Update README.md

README.md

@@ -1,12 +1,58 @@
 ---
-license: apache-2.0
+license: cc-by-4.0
+task_categories:
+- text-classification
+- feature-extraction
+- tabular-classification
+language:
+- sq
+- th
+- it
+- tl
+- de
+- hr
+- fi
+- da
+- lv
+- pl
+- ca
+- ro
+- ja
+- lt
+- af
+- ru
+- so
+- en
+- id
+- cs
+- sw
+- es
+- sl
+- hu
+- ko
+- nl
+- pt
+- tr
+- sv
+- sk
+- cy
+- bg
+- fr
+- et
+- no
+- vi
+- multilingual
+size_categories:
+- 1M<n<10M
 ---
 **I have decided to release the auto-moderation models all at once sometime in July, 2023. The curated/original datasets for training these models will be avaliable first.**
 
-In the *features* train set, there're 593,430 websites online at the time of data collection. The plots below show the regression line and correlation coefficients of 20+ features extracted and whether the URL is malicious.
+This dataset is original, and my feature extraction method is covered in [feature_extraction.py](./feature_extraction.py)
+
+In the *features* dataset, there're 770,098 websites online at the time of data collection. The plots below show the regression line and correlation coefficients of 20+ features extracted and whether the URL is malicious.
 If we could plot the lifespan of URLs, we could see that the oldest website has been online since Nov 7th, 2008, while the most recent phishing websites appeared as late as July 10th, 2023.
 
-As we can see, there's no correlation between `is_malicious` and the columns `meta_percentage`, `mouseover_changes`, `not_indexed_by_google`, `right_click_disabled`, and `popup_window_has_text_field` as of July, 2023, contrary to some [analyses of researchers in 2013 on phishing detection](./Phishing_Websites_Features.docx).
+As we can see, there's no correlation between `is_malicious` and the columns `meta_percentage`, `mouseover_changes`, `not_indexed_by_google`, and `right_click_disabled` as of July, 2023, contrary to some [analyses of researchers in 2013 on phishing detection](./Phishing_Websites_Features.docx).
 The majority of features have very weak correlations with `is_malicious`, while a minority has a weak correlation. Is this problematic for training? Not really.
 
 I split the classification task into two stages in anticipation of the limited availability of online phishing websites due to their short lifespan, as well as the possibility that research done on phishing is not up-to-date:
@@ -15,4 +61,4 @@ I split the classification task into two stages in anticipation of the limited a
 
 This way, I can make the most out of the limited phishing websites avaliable.
 
-![Phish Eater Data Analysis](https://i.imgur.com/ICMudAG.png)
+![Phish Eater Data Analysis](https://i.imgur.com/ADh6luR.png)