Data
LottoMaster-144

LottoMaster-144

active ARFF Attribution-NonCommercial (CC BY-NC) Visibility: public Uploaded 20-07-2021 by DAVID GILBERT
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  • Computer Systems Machine Learning
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This is an experimental data set for trying to classify numbers in a lottery as "Highly likely to be picked" or "Not very likely to be picked". It is based on a little more than a years worth of actual daily drawing results from the Florida Fantasy Five, with derivative elements such as count of times picked, average times picked, days since last picked, etc.

36 features

countnumeric34 unique values
0 missing
daysSinceLastPickednumeric53 unique values
0 missing
avgDaysSinceLastPickednumeric30 unique values
0 missing
countsMeannumeric143 unique values
0 missing
countsMediannumeric39 unique values
0 missing
countsVariancenumeric279 unique values
0 missing
countsStdDevnumeric279 unique values
0 missing
countsMeanDiffnumeric650 unique values
0 missing
countsMedianDiffnumeric51 unique values
0 missing
countsVarianceDiffnumeric2206 unique values
0 missing
countsStdDevDiffnumeric4048 unique values
0 missing
countsSNDnumeric4098 unique values
0 missing
daysMeannumeric123 unique values
0 missing
daysMediannumeric10 unique values
0 missing
daysVariancenumeric346 unique values
0 missing
daysMeanDiffnumeric1187 unique values
0 missing
daysMedianDiffnumeric108 unique values
0 missing
daysVarianceDiffnumeric4953 unique values
0 missing
daysStdDevDiffnumeric5467 unique values
0 missing
daysSNDnumeric5450 unique values
0 missing
daysStdDevnumeric346 unique values
0 missing
countsModeDiffnumeric35 unique values
0 missing
daysModeDiffnumeric69 unique values
0 missing
percentnumeric1017 unique values
0 missing
avgPercentnumeric1 unique values
0 missing
percentsMeannumeric1 unique values
0 missing
percentsMediannumeric102 unique values
0 missing
percentsVariancenumeric283 unique values
0 missing
percentsMeanDiffnumeric1018 unique values
0 missing
percentsStdDevDiffnumeric4088 unique values
0 missing
percentsSNDnumeric3920 unique values
0 missing
percentsStdDevnumeric282 unique values
0 missing
percentsMedianDiffnumeric1104 unique values
0 missing
percentsVarianceDiffnumeric3490 unique values
0 missing
percentsModeDiffnumeric1104 unique values
0 missing
pickednumeric2 unique values
0 missing
Show all 36 features

19 properties

NumberOfInstances
12528
Number of instances (rows) of the dataset.
NumberOfFeatures
36
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
36
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
Dimensionality
0
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
0
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
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
AutoCorrelation
Average class difference between consecutive instances.
PercentageOfMissingValues
0
Percentage of missing values.
Show all 19 properties

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