OnPoint Machine-Learning Ready Weather

A dataset by Weather Source
Pricing available upon request
Column Sample Another one Attribute
1 Fashion Sports, Health 25-49 Germany
2 Just Another Sample Another Row
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OnPoint Machine-Learning Ready Weather

Use Cases

Geography

Africa (58)
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cabo Verde
Cameroon
Central African Republic
Chad
Comoros
Congo
Congo (Democratic Republic of the)
Côte d'Ivoire
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mayotte
Morocco
Mozambique
Namibia
Niger
Nigeria
Réunion
Rwanda
Saint Helena, Ascension and Tristan da Cunha
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Swaziland
Tanzania, United Republic of
Togo
Tunisia
Uganda
Western Sahara
Zambia
Zimbabwe
Asia (51)
Afghanistan
Armenia
Azerbaijan
Bahrain
Bangladesh
Bhutan
Brunei Darussalam
Cambodia
China
Cyprus
Georgia
Hong Kong
India
Indonesia
Iran (Islamic Republic of)
Iraq
Israel
Japan
Jordan
Kazakhstan
Korea (Democratic People's Republic of)
Korea (Republic of)
Kuwait
Kyrgyzstan
Lao People's Democratic Republic
Lebanon
Macao
Malaysia
Maldives
Mongolia
Myanmar
Nepal
Oman
Pakistan
Palestine, State of
Philippines
Qatar
Saudi Arabia
Singapore
Sri Lanka
Syrian Arab Republic
Taiwan, Province of China
Tajikistan
Thailand
Timor-Leste
Turkey
Turkmenistan
United Arab Emirates
Uzbekistan
Vietnam
Yemen
Europe (51)
Åland Islands
Albania
Andorra
Austria
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Faroe Islands
Finland
France
Germany
Gibraltar
Greece
Guernsey
Holy See
Hungary
Iceland
Ireland
Isle of Man
Italy
Jersey
Latvia
Liechtenstein
Lithuania
Luxembourg
Macedonia (the former Yugoslav Republic of)
Malta
Moldova (Republic of)
Monaco
Montenegro
Netherlands
Norway
Poland
Portugal
Romania
Russian Federation
San Marino
Serbia
Slovakia
Slovenia
Spain
Svalbard and Jan Mayen
Sweden
Switzerland
Ukraine
United Kingdom
North America (13)
Belize
Bermuda
Canada
Costa Rica
El Salvador
Greenland
Guatemala
Honduras
Mexico
Nicaragua
Panama
Saint Pierre and Miquelon
United States of America
Oceania (25)
American Samoa
Australia
Cook Islands
Fiji
French Polynesia
Guam
Kiribati
Marshall Islands
Micronesia (Federated States of)
Nauru
New Caledonia
New Zealand
Niue
Norfolk Island
Northern Mariana Islands
Palau
Papua New Guinea
Pitcairn
Samoa
Solomon Islands
Tokelau
Tonga
Tuvalu
Vanuatu
Wallis and Futuna
Other (9)
Antarctica
Bouvet Island
British Indian Ocean Territory
Christmas Island
Cocos (Keeling) Islands
French Southern Territories
Heard Island and McDonald Islands
South Georgia and the South Sandwich Islands
United States Minor Outlying Islands
South America (42)
Anguilla
Antigua and Barbuda
Argentina
Aruba
Bahamas
Barbados
Bolivia (Plurinational State of)
Bonaire, Sint Eustatius and Saba
Brazil
Cayman Islands
Chile
Colombia
Cuba
Curaçao
Dominica
Dominican Republic
Ecuador
Falkland Islands (Malvinas)
French Guiana
Grenada
Guadeloupe
Guyana
Haiti
Jamaica
Martinique
Montserrat
Paraguay
Peru
Puerto Rico
Saint Barthélemy
Saint Kitts and Nevis
Saint Lucia
Saint Martin (French part)
Saint Vincent and the Grenadines
Sint Maarten (Dutch part)
Suriname
Trinidad and Tobago
Turks and Caicos Islands
Uruguay
Venezuela (Bolivarian Republic of)
Virgin Islands (British)
Virgin Islands (U.S.)

Categories

History

20 years of past data available

Product Description

Incorporating weather data into AI and ML workflows has historically been difficult because of varying weather values and the challenge of providing context for anomalies. OnPoint ML-Ready Weather offers a suite of datasets engineered for direct use in AI- and machine learning (ML)-based applications. OnPoint ML-Ready Weather, an extension of OnPoint Weather, employs feature engineering to create datasets that help organizations analyze the effects of specific weather conditions on a wide range of weather-sensitive activities. The ML-Ready datasets include: ML-Ready Cloud Cover ML-Ready Storm ML-Ready Temperature ML-Ready Wind For example, the ML-Ready Storm dataset provides a location-based time series of storm activity that reflects the variations of storminess at a location over time. This information includes the level of storminess as well as whether the level is above or below normal and by how much. Additional options include the ability to weigh workdays and non-workdays differently—e.g., theme parks are more financially sensitive to severe weather conditions on weekends.

Suitable Company Sizes

Small Business
Medium-sized Business
Enterprise

Pricing

Free sample available
5% discount if you buy via Datarade
Weather Source has not published pricing information for this product yet. You can request detailed pricing information below.

Quality

Self-reported by the provider
gap-free and error corrected

Delivery

Methods
S3 Bucket
SFTP
Email
UI Export
REST API
SOAP API
Streaming API
Feed API
Frequency
secondly
minutely
hourly
daily
weekly
monthly
quarterly
yearly
real-time
on-demand
Format
.bin
.json
.xml
.csv
.xls
.sql
.txt

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