sabafraz/phishing_mails_detection repository overview

⁠Flask Application

1. Pull the Docker image

docker pull sabafraz/phishing_mails_detection:supervised

2. Run the Flask app

docker run -p 5000:5000 sabafraz/phishing_mails_detection:supervised

⁠Phishing Email Detection using Supervised Machine Learning⁠

⁠Project Overview

This project focuses on building a Supervised Machine Learning model to automatically classify emails as:

• ✅ Legitimate (Safe)
• ⚠️ Phishing (Malicious)

The goal is to help improve email security by detecting phishing attempts efficiently.

⁠Workflow Pipeline

⁠Step 1: Data Loading

• Phishing email dataset Phishing Email Dataset⁠

⁠Step 2: Data Cleaning & Preprocessing

• Remove special characters, punctuation
• Convert text into lowercase
• Remove stopwords
• Tokenization

⁠Step 3: Feature Extraction

To convert text into numerical format:

• TF-IDF Vectorization
• Bag of Words (CountVectorizer)

⁠Step 4: Model Training

Train multiple supervised classifiers such as:

• Logistic Regression
• Naive Bayes
• Random Forest
• Support Vector Machine

⁠Step 5: Model Evaluation

Performance is evaluated using:

• Accuracy Score: 1.00
• Precision: 1.00
• Recall: 1.00
• F1-Score: 1.00
• Confusion Matrix: [[3453 9] [0 4369]]

⁠Step 6: Real Email Prediction

The pipeline supports prediction on real-world email text:

• Input: Email content
• Output: Phishing or Legitimate

⁠Results

The trained model successfully detects phishing emails with strong accuracy and classification performance.

⁠👨‍💻 Author

Developed by Saba Faraz
📧 Email: [email protected]⁠