https://github.com/derak-isaack/movie_analysis_project

Analyze data to give Microsoft recommendations before joining the movie industry.
https://github.com/derak-isaack/movie_analysis_project

Last synced: about 1 month ago
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Analyze data to give Microsoft recommendations before joining the movie industry.

• Host: GitHub
• URL: https://github.com/derak-isaack/movie_analysis_project
• Owner: derak-isaack
• License: other
• Created: 2023-09-15T02:53:59.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-09-15T06:23:44.000Z (over 1 year ago)
• Last Synced: 2023-09-15T21:55:23.029Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 68.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.md

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README

        

# Movie & film industry factors analysis(Box office)

 In this project, we are tasked with analyzing data from various sites some with common columns and some with different columns.

 The objectives and tasks are self generated and may vary accross individuals. The factors which make good films are:

  * The genre 

  * The movie/film duration. Long films makes require alot of concentration unlike short duration films.

  * The rating of movies determines the audience. Films with explicit contents cannot have an audience for children.

  * The experience of the directors and writers matters a lot because no one wants to dealwith an inexperienced person.

After listing out our objectives, read into the provided data with the sole aim of understanding the contents. Check the shape, info and describe functions to get a better understanding of provided data.

Third-party libraries such as pandas and sqlite3 come in handy when reading data into pandas dataframes. 

Data cleaning is the next step after understanding our data and some contents of few columns. Dropping missing values, converting data types, stripping necessary column values and checking for duplicates. 

The next stage is data analysis where we seek to adress our objectives in a clear and simple visual manner. Bar graphs offer simple visualizations. matplotlib and seaborn comes in handy during this stage. 

It is more efficient using functions to plot the graphs to avaoid code repetition and easier code debbuging. 

NOTE: All the plots should seek to adress the objectives outlined, should not be cluttered and should have labels and headings.

      

[![linkedin](https://www.linkedin.com/in/isaackodhiamboodera/)](https://www.linkedin.com/)

## 🚀 About Me

I'm a data scientist...

## Authors

- [@derak-isaack](https://github.com/derak-isaack)