Data
WMO-Hurricane-Survival-Dataset

WMO-Hurricane-Survival-Dataset

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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It is estimated that 10,000 people die each year worldwide due to hurricanes and tropical storms. The majority of human deaths are caused by flooding. Hurricane Irma hit Florida as a Category 4 storm the morning of Sept. 10, 2017, ripping off roofs, flooding coastal cities, and knocking out power to more than people. The storm and its aftermath has killed at least 38 in the Caribbean, 34 in Florida, three in Georgia, four in South Carolina, and one in North Carolina. The occurrences of these natural disasters have been on a high which is a concern for United Nation; The World Meteorological Organization (specialized agency of UN) has been collecting data about all the individuals that are living in and around Hurricanes and Cyclones prone areas. In the aftermath of Irma, WMO wants to find a pattern or a relation between the attributes that will prove whether an individual will SURVIVE OR NOT SURVIVE any hurricane/cyclones in the near future. DATA DICTIONARY VARIABLES DESCRIPTION DOB Date of Birth(MM/DD/YYYY) MSTATUS Marital Status (Married/Unmarried/Divorced) SALARY Annual salary ( specified in Ranges) EDUDATA Education details ( Uneducated/High-School/Gradute / Post-Graduate) EMPDATA Employment details ( Employed/Self-Employed/unemployed) RELORIEN Religious orientation ( Agnostic / Atheist / Believer) FAVTV Favourite TV Show PREFCAR Preferred brand of car GENDER Gender( Male/Female/Other) FAVCUIS Favourite Cuisine FAVMUSIC Favourite Genre of Music ENDULEVEL Endurance Level FAVSPORT Favourite sport FAVCOLR Favourite color NEWSSOURCE Source of the news DISTFRMCOAST Distance from the coast MNTLYTRAVEL Monthly travel GENMOVIES Preferred Genre of Music FAVSUBJ Favourite subject ALCOHOL Preferred Alcohol FAVSUPERHERO Favourite Superhero Class x(will survive) and y(Will not survive)

23 features

Class (target)string2 unique values
0 missing
ID (ignore)numeric5020 unique values
1 missing
DOBstring4172 unique values
1 missing
M_STATUSstring3 unique values
24 missing
SALARYstring6 unique values
19 missing
EDU_DATAstring4 unique values
5 missing
EMP_DATAstring3 unique values
1 missing
REL_ORIENstring3 unique values
1 missing
FAV_TVstring10 unique values
1 missing
PREF_CARstring14 unique values
1 missing
GENDERstring3 unique values
17 missing
FAV_CUISstring9 unique values
1 missing
FAV_MUSICstring8 unique values
1 missing
ENDU_LEVELstring8 unique values
1 missing
FAV_SPORTstring8 unique values
1 missing
FAV_COLRstring9 unique values
1 missing
NEWS_SOURCEstring8 unique values
1 missing
DIST_FRM_COASTstring5 unique values
1 missing
MNTLY_TRAVELstring6 unique values
6 missing
GEN_MOVIESstring7 unique values
1 missing
FAV_SUBJstring10 unique values
1 missing
ALCOHOLstring7 unique values
4 missing
FAV_SUPERHEROstring7 unique values
1 missing
Dist_Coastnumeric1411 unique values
1 missing

19 properties

5021
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
91
Number of missing values in the dataset.
70
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
4.35
Percentage of numeric attributes.
51.46
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
2584
Number of instances belonging to the most frequent class.
48.54
Percentage of instances belonging to the least frequent class.
2437
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
1.39
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0.08
Percentage of missing values.

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