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The dataset created and utilized for our analysis and visualizations are characterized by consumer television viewing trends and ratings. As a starting point, we web scraped Emmy nominations from 2016-2020 and used that dataset as an anchor point and calculated the nomination frequency filtered by title. From there, we merged viewing medium (channel), IMDB scores, Reel scores and Rotten Tomatoes scores. We then built a Full Stack Web Application to host several visualizations and interactive dashboards to display our findings. The web app draws the data from Postgres then filters and displays it.

Jupyter Notebook 94.64% HTML 3.73% JavaScript 0.98% Python 0.04% CSS 0.61%
beautifulsoup canvasjs css flask fullstack-development html javascript pandas postgresql python regex requests splinter sqlalchemy webscraping wordcloud

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