Data
Cinema-Tickets

Cinema-Tickets

active ARFF CC BY-NC-SA 4.0 Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Context Cinema industry is not excluded of getting advantage of predictive modeling. Like other industry it can help cinemas for cost reduction and better ROI. By forecasting sale, screening in different location could be optimized as well as effective market targeting and pricing. Also historical data of sale and movies details e.g. cost, cast and crews, and other project details like schedule, could help producers to select high performance cast and crews and planning for better projects ROI . Also it helps to assign screening location on hot spots and areas. Content About eight months sales history of different cinemas with detailed data of screening , during 2018 with encoded annonymized locations . Starter Kernels EDA , Temporal Feat Eng and XGBoost Inspiration Time series analysis Cinema Clustering Forecast sales for each cinema Recommendation: Movie genre recommendation for cinemas Cinema location recommendation Cast and crew ratings

14 features

film_codenumeric48 unique values
0 missing
cinema_codenumeric246 unique values
0 missing
total_salesnumeric9464 unique values
0 missing
tickets_soldnumeric2231 unique values
0 missing
tickets_outnumeric96 unique values
0 missing
show_timenumeric51 unique values
0 missing
occu_percnumeric9311 unique values
125 missing
ticket_pricenumeric23235 unique values
0 missing
ticket_usenumeric2283 unique values
0 missing
capacitynumeric57361 unique values
125 missing
datestring234 unique values
0 missing
monthnumeric10 unique values
0 missing
quarternumeric4 unique values
0 missing
daynumeric31 unique values
0 missing

19 properties

142524
Number of instances (rows) of the dataset.
14
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
250
Number of missing values in the dataset.
125
Number of instances with at least one value missing.
13
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
92.86
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
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.
0.09
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.01
Percentage of missing values.

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