📚 Student Feedback Analyzer

A beginner-friendly Python project that collects, stores, and analyzes student feedback using:

• 🗄️ MySQL – for database storage
• 🌐 Streamlit – for interactive UI
• 🤖 Google Gemini AI – for sentiment analysis & automated summaries
• 🎨 WordCloud + Charts – for data visualization

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🚀 Features

• ✅ Feedback Submission – Students can enter name, subject, rating, and comments.

• ✅ Database Integration – Feedback stored securely in a MySQL database.

• ✅ Dashboard:

  • View all feedback in tabular format.

  • See average rating per subject in bar charts.

  • Subject-wise Analysis with:

    • Word Cloud of comments
    • AI-powered Sentiment Analysis (Positive/Negative/Neutral)
    • AI-generated Summary of all feedback

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🏗️ Project Architecture

+----------------+       +------------------+       +-------------------+
|  Streamlit UI  | <-->  |  MySQL Database  | <-->  |   Data Storage    |
+----------------+       +------------------+       +-------------------+
        |
        v
+----------------+       +---------------------------+
|  WordCloud &   |       |   Google Gemini AI API    |
|  Charts        | <-->  | (Sentiment + Summarizer)  |
+----------------+       +---------------------------+

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⚙️ Tech Stack

• Language: Python
• Framework: Streamlit
• Database: MySQL
• Visualization: Matplotlib, WordCloud
• AI: Google Gemini (via Google AI Studio API)

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📂 Project Structure

student-feedback-analyzer/
│
├── app.py               # Main Streamlit app
├── requirements.txt     # Python dependencies
├── .env                 # Environment variables (API key, DB creds)
└── README.md            # Project documentation

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📦 Installation & Setup

1. Clone the repository

git clone https://github.com/arunishrajput/student-feedback-analyzer.git
cd student-feedback-analyzer

2. Install dependencies

pip install -r requirements.txt

requirements.txt should include:

streamlit
mysql-connector-python
pandas
matplotlib
wordcloud
python-dotenv
google-genai

3. Setup MySQL Database

CREATE DATABASE feedback_db;
USE feedback_db;

CREATE TABLE feedback (
    id INT AUTO_INCREMENT PRIMARY KEY,
    student_name VARCHAR(100),
    subject VARCHAR(100),
    rating INT,
    comments TEXT,
    date_submitted TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

4. Configure Environment Variables

Create a .env file in the project root:

# Google Gemini API Key
GEMINI_API_KEY=your_google_ai_studio_api_key_here

# MySQL Config
DB_HOST=localhost
DB_USER=root
DB_PASSWORD=your_mysql_password
DB_NAME=feedback_db

5. Run the app

streamlit run app.py

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🎮 Usage Flow

1. Submit Feedback

   • Enter Name, Subject, Rating, Comments
   • Data saved to MySQL

2. Dashboard

   • View all raw feedback in a table
   • See average ratings (per subject)
   • Choose a subject → WordCloud + Sentiment + AI Summary

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📊 Example Output

• Word Cloud

• Average Ratings

Physics        4.2
Chemistry      3.8
Mathematics    4.5

• Sentiment Counts (Chemistry)

{
    "Positive": 12,
    "Negative": 3,
    "Neutral": 5
}

• AI Summary Example

“Most students appreciated the clarity of explanations. A few suggested more real-world examples. Overall feedback is positive with some scope for improvement.”

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💡 Future Improvements

• 🔐 Add login system (students/teachers)
• 📤 Export reports (PDF/Excel)
• 📈 Compare subject feedback across semesters
• 🎤 Voice-based feedback submission

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🎤 Demo Script (for presentation)

See 👉 Demo Script Guide (extra file for classroom presentation)

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👨‍💻 Author

• Arunish (CSE Student, 1st Year)