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outline.txt
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streamlit run analysis.py
Datasets:
ds1 - https://raw.githubusercontent.com/danielgrijalva/movie-stats/7c6a562377ab5c91bb80c405be50a0494ae8e582/movies.csv
ds2 - https://www.kaggle.com/datasets/sakshigoyal7/credit-card-customers
Guide layout and components:
https://docs.streamlit.io/library/api-reference/layout
https://earthly.dev/blog/streamlit-python-dashboard/
https://github.com/ChrisDelClea/streamlit-agraph
https://www.geeksforgeeks.org/python-imdbpy-retrieving-actor-from-the-movie-details/
https://vgnshiyer.medium.com/link-prediction-in-a-social-network-df230c3d85e6
https://gist.github.com/simonkamronn/e846d88b8660f1aba7edbeca9afa1bd9
https://docs.streamlit.io/library/api-reference/status/st.info
Deploy:
https://docs.streamlit.io/knowledge-base/tutorials/deploy/kubernetes
Questions ds1:
- part 1
- filter by review score, genre - ok
- boxplot to show
- world map mean budget or gross in countries rescale - ok
- histogram of votes - https://gist.github.com/simonkamronn/e846d88b8660f1aba7edbeca9afa1bd9
- Distribution of votes, show score in a subsample that has a homogeneous distribution of votes - ok
- part 2
- pairplot with score, time, gross and budget - ok
- Budgets have some correlation with high score? - ok
- Mean of movie time by score - ok
- feature engineering - separate movie time into categories (short, medium and high) and put on a boxplot where the y is the score - ok
- idem with score in categories, Filtering by country,where y is the budget - ok
- Companies with budgets and gross as stacked bars --- profit (mean gross - mean budget) - ok
- median gross/budget/time aggregating by min max or avg, along the year ranges (five) - ok
- part 3
- top 10 actors in high score movies
- most sucessful writers
- graph where the x are the top 10 actor stars ordered by participation in movies above 7 score, stacked bars showing participation in movies with hgh and medium score ranges
- network of stars filtered by score range
- get imdb actors of a movie and show semantic graph