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This is a project taking csv and json data sets and creating graphs using matplotlib

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# matplotlib
**This is a project taking csv and json data sets and creating graphs using matplotlib**

# Plot 1: US Movies Produced by Year (1900-2018) 
<img src=movies_plot.png> 
This is a bar graph plot that shows the total amount of US movies produced between 1900-2018. I got this data set from [this](https://github.com/jdorfman/awesome-json-datasets) site, which contains a ton of cool json datasets! This dataset contains a lot of data, such as the genre, year, cast, title, and much more. In terms of the data itself, I wanted to see if there was a trend over time in terms of movies produced - but I was unsure which way the trend would go. I was suprised that the most movies were produced between the last 1910's and the late 1950's, whith a large decrease between the 1960's until the mid-1980's.

# Plot 2: Neflix Shows & US Movies Produced by Year (1990-2015) 
<img src=netflixmovies_plot.png> 
This is a line graph plot that compares the amount of Netflix shows created versus the amount of US movies produced between 1990-2015. I chose to plot both of these data sets on one graph to see if there was a correlation between the two. The US movies data set, found [here](https://github.com/jdorfman/awesome-json-datasets) contains the year, genre, cast, title, and more. The Neflix shows data set, found [here](https://www.kaggle.com/datasets/shivamb/netflix-shows?resource=download) has the cast, year, rating, duration, and more. This graph shows that there is at least a correlation between the amount of Netflix shows released versus the amount of US Movies produced, but I cannot draw any causations. I also would need to run a multi-regression analysis. It would be cool to revisit and update this graph once more data comes out on the more recent years, as COVID probably had a large impact on releases (not sure which, Movies or Netflix, would 'benifit' more, though).

[Here](https://github.com/mikeizbicki/cmc-csci040/tree/2022fall/project_02) is a link to the instructions for this project

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