Data
Marvel_Movies_Dataset

Marvel_Movies_Dataset

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Description: The "Marvel Movies.csv" dataset is a comprehensive collection of financial and reception data related to Marvel Cinematic Universe (MCU) films. This dataset chronicles a selection of movies, providing insights into their financial success and critical reception. Covering films from the mid-2010s to the late 2010s, it includes various metrics such as yearly release information, worldwide gross, budget details, domestic and international earnings, opening and second weekend performance, and audience and critic scores. Attribute Description: - movie: Title of the Marvel movie. - category: Associated Marvel Comics character or franchise. - year: Release year of the movie. - worldwide gross ($m): Total global box office earnings in millions of USD. - % budget recovered: Percentage of the production budget recovered through earnings. - critics % score: Percentage score awarded by critics. - audience % score: Percentage score awarded by audiences. - audience vs critics % deviance: Deviation in percentage points between audience and critic scores. - budget: Production budget of the movie in millions of USD. - domestic gross ($m): Box office earnings in millions of USD within the domestic market. - international gross ($m): Box office earnings in millions of USD from international markets. - opening weekend ($m): Earnings in millions of USD during the opening weekend. - second weekend ($m): Earnings in millions of USD during the second weekend after release. - 1st vs 2nd weekend drop off: Percentage drop in earnings from the first to the second weekend. - % gross from opening weekend: Percentage of total gross earnings acquired during the opening weekend. - % gross from domestic: Percentage of total gross earnings from the domestic market. - % gross from international: Percentage of total gross earnings from international markets. - % budget opening weekend: Percentage of the production budget earned during the opening weekend. Use Case: This dataset is valuable for film industry analysts, marketing professionals, and business researchers focusing on the financial viability and reception of big-budget movies within major franchises. By examining metrics such as worldwide gross, audience versus critics score deviance, and budget recovery percentages, users can identify trends in film performance, audience preferences, and critical reception. Further, the dataset can aid in comparative studies across different MCU phases or characters, offering insights for future film projects and marketing strategies.

18 features

movienominal36 unique values
0 missing
categorynominal11 unique values
0 missing
yearnominal14 unique values
0 missing
worldwide gross ($m)string36 unique values
0 missing
% budget recoveredstring34 unique values
0 missing
critics % scorestring22 unique values
0 missing
audience % scorestring24 unique values
0 missing
audience vs critics % deviancestring20 unique values
0 missing
budgetnumeric19 unique values
0 missing
domestic gross ($m)numeric35 unique values
0 missing
international gross ($m)numeric36 unique values
0 missing
opening weekend ($m)numeric34 unique values
0 missing
second weekend ($m)numeric35 unique values
0 missing
1st vs 2nd weekend drop offstring18 unique values
0 missing
% gross from opening weekendnumeric34 unique values
0 missing
% gross from domesticstring33 unique values
0 missing
% gross from internationalstring35 unique values
0 missing
% budget opening weekendstring36 unique values
0 missing

19 properties

36
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
6
Number of numeric attributes.
3
Number of nominal attributes.
Percentage of instances belonging to the most frequent class.
16.67
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0.5
Number of attributes divided by the number of instances.
33.33
Percentage of numeric attributes.

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