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What the 96.70% result measures
In a September 29, 2026 DEV Community post, edited October 6, ELNAZEER DAWOD describes training three classifiers on the UCI Phishing Websites dataset. The post reports an 80/20 train/test split and five-fold cross-validation. On the reported test evaluation, Random Forest scored 96.70% accuracy, SVM 94.71%, and Logistic Regression 92.45%.
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Those figures are the author’s reported results for this experiment. They do not show that a detector will achieve the same accuracy on current phishing campaigns, unfamiliar websites or a live security product. The post does not provide a confusion matrix, precision, recall or external or temporal validation.
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What data the models used
The UCI Machine Learning Repository record for Phishing Websites lists 11,055 instances and 30 integer features. The dataset was donated on March 25, 2015, and credits Rami Mohammad and Lee McCluskey. Its age matters: a result on this historical dataset does not establish performance against newer phishing techniques or websites.
UCI also notes that reliable training data is a challenge and that the literature has not settled on definitive features that characterize phishing webpages. Those limits make it important to treat the dataset as a particular benchmark, not a complete representation of the changing web.
How the three reported models compare
| Classifier | Reported test accuracy | What the result establishes |
|---|---|---|
| Random Forest | 96.70% (reported by ELNAZEER DAWOD in the 2026 DEV Community post) | Highest accuracy among the three models in this experiment |
| SVM | 94.71% (reported by ELNAZEER DAWOD in the 2026 DEV Community post) | Second-highest accuracy among the three models in this experiment |
| Logistic Regression | 92.45% (reported by ELNAZEER DAWOD in the 2026 DEV Community post) | Lowest accuracy among the three models in this experiment |
The ranking is useful as a comparison within the reported setup, but accuracy alone cannot show how a detector behaves when the cost of a false alarm differs from the cost of missing a phishing site. A fuller assessment would need precision, recall, false-positive and false-negative counts, class balance, and evaluation on separate, newer data. Those results are not reported in the post.
Which features the author says mattered
The post identifies SSL certificate state and anchor-link behavior as the most important features. The author suggests that a phishing site might lack a valid certificate for its fake domain, or that a copied page design might preserve links pointing to the legitimate site. This is an interpretation of the feature ranking, not evidence that either feature causes a site to be phishing. As the author puts it, “This is my interpretation of the result, not something the experiment proved.”
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What a reader can take away
- The experiment is a useful beginner project showing how website features can be used to train and compare classifiers.
- The 96.70% figure is a reported Random Forest test accuracy on a held-out split of a dataset donated in 2015; it is not a general phishing-detection guarantee.
- The other reported figures—94.71% for SVM and 92.45% for Logistic Regression—also describe this experiment, not a broad head-to-head benchmark.
- Feature importance can suggest patterns worth investigating, but it does not prove why a site is malicious.
- The post discusses Python and scikit-learn as free tools and links to code; the reported experiment does not establish that paid software or particular hardware is required.
Read the author’s DEV Community article for the experiment description. The UCI dataset record provides its provenance and limitations.
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