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Shoot for the Stars - the analysis of data using Python, Pandas, and Data Visualization

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Shoot for the Stars

An analysis of return on investment in the film industry based on genre, lead actor, and director.

The analysis we conducted of the film industry was derived using data from IMDB. This included a large list of movies as well as plenty of information about each movie - such as who directed the movie, who starred in it, what it was rated, and more.

The first point of interest we approached this data set with was ROI (return on investment). As this is a finance class, we focused on the financial aspect of the film industry. We were curious to see if movies that have large gross profits also have a large return on investment. Were they making a lot in the box office, but way over budgeting for big name stars? Was it worth the investment? Do certain genres tend to have a greater return? These are some of the questions we aimed to uncover.

Below is a summary answer to each question we asked, as well as a plot to help visualize our findings.

Genre Trends

  • Which genre has the highest average ROI?

Genre_ROI

Genre_ROI10

  • Which genre has the highest average gross profit?
  • Which genre has the highest average budget?

Genre_P&B

  • Which genre has the highest average IMDB ratings?

Genre_Ratings

Actor Trends

  • Which actor's movies have the highest average ROI?

Actor_ROI

  • Which actor's movies have the highest average gross profit?
  • Which actor's movies have the highest average budget?

Actor_P&B

  • Which actor's movies have the highest average IMDB ratings?

Actor_Ratings

Director Trends

  • Which director's movies have the highest average ROI?

Director_ROI

Director_ROI2

  • Which director's movies have the highest average gross profit?

Director_Profit

  • Which director's movies have the highest average budget?

Director_Budget

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