Data
Shark-Tank-Pitches

Shark-Tank-Pitches

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Acknowledgement Thanks to The Concept Center for publishing this article, an analysis, and pulling the data. Background Shark Tank is an ABC show where entrepreneurs around the United States can pitch their ideas to billionaires. The show started in Japan in 2009 and has gained traction over the years to what it is today. Wikipedia For this study, I thought that it would be interesting to take the 6 seasons of Shark Tank which consists of 122 episodes and 495 companies, see which ones performed the best and asked the most. In addition, I wanted to see if there are any trends when it comes to those companies getting a deal with at least one shark. Which of the Shark Tank companies will be the best? Lets explore from the data what we can find. Data The data was collected from Shark Analytics, who was able to aggregate the information into one relative area. Date Pulled: 11/28/2017 10:30pm Total Seasons: 6 Total Episodes: 122 Total Companies on the Show: 495

19 features

dealnominal2 unique values
0 missing
descriptionstring493 unique values
0 missing
episodenumeric29 unique values
0 missing
categorystring54 unique values
0 missing
entrepreneursstring421 unique values
72 missing
locationstring255 unique values
0 missing
websitestring455 unique values
38 missing
askedFornumeric60 unique values
0 missing
exchangeForStakenumeric28 unique values
0 missing
valuationnumeric116 unique values
0 missing
seasonnumeric6 unique values
0 missing
shark1string2 unique values
0 missing
shark2string4 unique values
0 missing
shark3string3 unique values
0 missing
shark4string4 unique values
0 missing
shark5string5 unique values
0 missing
titlestring493 unique values
0 missing
episode-seasonstring122 unique values
0 missing
Multiple_Entreprenuersnominal2 unique values
0 missing
Show all 19 features

19 properties

NumberOfInstances
495
Number of instances (rows) of the dataset.
NumberOfFeatures
19
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
110
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
108
Number of instances with at least one value missing.
NumberOfNumericFeatures
5
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
Dimensionality
0.04
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
26.32
Percentage of numeric attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
10.53
Percentage of nominal attributes.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
2
Number of binary attributes.
PercentageOfBinaryFeatures
10.53
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
21.82
Percentage of instances having missing values.
AutoCorrelation
Average class difference between consecutive instances.
PercentageOfMissingValues
1.17
Percentage of missing values.
Show all 19 properties

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