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README.md

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+
 ---
 license: mit
+tags:
+  - phishing-detection
+  - url-classification
 ---
+
+# Overview
+
+The objective of the project is to classify if an URL is phishing or not. This model repo contains the required encoders (for url,dom and tld), scaler (for digit_cnt and is_https) and the trained model (RandomForest Classifier).
+
+# Dataset & Attribution
+This project uses the URL-Phish dataset. The dataset was obtained from Kaggle, where it is available as [Phishing URL Detection (111K URLs, 22 Features)](https://www.kaggle.com/datasets/sahandnamvar/phishing-url-detection-111k-urls-22-features).
+
+The dataset is licensed under **[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)**, 
+which permits sharing, redistribution, and adaptation with appropriate credit.
+
+**Dataset citation**<br>
+>Dam Minh, Linh; Tran Cong, Hung (2025).<br>
+>URL-Phish: A Feature-Engineered Dataset for Phishing Detection.<br>
+>Mendeley Data, V1.<br>
+>DOI: https://doi.org/10.17632/65z9twcx3r.1<br>
+
+**Original data sources referenced by the dataset authors**<br>
+>PhishTank – Community-driven phishing URL repository<br>
+>Research Organization Registry (ROR) dataset – Source of trusted benign domain URLs<br>
+
+**Paper citation**<br>
+>Dam Minh Linh, Tran Cong Hung, <br>
+>A feature-engineered dataset of benign and phishing URLs for machine learning and large language models evaluation,<br>
+>Data in Brief,<br>
+>Volume 63,<br>
+>2025,<br>
+>112162,<br>
+>ISSN 2352-3409,<br>
+>https://doi.org/10.1016/j.dib.2025.112162.
+
+**Modifications:** <br>
+The following preprocessing was applied to the original dataset:
+- Duplicate rows and null/missing values were checked for and removed, if present
+- Feature scaling applied to selected numeric features
+- TF-IDF encoding applied to selected URL/text-derived feature(s)
+- Data split into train / validation / test sets
+
+**Feature usage:** <br>
+The final model was trained using a selected subset 
+of the features; the remaining 
+features were excluded at training time via feature selection, not by removing them from 
+the stored datasets.
+
+# License
+- **Code**: MIT License — see `LICENSE`
+- **Data**: Raw and processed datasets are redistributed under **Creative Commons Attribution 4.0 International (CC BY 4.0) license**, consistent with the original dataset's license (see Dataset & Attribution above).
+- **Model & preprocessors**: MIT License — trained artifacts are provided under the same 
+  terms as the codebase. Model trained on **Creative Commons Attribution 4.0 International (CC BY 4.0) license** data; see Dataset & Attribution section for details.