Datathon
Ended
Data Sprint #11: Online News Popularity
Find the popularity of the news articles
Medium
|
246 Submissions
Context
Reading the newspaper may be a sensible habit. It carries information concerning politics, economy, recreations, sports, business, industries, trade and commerce. This habit will not only enhance your information concerning general information but it will likewise improve your language skills and vocabulary.
An online newspaper is the online version of a newspaper, either as a complete publication or the on-line version of a written periodical. Online news services have several uses, and for this reason, this service encompasses a pile of benefits.
Unlike watching the news on TV, or listening to it on the radio, online news services enable the user to decide on what articles they hear, watch, or read. this can be helpful, as individuals would not “waste their time” on articles that don’t interest them – they now have the control to pick whatever interests them.
Problem Statement
With the zoom of online news services and social media, it's incredibly useful if we could verify readers’ unseen behavioural patterns. In addition to that, it is helpful to shed light on readers’ intentions and to predict the recognition of the internet news, which implies whether the news article will receive a good number of readers' attention. It's vital so as to present pre-info to the media staff (authors, advertisers, etc.) to modify every article in line with its quality with none influence from.
Objective
Imagine you are working as a Data Scientist for an online newspaper. You are required to build a Machine Learning model that will predict the number of shares (popularity) for the given news or article.
Evaluation Criteria
Submissions are evaluated using Root Mean Squared Error (RMSE).
How do we do it?
Once you generate and submit the target variable predictions on the testing dataset, your submissions will be compared with the true values of the target variable.
The True or Actual values of the target variable are hidden on the DPhi Practice platform so that we can evaluate your model's performance on testing data. Finally, a Root Mean Squared Error (RMSE) for your model will be generated and displayed.
Update at the end of the data sprint: Earlier the evaluation metric was MSE instead of RMSE due to which the errors were very high. Now the metric has been corrected from MSE to RMSE.
About the Data
This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years.
To load the training data in your jupyter notebook, use the below command:
import pandas as pd
news_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/online_news_popularity/train_set_label.csv" )
Data Description
0. url: URL of the article
1. timedelta: Days between the article publication and the dataset acquisition
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)
Saving Prediction File & Sample Submission
You can find more details on how to save a prediction file here: https://discuss.dphi.tech/t/how-to-submit-predictions/548
Sample submission: You should submit a CSV file with a header row and the sample submission can be found below.
prediction
110
45
12
225
Etc.
Note that the header name should be prediction else it will through evaluation error
Test Dataset
Load the test data (name it as test_data). You can load the data using the below command.
test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/online_news_popularity/test_set_label.csv')
Here the target column is deliberately not there as you need to predict it
Acknowledgement
This dataset was downloaded from UCI Machine Learning Repository.
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