#Facebook Metrics (2016) modeling for data science fellowship
Commencing the initial clean-up on the data and explore what can be done with the data.
Start by looking for any numerical outliers as well as plotting out some basic information from the data. Based on these plots and the domain knowledge I have, I'll begin making a plan as to how I wish to further analyze the data.
The goal of this EDA is to understand how different features relate to each other and begin to consider which key features I'd like to pursue first in my analysis of the data. At the end of this report, there should be a good understanding of how the Facebook metrics data is distributed.
Data retrieved from UC Irvine Machine Learning Repository. Facebook performance metrics of a renowned cosmetic's brand Facebook page. The data is related to posts' published during 2014. Metrics donated 08/04/2016. This dataset is licensed under a Creative Commons Attribution 4.0 International, (CC BY 4.0) license which allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.
Additional Variable Information: it includes seven (7) features known prior to post publication and twelve (12) features for evaluating post impact.
Citation: (Moro et al., 2016) S. Moro, P. Rita and B. Vala. Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, Elsevier, In press.
Available at: https://dx.doi.org/10.1016/j.jbusres.2016.02.010