https://github.com/mr-chang95/udacity_movie_project
Movie Data Analysis and Visualization Project for Udacity's Data Analyst Program. Using Python in Jupyter Notebook.
https://github.com/mr-chang95/udacity_movie_project
data-analysis data-visualization jupyter-notebook movie python
Last synced: 3 months ago
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Movie Data Analysis and Visualization Project for Udacity's Data Analyst Program. Using Python in Jupyter Notebook.
- Host: GitHub
- URL: https://github.com/mr-chang95/udacity_movie_project
- Owner: Mr-Chang95
- Created: 2021-11-16T04:36:49.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-03-27T21:34:52.000Z (over 4 years ago)
- Last Synced: 2025-01-26T15:34:12.199Z (over 1 year ago)
- Topics: data-analysis, data-visualization, jupyter-notebook, movie, python
- Language: Jupyter Notebook
- Homepage:
- Size: 278 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
### Date Created
11/25/2021
### Project
This dataset contains information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue and starting at year 1966. As a movie enthusiast, I am interested in exploring how the audience vote on movies over the years. This project is inspired by Youtube videos explaining "why modern movies are bad", arguing that the quality and creativity are deteriorating.
The movie dataset was provided by Udacity and is also publicly available in kaggle. The data includes information about 10,000 movies collected from The Movie Database (TMDb), including user ratings and revenue. The data can be accessed from this link [here](https://www.google.com/url?q=https://d17h27t6h515a5.cloudfront.net/topher/2017/October/59dd1c4c_tmdb-movies/tmdb-movies.csv&sa=D&ust=1605197347716000&usg=AOvVaw1SoCpfUCeHn5oyvjgbJPP-).
### Variables in the dataset
```markdown
1. id
2. imdb_id
3. popularity
4. budget
5. revenue
6. original_title
7. cast
8. homepage
9. director
10. tagline
11. keywords
12. overview
13. runtime
14. genres
15. production_companies
16. release_date
17. vote_count
18. vote_average
19. release_year
20. budget_adj
21. revenue_adj
```
### Goal/Questions
This project will use films in the TMDb database to answer the following questions.
- How has the output of movies changed over the years?
- Average Runtime Of Movies From Year To Year?
- Average Vote Of Movies From Year To Year?
- Over the years, how has the output for films of different lengths (short, average, and long) changed?
- What is the relationship between the films' length and vote average over time?
- Total Number Of Movies Released For Each Genre?
- Average Profit By Year?
- What Is The Relationship Between Release Year And Profit?
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### Packages Used
In this project, I will be using data obtained from kaggle to answer questions about the Database. I used numpy, pandas, matplotlib, along with the data file 'tmdb-movies.csv'.