
*See my other [GitHub Repo](https://github.com/krozic/imdb-chrome-extension) for the chrome extension I built off this data.

# Summary

This application takes a chosen film as an input and creates a plot and table describing the rank of the film for the specific genres it is categorized in.

The intent of this project is to compare the average imdb ratings between genres and visualize the distribution of ratings for each genre in an effort to translate a film rating to a genre specific ranking. 

<div>
	![app](/images/imdb-rating/app.png)
</div>

# Data Sources

2 datasets, 2 APIs, and a web crawler comprise the backend data handling for this project:
- [IMDb open dataset](https://www.imdb.com/interfaces/)
- [Stefano Leone's IMDb extensive dataset](https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset)
- [OMDb API](https://www.omdbapi.com/) (Third party API)
- [IMDb-API](https://imdb-api.com/) (Third party API)
- [IMDB website](https://www.imdb.com/) (BS4 web crawling)


# Methods

The tables 'title.basics.tsv', 'title.ratings.tsv' and 'IMDB movies.csv' were loaded into Microsoft SQL Server as TitleBasics, TitleRatings and TitleExtras respectively. These tables were used for exploratory data analysis and subsequent creation of the tables necessary for visualization (OMDb API was used to source the Rotten Tomatoes and Metacritic ratings). The database schema is shown below (created in [dbdiagram.io](https://dbdiagram.io/))

<div style={{'text-align': 'center'}}>
	![app](/images/imdb-rating/imdb_table_schema.png)
</div>

The python package `pyodbc` was then used to load the tables from this database into `pandas` for visualization in `matplotlib` and `plotly`. Dash was used for the frontend of the web application.



IMDB Ratings Visualization


# Summary

A python Web Scraper used to search a number of different retailers to check for stock. The package `twilio` is used to send a text when stock is received. This app was used to acquire a personal graphics card during the [Great Graphics Card Shortage of 2020](https://www.bbc.com/news/technology-55755820).

Nvidia Graphics Card Scraper


# TIFF-2019

Movie_Grabber.py extracts film information from https://2019.tiffr.com using `bs4` to build the variables 'Title', 'Director', 'Category', 'Link', and the metric 'Heart' to gauge the popularity of the title. A comma separated file 'movies.csv' is exported which can then be explored in any spreadsheet software.

'Text_Notification.py' takes a list of films and sends a text message using `twilio` when tickets become available for the title.

### Spreadsheet Sample

{/* <div style={{'text-align': 'center'}}> */}
<div>
	![Top 20 Films](/images/tiff-scraper/table_screenshot.png)
</div>
{/* <div style={{margin: '0rem 2.5rem'}}>
	<Image
		src={ '/images/tiff-scraper/table_screenshot.png' }
		alt='app'


	/>
</div> */}

Without this application, I would not have bought tickets for Jojo Rabbit, Knives Out, Parasite or The Lighthouse, which turned out to be great films at the festival.

Kyle Rozic

bs4

All Tags

IMDB Ratings Visualization

Nvidia Graphics Card Scraper

TIFF Data Scraping with Organization in Excel