Module2 Applied Statistics Project
Using this dataset: https://www.kaggle.com/rounakbanik/the-movies-dataset?select=credits.csv
Exploring a kaggle data set for clues on what's responsible https://www.kaggle.com/rounakbanik/the-movies-dataset Please download:
- movies_metadata.csv
- credits.csv
- return_of_investment_(thomas).ipynb
- cleaning and subsetting
- regressing against actors
- regressing against directors
- regressing against if movie is part of a franchise
- regressing against genres
- significance check
Explored the movies_metadata.csv from kaggle dataset merging with two other datasets of ratings_small.csv,and a subset of actors from credits.csv data set.
The aim of this analysis is to explore different features that contributes to high ratings in a successful movie. Please download:
- credits.csv
- ratings
- movies_metadata.csv
- Exploring_movie_ratings.Victoria.ipynb
- cleaning, exploding and subsetting
- Highest and lowest rated movies
- Highest rated movies based on popularity, actors, original_language and genres
Exploring the credits.csv file from the Kaggle dataset. Subsetted to include only cast members per movie_id. Then I isolated the top 200 actors. I also conducted a network analysis on the top 100 female actors and the top 100 male actors. Please download:
- credits.csv
- Network-Analysis-by-Gender-Kate.ipynb
- Network-analysis-Top200-Actors-Kate.ipynb
- cleaning, exploding, cleaning original csv files
- preparing pandas dataframes for use with Networkx package
- visualizing the network of the most frequently booked actors
- visualizing the network of the most frequently male and female actors respectively
- finding the degrees of each node (actor) and the actor's network centrality and visualizing in dataframe