Network Analysis and Applied Statistics Project

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