Prediting Article Shares : News Popularity Dataset

This repository is final submission for ST558 Project 3 by Shyam Gadhwala & Kamlesh Pandey. In this project we are predicting the number of shares for an article published in Mashable website.

For this project, we have utilized R programming language and its several libraries for data analysis, visualization, and predictive modeling. R libraries used in the project are listed below:

library(dplyr)  
library(readr)  
library(ggplot2)  
library(tidyr)  
library(tidyverse)  
library(caret)  
library(glmnet)  

All reports generated via automating R Markdown can be found here:

Technology articles analysis
Lifestyle articles analysis
Entertainment articles analysis
Business articles analysis
Social Media articles analysis
World articles analysis

We have also utilized powerful R markdown to automate report generation for different types of data type available in the online article website. Following code is responsible for automating reports and generating github rendered page.

library(rmarkdown)
library(tidyr)

dataType = c('data_channel_is_lifestyle',
             'data_channel_is_entertainment',
             'data_channel_is_bus',
             'data_channel_is_socmed',
             'data_channel_is_world',
             'data_channel_is_tech')

outputFile <- paste0(dataType, ".html")

#create a list of data channel type
params = lapply(dataType, FUN = function(x){list(data_channel = x)})

reports <- tibble(outputFile, params)

apply(reports, MARGIN = 1,
      FUN = function(x){
        render(input = 'ST558_Project_3.Rmd', output_file = x[[1]], params = x[[2]])
      })