Student Feedback System using Python and Machine Learning

In today’s academic environment, understanding student sentiments is crucial for educational institutions aiming to improve teaching methods, faculty performance, and overall learning experiences. The Student Feedback System using Python and Machine Learning is a robust, web-based solution built to capture and analyze student feedback through intelligent sentiment analysis.

📝 Project Overview

This project enables students to submit feedback anonymously, which is then processed using machine learning techniques to classify the sentiment as Positive, Neutral, or Negative. The system provides role-based dashboards for students, faculty, and administrators to review the sentiment trends and feedback summaries through engaging visualizations.

The backend is powered by Python and Flask, while SQLite is used for data storage. The feedback sentiment is analyzed using pre-trained ML models like Naive Bayes and SVM. Visualization is done using Matplotlib, and the UI is built with HTML, CSS, and Bootstrap.

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⚙ Key Features

• Anonymous Feedback Submission
  Students can submit their feedback without revealing their identities.
• Sentiment Analysis Using ML Models
  Feedback is processed using pre-trained models (Naive Bayes, SVM) trained on labeled data.
• Role-Based Authentication
  • Student: Submit feedback
  • Admin: Manage users and view complete feedback history
• Interactive Dashboards
  Admin users can access dashboards with:
  • Sentiment breakdowns (pie charts and graphs)
  • Total feedback statistics
• Structured Storage
  All user and feedback data is stored in a SQLite database.
• Modular Codebase
  The application follows a clean separation of concerns:
  • Models (ML)
  • UI templates
  • Database and logic

💻 Technologies Used

🚀 Running the Application

To start the server, simply run:

python server.py

This will launch the web application at http://127.0.0.1:5000/.

🔐 User Roles and Access

1. Login

Users navigate to the /login endpoint and are directed to their respective dashboards based on their role:

• Student
• Admin

2. Feedback Submission

Students can submit feedback through /feedback, which includes:

• Feedback text
• Teacher/department selection

Once submitted:

• The text is analyzed using a pre-trained ML model.
• Sentiment is classified as:
  • Positive (1)
  • Neutral (0)
  • Negative (-1)
• Data is stored in a CSV file (feedback_data table).

3. Admin Dashboards

Admins can:

• View the total number of feedback submissions
• Analyze sentiment distribution through pie charts
• Read feedback entries along with sentiment scores

Visualizations are dynamically generated using Matplotlib or equivalent.

🤖 ML Model Integration

ML models are stored in the models/ folder and used within server.py. For example:

MultinomialNB_stemmed_classifier.pkl

These are loaded to analyze incoming feedback automatically.