Feedback Analysis Classification

This project focuses on classifying feedback in college teaching and education using both Machine Learning and Deep Learning models.

Features

• Preprocessing

  • Stopword removal, tokenization, lemmatization, normalization.
  • Removal of digits, special characters, and punctuation.

• Feature Extraction

  • TF-IDF for Machine Learning models.
  • FastText embeddings for Deep Learning models.

• Model Training & Evaluation

  • Machine Learning Models: SVM, Decision Tree, Random Forest.
  • Deep Learning Models: GRU, Bi-GRU (trained for 100 epochs).
  • Performance Metrics: Classification Report, Confusion Matrix, AUC-ROC Curve.
  • Visualizations: Accuracy & Loss Graphs over epochs.

Dataset

• The dataset contains two columns:
  • comment: Textual feedback from students.
  • quality: Labels representing the quality of feedback (Awesome, Good, Average, Poor, Awful).

Installation

1. Clone the repository:

   git clone https://github.com/your-username/Feedback-Analysis.git
   cd Feedback-Analysis

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

• Run the Jupyter Notebook Feedback_Analysis_Classification.ipynb to train and evaluate models.

Results

• The notebook computes classification reports, confusion matrices, and AUC-ROC curves.
• Accuracy and loss graphs are generated for Deep Learning models.

Contribution

Feel free to contribute to this project by submitting issues or pull requests.

License

This project is licensed under the MIT License.