → return probability + suggestions.

Visualization

Displays employment trends, major comparisons, feature importance, prediction distribution, and student radar charts.

Student Side

• Self-assessment of employment competitiveness
• Targeted improvement suggestions
• Reasonable goal setting

School Side

• Early warning for high-risk students
• Optimized resource allocation
• Policy effect evaluation
• Employment quality reports

Research Value

• Identify key employment factors
• Analyze major/background differences
• Support education reform

Current Limitations

• Small dataset (limited generalization)
• Simple features (no soft skills/character traits)
• No real-time data updates
• Predictions are reference-only

Future Directions

• Expand data sources (online learning, social data)
• Upgrade models (deep learning)
• Add real-time prediction
• Enhance model explainability
• Develop mobile app

Environment Preparation

1. Install Python 3.8+
2. Install MySQL
3. Install dependencies: pip install -r requirements.txt
4. Configure DB connection

Initialization

• Create DB: python init_db.py
• Train model: python ml/train.py
• Start app: python app.py

Access

Visit http://localhost:5000 in browser.

This project demonstrates integrating ML into education via Flask. It provides an end-to-end reference for data science/Web dev learners. For schools, it enables data-driven precise employment guidance. As an open-source project, it welcomes community contributions to advance educational AI tools.