🛡️ PHISHING-URL-DETECTION-USING-MACHINE-LEARNING

🔗 Table of Contents

• 📍 Overview
• 👾 Features
• 🚀 Getting Started
  • ☑️ Prerequisites
  • ⚙️ Installation
  • 🤖 Usage

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📍 Overview

This project focuses on identifying phishing URLs using machine learning algorithms. It extracts key lexical, domain-based, and technical features from URLs to distinguish between legitimate and phishing websites.

A variety of classification algorithms are implemented and evaluated, including Random Forest, Decision Tree, XGBoost, SVM, and Logistic Regression.

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👾 Features

• 🔍 Feature extraction from URLs
• 🧠 Multiple machine learning models implemented
• 📈 Model evaluation using Accuracy, Precision, Recall, and F1 Score
• 📊 Confusion matrix and classification report visualization
• 🧪 Train-test data split with reproducible results
• ✅ Supports binary classification (Phishing / Legitimate)

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🚀 Getting Started

Follow these instructions to get a copy of the project up and running on your local machine.

☑️ Prerequisites

• Python 3.7+
• Jupyter Notebook / VSCode
• Basic knowledge of machine learning

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⚙️ Installation

1. Clone the repository:

   git clone https://github.com/Gauri9977/Phishing-URL-Detection-using-Machine-Learning.git
   cd Phishing-URL-Detection-using-Machine-Learning

2. (Optional) Create and activate a virtual environment:

   • On Windows:

     python -m venv env
     env\Scripts\activate

   • On Linux/Mac:

     python3 -m venv env
     source env/bin/activate

3. Install the required dependencies

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🤖 Usage

1. Launch Jupyter Notebook:

   jupyter notebook

2. Open Phishing_URL_Detection.ipynb and run all the cells sequentially.

3. Explore model performance and tune hyperparameters as needed.

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