Student Feedback Analysis System Overview

The Student Feedback Analysis System is a Python-based data analysis project designed to collect, clean, analyze, and visualize student feedback data. The system enables users to enter feedback for courses and instructors, perform statistical analysis, and generate insightful visualizations to support educational decision-making.

This project demonstrates practical applications of data analysis, data cleaning, visualization, and reporting using Python.

Features Data Collection Collects student feedback through interactive user input. Stores information such as: Student ID/Name Course Name Instructor Name Course Rating (1–5) Difficulty Level (1–5) Feedback Comments Data Validation Validates numerical inputs. Ensures ratings and difficulty values remain within the specified range. Prevents invalid entries from being recorded. Data Cleaning Removes duplicate feedback records. Removes incomplete records with missing student or course information. Standardizes text formatting for consistency. Statistical Analysis Generates descriptive statistics for ratings and difficulty levels. Calculates: Average rating per course Average difficulty per course Correlation between rating and difficulty Data Visualization

The system automatically generates and saves the following visualizations:

Average Course Ratings (Bar Chart) Course Difficulty Distribution (Box Plot) Rating vs Difficulty Analysis (Scatter Plot) Feedback Comments Word Cloud Automated Insights

Provides summary insights including:

Total feedback entries Average overall rating Average overall difficulty Rating–difficulty relationship Top-rated course Lowest-rated course Most difficult course Instructor-wise average ratings Technologies Used Python Pandas NumPy Matplotlib Seaborn WordCloud Project Structure

Student_Feedback_Analysis/

├── student_feedback_analysis.ipynb

├── requirements.txt

├── README.md

└── charts/

├── avg_course_ratings.png

├── difficulty_distribution.png

├── rating_vs_difficulty.png

└── feedback_wordcloud.png

Installation

Install the required libraries using:

pip install -r requirements.txt

Required libraries:

pandas numpy matplotlib seaborn wordcloud How to Run Open the notebook in Google Colab or Jupyter Notebook. Run all cells. Enter student feedback data when prompted. View the generated analysis and visualizations. Access saved charts in the charts/ directory. Sample Outputs

The project generates:

Course Rating Analysis Difficulty Distribution Analysis Rating vs Difficulty Correlation Visualization Feedback Comment Word Cloud Statistical Summary Report Learning Outcomes

This project demonstrates:

Data Collection and Validation Data Cleaning Techniques Exploratory Data Analysis (EDA) Statistical Analysis Data Visualization Insight Generation using Python Future Enhancements

Potential improvements include:

CSV and Excel data import/export Sentiment analysis of feedback comments Instructor performance dashboard Interactive visualizations Web-based user interface Automated report generation in PDF format

Author

Sriya Patil

Developed as a Python Data Analysis Project for student feedback evaluation and educational insights.

GitHub: https://github.com/SriyaPatil