Create README.md

README.md

@@ -0,0 +1,92 @@
+---
+task_categories:
+- tabular-regression
+- tabular-classification
+tags:
+- tabular
+size_categories:
+- 10K<n<100K
+---
+
+## Source
+Source: [UCI](https://archive.ics.uci.edu/ml/datasets/BlogFeedback)
+
+## Data Set Information:
+
+This data originates from blog posts. The raw HTML-documents
+of the blog posts were crawled and processed.
+The prediction task associated with the data is the prediction
+of the number of comments in the upcoming 24 hours. In order
+to simulate this situation, we choose a basetime (in the past)
+and select the blog posts that were published at most
+72 hours before the selected base date/time. Then, we calculate
+all the features of the selected blog posts from the information
+that was available at the basetime, therefore each instance
+corresponds to a blog post. The target is the number of
+comments that the blog post received in the next 24 hours
+relative to the basetime.
+
+In the train data, the basetimes were in the years
+2010 and 2011. In the test data the basetimes were
+in February and March 2012. This simulates the real-world
+situtation in which training data from the past is available
+to predict events in the future.
+
+The train data was generated from different basetimes that may
+temporally overlap. Therefore, if you simply split the train
+into disjoint partitions, the underlying time intervals may
+overlap. Therefore, the you should use the provided, temporally
+disjoint train and test splits in order to ensure that the
+evaluation is fair.
+
+## Attribute Information:
+
+1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
+For example, myblog.blog.org would be the source of the post myblog.blog.org/post_2010_09_10
+
+51: Total number of comments before basetime
+
+52: Number of comments in the last 24 hours before the
+basetime
+
+53: Let T1 denote the datetime 48 hours before basetime,
+Let T2 denote the datetime 24 hours before basetime.
+This attribute is the number of comments in the time period
+between T1 and T2
+
+54: Number of comments in the first 24 hours after the
+publication of the blog post, but before basetime
+
+55: The difference of Attribute 52 and Attribute 53
+
+56...60:
+The same features as the attributes 51...55, but
+features 56...60 refer to the number of links (trackbacks),
+while features 51...55 refer to the number of comments.
+
+61: The length of time between the publication of the blog post
+and basetime
+
+62: The length of the blog post
+
+63...262:
+The 200 bag of words features for 200 frequent words of the
+text of the blog post
+
+263...269: binary indicator features (0 or 1) for the weekday
+(Monday...Sunday) of the basetime
+
+270...276: binary indicator features (0 or 1) for the weekday
+(Monday...Sunday) of the date of publication of the blog
+post
+
+277: Number of parent pages: we consider a blog post P as a
+parent of blog post B, if B is a reply (trackback) to
+blog post P.
+
+278...280:
+Minimum, maximum, average number of comments that the
+parents received
+
+281: The target: the number of comments in the next 24 hours
+(relative to basetime)