Online News Popularity

Dataset: httpshttps://archive-beta.ics.uci.edu/ml/datasets/online+news+popularity

Exploratory Data Analysis in a Regression Problem

Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field)

• Attribute Information:

0. url: URL of the article (non-predictive)

1. timedelta: Days between the article publication and the dataset acquisition (non-predictive)

2. n_tokens_title: Number of words in the title

3. n_tokens_content: Number of words in the content

4. n_unique_tokens: Rate of unique words in the content

5. n_non_stop_words: Rate of non-stop words in the content

6. n_non_stop_unique_tokens: Rate of unique non-stop words in the content

7. num_hrefs: Number of links

8. num_self_hrefs: Number of links to other articles published by Mashable

9. num_imgs: Number of images

10. num_videos: Number of videos

11. average_token_length: Average length of the words in the content

12. num_keywords: Number of keywords in the metadata

13. data_channel_is_lifestyle: Is data channel 'Lifestyle'?

14. data_channel_is_entertainment: Is data channel 'Entertainment'?

15. data_channel_is_bus: Is data channel 'Business'?

16. data_channel_is_socmed: Is data channel 'Social Media'?

17. data_channel_is_tech: Is data channel 'Tech'?

18. data_channel_is_world: Is data channel 'World'?

19. kw_min_min: Worst keyword (min. shares)

20. kw_max_min: Worst keyword (max. shares)

21. kw_avg_min: Worst keyword (avg. shares)

22. kw_min_max: Best keyword (min. shares)

23. kw_max_max: Best keyword (max. shares)

24. kw_avg_max: Best keyword (avg. shares)

25. kw_min_avg: Avg. keyword (min. shares)

26. kw_max_avg: Avg. keyword (max. shares)

27. kw_avg_avg: Avg. keyword (avg. shares)

28. self_reference_min_shares: Min. shares of referenced articles in Mashable

29. self_reference_max_shares: Max. shares of referenced articles in Mashable

30. self_reference_avg_sharess: Avg. shares of referenced articles in Mashable

31. weekday_is_monday: Was the article published on a Monday?

32. weekday_is_tuesday: Was the article published on a Tuesday?

33. weekday_is_wednesday: Was the article published on a Wednesday?

34. weekday_is_thursday: Was the article published on a Thursday?

35. weekday_is_friday: Was the article published on a Friday?

36. weekday_is_saturday: Was the article published on a Saturday?

37. weekday_is_sunday: Was the article published on a Sunday?

38. is_weekend: Was the article published on the weekend?

39. LDA_00: Closeness to LDA topic 0

40. LDA_01: Closeness to LDA topic 1

41. LDA_02: Closeness to LDA topic 2

42. LDA_03: Closeness to LDA topic 3

43. LDA_04: Closeness to LDA topic 4

44. global_subjectivity: Text subjectivity

45. global_sentiment_polarity: Text sentiment polarity

46. global_rate_positive_words: Rate of positive words in the content

47. global_rate_negative_words: Rate of negative words in the content

48. rate_positive_words: Rate of positive words among non-neutral tokens

49. rate_negative_words: Rate of negative words among non-neutral tokens

50. avg_positive_polarity: Avg. polarity of positive words

51. min_positive_polarity: Min. polarity of positive words

52. max_positive_polarity: Max. polarity of positive words

53. avg_negative_polarity: Avg. polarity of negative words

54. min_negative_polarity: Min. polarity of negative words

55. max_negative_polarity: Max. polarity of negative words

56. title_subjectivity: Title subjectivity

57. title_sentiment_polarity: Title polarity

58. abs_title_subjectivity: Absolute subjectivity level

59. abs_title_sentiment_polarity: Absolute polarity level

60. shares: Number of shares (target)