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Online News Popularity/README.md
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
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**Level:** Advanced <br/>
**Recommended Use:** Regression/Classification Models<br/>
**Domain:** Business/Web<br/>
## Online News Popularity Data Set
### Predict the number of shares in social networks
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![](OBDL960.jpg)
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This *intermediate* level data set has 39644 rows and 61 columns.
This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years.
This could be used to predict the number of shares of an article in social networks.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **regression/classification modelling techniques**.
It also allows you to practice with large number of features. Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
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### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios | | Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |------------------------------- |------------------------------------------------------------------------------------------------ |-------------- |---------------------------------------------------------------- |--------------- | |------------------- |------------------------------- |------------------------------------------------------------------------------------------------ |-------------- |---------------------------------------------------------------- |--------------- |
| 1 | URL | URL Of The Article (Non-Predictive) | Qualitative | "http://mashable.com/2013/01/07/amazon-instant-video-browser/" | 0 | | 1 | URL | URL Of The Article (Non-Predictive) | Qualitative | "http://mashable.com/2013/01/07/amazon-instant-video-browser/" | 0 |
...@@ -61,3 +90,13 @@ ...@@ -61,3 +90,13 @@
| 59 | Abs_Title_Subjectivity | Abs_Title_Subjectivity: Absolute Subjectivity Level | Quantitative | 0 | 0 | | 59 | Abs_Title_Subjectivity | Abs_Title_Subjectivity: Absolute Subjectivity Level | Quantitative | 0 | 0 |
| 60 | Abs_Title_Sentiment_Polarity | Abs_Title_Sentiment_Polarity: Absolute Polarity Level | Quantitative | 0.1875 | 0 | | 60 | Abs_Title_Sentiment_Polarity | Abs_Title_Sentiment_Polarity: Absolute Polarity Level | Quantitative | 0.1875 | 0 |
| 61 | Shares | Shares: Number Of Shares | Quantitative | 593 | 0 | | 61 | Shares | Shares: Number Of Shares | Quantitative | 593 | 0 |
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### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Online News Popularity Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity).
The UCI page mentions the following publication as the original source of the data set:
*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*
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