This is Analysis and a Visualization of the Online News Popularity data set

• The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls.

Pyhon

In here we preprocess the data set and validate the information.

PowerBI

The vizualization of the data set using PowerBI

1. Title - The title at the top clearly defines the purpose of the dashboard. It indicates that the visual is focused on analyzing the popularity of online news articles, specifically by looking at how they perform in terms of shares and other related metrics like word counts, time periods, and the sentiment of the words used.
2. News Categories - This allows the users to filter the data based on different news categories.
3. Total number of shares - Display the total number of shares
4. Best-performed article URL - Display best-performed article's URL
5. Worst-performed article URL - Display worst-performed article's URL
6. Total shares by News types - The horizontal bar chart visualizes the number of shares across different news types. Each bar represents the total number of shares per category.
7. Total number of shares by Days of the week - Display the total number of shares by Days of the week
8. Shares by Positive vs. Negative word rates - A pie chart displays the proportion of articles based on their word sentiment (positive, negative, neutral) and how that correlates with shares.
9. Total Number of words in the content - This chart provides insight into the length of content by category, suggesting that word count has an impact on the shares.
10. Total Number of Shares by Time Period - This is a line chart that tracks the time period of the creation of news articles and shows this with the number of shares.

Reference

K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision
Support System for Predicting the Popularity of Online News. Proceedings
of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence,
September, Coimbra, Portugal.