Email Spam Classifier

Project Overview

This project is a machine learning–based spam classifier that detects spam emails using NLP techniques. It features a simple GUI for interaction and visual performance metrics (confusion matrices and label distribution plots).

Features

• Cleaned and preprocessed dataset (email.csv)
• Text vectorization using CountVectorizer and TF-IDF
• Trained classification models (e.g., Naive Bayes, Logistic Regression)
• GUI interface for real-time spam prediction (spam_gui.py)
• Visualizations including confusion matrices and label distribution plots

Project Structure

ds_project/
├── email.csv                      # Labeled email dataset taken from Kaggle
├── spam_gui.py                    # GUI for spam prediction
├── try5_guiver.py                 # Model training and grid search
├── confusion_matrices/           # Saved confusion matrix images
├── all_confusion_matrices_grid.png
├── label_distribution.png
├── README.docx                    # This file

How to Run

1. Clone the repository:

   git clone https://github.com/HafsaFatima26/Email-Spam-Classifier.git
   cd Email-Spam-Classifier

2. Install Dependencies pip install -r requirements.txt
3. Run the GUI python spam_gui.py

Results

• Accuracy: ~XX % (based on the best model)
• Precision / Recall: Available in the saved confusion-matrix images
• Visual Plots: Confusion matrices and label-distribution charts

Tech Stack

• Python: Pandas, NumPy, scikit-learn, Matplotlib
• Tkinter: Graphical User Interface
• Seaborn: Advanced plotting and visualization

Contributors

• Hafsa Fatima
• Syeda Haneesh
• Waniya Syed