Movie-Data-Analysis-Microsoft

Overview

Topic: Microsoft wants to enter the movie business industry

• We were tasked to give three recommendations to Microsoft for entering the industry as a part of their Data Scientist team
• We used SQL along with the Python package Pandas to load in and clean data from data sets as well as analyze, create visualizations and make recommendations

Business Understanding

• The key stakeholders are the higher ups in the company such as the CEO and Owners
• Microsoft is entering a very saturated industry with many big competitors
  • How can Microsoft best navigate starting a new movie studio?
• A good measure of success in any industry is the return on investment and profit
• In our analysis we focused in on recent movies from 2010-2019 and their profitability
• The more granular focus of our analysis was in the profitability of top: movie genres, movie run times and movie actors/actresses

Data Understanding

• We used two different data sets
  • IMDB website
    • A data set which contained categorical information on movie start years, run times, peoples profession associated with the movie (actors, actresses, writers, directors, producers etc..)
  • The Numbers website
    • A data set which contained numerical information on movie production budgets and gross earnings from which we were able to calculate the profit
• We merged the two different data sets to conduct our analysis on movie profitability in our three areas of focus

Data Analysis

Most Profitable Genres

• Based on our data we found most profitable genres by median profit
• From the top 13 genres with the highest profitability based on the median profit we decided to focus in on the top 4 genres

Top 4 Genre's Profitability based on Run Times

• For these Top 4 profiting genres, we analyzed each with respect to profit by run times
• We found that for animation movies the most profitable run times are in the time range of 80-110 minutes
• For the other three genres, the most profitable run times are in the interval of 140-155 minutes
  • The 125-140 and 155-170 run times are quite profitable as well

Action Genre Most Profitable Actor/Actress

• Profitability of top 10 actors for the action genre
  • Chose the action genre because it had the most actors and actresses in the data set compared to the other top 4 genres
• We filtered down to actors who had been in 4 or more different action movies which represents their popularity and established success in the action genre

Conclusion

• If Microsoft were to enter the movie industry we would give 3 recommendations:
  1. Pursue 1 of the top 4 most profitable genres (Animation, Adventure, Sci-Fi and Action)
  2. For Adventure, Sci-Fi and Action focus on run times between 140-155 minutes. For Animation focus on run times between 80-110 minutes
  3. For Action movies, recruit 1 of the 10 actors previously shown

Repository Information

• This repository contains:
  • 2 folders containing exploratory notebooks
  • A folder containing data
  • A gitignore file
  • A presentation pdf
  • Final project Jupyter Notebook
  • A readme file

Presentation