🛡️ Phishing Detection using Machine Learning

A machine learning project that detects phishing attempts using features extracted from real-world phishing data. This project combines network security fundamentals with data-driven intelligence to enhance online safety.

🚀 Project Overview

Phishing attacks are one of the most common forms of cyber threats targeting users via email, websites, or messaging platforms. This project aims to develop a supervised machine learning model that can accurately classify URLs/emails as phishing or legitimate, helping reduce the risk of such attacks.

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📂 Dataset

• Source: Custom Dataset
• Format: CSV
• Size: 11,000+ records
• Features:
  • URL length
  • Presence of '@' symbol
  • Number of subdomains
  • HTTPS usage
  • Domain age
  • WHOIS information

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🧠 ML Models Used

• Logistic Regression
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)

Best performing model: [Model Name] with X% Accuracy

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⚙️ Technologies Used

• Python 3.x
• Pandas, NumPy
• Scikit-learn
• Matplotlib / Seaborn
• Jupyter Notebook

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🔍 How It Works

1. Data Preprocessing

   • Clean and format dataset
   • Feature extraction

2. Model Training

   • Split into training & testing sets
   • Apply and compare ML models
   • Evaluate using metrics like accuracy, precision, recall

3. Prediction

   • Use trained model to classify new data as phishing or legitimate

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📁 Project Structure