katychow / dsi_project3_pricingadswithreddit Goto Github PK

The objective of Project 3 was to be able to use classification methods and run an experiment on whether or not one could predict the correct subreddit

Reddit is starting to sell advertising space in subreddits. They want a tiered structure to pricing based on post popularity. (eg. if a post is more popular, it will cost more for ad space within a subreddit) It seems as though while we are very good at predicting the correct subreddit, we are not very good at predicting the up votes. Perhaps a better marketing strategy would be to predict which subreddits are the most popular on the hot page and price advertisements based on the subreddit.

Data scraped from subreddits are the psychology and mental health subreddit.

1. Code Folder, 4 Files, Organized with _NB suffix

• NB1 contains code to scrape subreddit data
• NB2 contains code to run models to classify subreddit data and determine if the title of a subreddit is either mental health or psychology, some visualizations at the end from using sentiment analysis
• NB3 contains code to run adhoc visualizations including word cloud code and also lots of histograms
• NB4 contains code to run models on prediction of 'ups' given a specific subreddit
  
  

2. Data Folder, 6 Files

• Subreddit webscrape data denoted by Reddit_Subreddit Name Here_datefilewritten.csv
• 4 subreddit webscraped data files, used Reddit_MH, most current version of Reddit_Psych for project
• other files include, "train" dataset intended for visualizations and coefficient values of classification model in CSV format
  
  

3. Images Folder, 22 Files

• Wordcloud images denoted by suffix of _wordcloud
• histograms of Top20 words for each subreddit and both subreddits combined denoted by prefix of Top20
• scatterplots of sentiment analysis comparing pos,neg,neu scores to the compound scores denoted by prefix of RedditComparisonsSentiments
• a few model flow charts denoted with psycho-or-mentalhealth prefix
• other images contain misnamed versions of above, the background image for wordclouds, and histogram comparing words across both subreddits.