Project 3
For this project, I am analyzing a dataset of features about articles published by Mashable over a period of two years. My overall goal is predicting the number of shares an article will get based on the attributes provided in the dataset. I looked at the data split out by data channels. There are six data channels in the complete dataset: lifestyle, entertainment, business, social media, technology, and world. My report for each data channel contains some summary statistics and plots, model-fitting for a linear regression model and a boosted tree, and a comparison of the predictive abilities of the two models. The full dataset contains 61 attributes for each article, but I focused on shares as the response variable and the following six predictor variables for summarizing and modeling:
- num_hrefs: Number of links
- n_tokens_title: Number of words in the title
- kw_avg_avg: Average keyword
- average_token_length: Average length of the words in the content
- num_imgs: Number of images
- n_non_stop_unique_tokens: Rate of unique non-stop words in the content
A lot of the challenge in this project was related to the automation. One thing that worked well for me on this project was to start the automation pieces of the project with enough time available to do some stumbling around and thinking about how to get them to work. I also ran into the challenge of not having a partner for this project, which meant there was not a teammate to turn to when things were not working. If I could do something differently, I would have gotten an overall earlier start on the project to spread the work out even more and allow more opportunities to look at things with fresh eyes.
My biggest takeaways from this project are that automating reports in RStudio with R Markdown is a very powerful tool and predicting the number of shares that articles will get is pretty difficult. None of my models worked very well for predicting the number of shares.
If you would like to see the results of my analysis, the rendered Github Pages site can be viewed here and the repository is located here.