Projecting future record prices with FBProphet, visualization with Altair.
my_token.py contains a template to place your Discogs token, your wantlist (download from Discogs) and your Popsike username and password.
discus.py scrapes current recommend sale prices from Discogs. This data was not sufficient for the model and was not used.
popsike_scraper.py uses Selenium to pull min / avg / max historical prices for each record from Popsike.
glue.py performs some cleaning functions on the data, also contains the DataFrames.
market_scrape.py pulls current data from the Discogs marketplace (i.e. records to buy)
prophet.py contains the bulk of the modelling with FBProphet.
app.py launches the local flask server, then navigate to http://0.0.0.0:5000/ in your browser. Click a record from the dropdown list to
chart.py contains not-yet-implemented prettier charts, and EDA.ipynb and prophet_start.ipynb were used for Exploratory Data Analysis and prototyping.