URL Website Detection

This project builds a machine learning model to detect whether a given URL is malicious or legitimate.
It is fully implemented in a Google Colab notebook, making it easy to run without any local setup.

📌 Features

• Colab-Ready: Run the notebook directly in Google Colab.
• Dataset Preprocessing: Cleaning, tokenizing, and extracting lexical & statistical features from URLs.
• Feature Engineering: Attributes include:
  • URL length
  • Presence of suspicious keywords
  • Domain structure
  • Character patterns
• Model Training: Experiments with multiple algorithms:
  • Logistic Regression
  • Decision Tree
  • Random Forest
• Evaluation Metrics:
  • Accuracy
  • Precision
  • Recall
  • F1-Score

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📂 Repository Structure

URL_website_detection/
│
├── URL.ipynb               # Main Google Colab notebook
├── dataset.csv             # Input dataset (if included)
├── README.md               # Project documentation

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🚀 How to Run

1. Open the notebook in Google Colab using the badge above.
2. Upload or connect the dataset.
3. Run each cell sequentially to:
   • Preprocess the data
   • Extract features
   • Train models
   • Evaluate results

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Author: Aviral Saini
Project Type: Machine Learning / URL Classification