Title
Hint Use CTRL+F to search this
list.
|
Size |
| Aanlysis on Boston Housing Data. |
63014 |
| AAPL data |
1766 |
| AAPL DATA |
614145 |
| aapl sg 1 year |
1766 |
| AAU VAP Trimodal People Segmentation Dataset |
1263885402 |
| Abalone |
191873 |
| Abalone Dataset |
196125 |
| AbaloneHotEncoded |
425308 |
| abbpp0 |
14291428 |
| abcdefg |
1635880 |
| abcdfe |
24064 |
| abchelloword |
6 |
| Abe_Shinzo_tweets |
321175 |
| About 60k Organization Information |
7664044 |
| Abseenteeism |
929911 |
| Absenteeism Dataset |
929911 |
| Academic Research from Indian Universities |
13163130 |
| Academic Scores for NCAA Athletic Programs |
1835179 |
| ACB 1994-2016 Spanish Basketball League Results |
25090048 |
| Accidents in India |
203003 |
| Accounting _Journals |
1927815 |
| ACLED African Conflicts, 1997-2017 |
62166877 |
| ACLED Asian Conflicts, 2015-2017 |
13625003 |
| Acorn Study: London Smart Meters Block_3 Only |
39024425 |
| ACS 2013 Wages |
57429033 |
| ACS Homes Year Built |
30313188 |
| ACS Shapefiles 2014 |
63223841 |
| Active Satellites in Orbit Around Earth |
344472 |
| Active Volcanoes in the Philippines |
1631 |
| activity_train |
12063966 |
| Acuweather data |
2650189 |
| add2test |
14610163 |
| added dataset sf dfsf fdf fds fdfs edf |
4593885 |
| added el pais tweets |
18548925 |
| Adding the sample submission to the RAOP |
18675 |
| Additional info for leukemia gene expression data |
14753124 |
| Additional Processed file churn prediction |
871920936 |
| Adelaide City Council Parking Expirations |
7737141 |
| adgjløøktg |
879 |
| Adience Benchmark Gender And Age Classification |
1322949093 |
| adj_close of 2 stocks in 2017 |
8325 |
| Adjective Counts in the Works of Edgar Allan Poe |
257002 |
| ADLCSV |
54087 |
| Administrative divisions of Moscow |
698130 |
| Admission |
3775 |
| Admob data set |
20341 |
| ads click |
492005809 |
| Ads from context advertising |
141015387 |
| Ads_dataset |
210050 |
| ADS-16 Computational Advertising Dataset |
790380560 |
| adult census |
478855 |
| Adult Census Income |
4104734 |
| Adult Census Income with AI |
19672538 |
| Adult Data Set |
5720607 |
| Adult income dataset |
5326368 |
| Adults |
479710 |
| adults |
3844216 |
| Adverse Food Events |
19721957 |
| Adverse Pharmaceuticals Events |
4737961793 |
| Advertisement |
107424 |
| Advertising |
107424 |
| Advertising |
107424 |
| Advertising |
107424 |
| Advertising and Predict Sales |
4756 |
| Advertising and Sales |
4756 |
| Advertising Data |
5166 |
| Advertising_Dummy |
107424 |
| Ae. aegypti and Ae. albopictus occurrences |
3406779 |
| Aegis Dataset |
221079 |
| Aerial Bombing Operations in World War II |
28483239 |
| Aeropress World Championship 2016 Recipe Data |
4004 |
| Aerosol ozone |
202743 |
| African bees dataset |
628443 |
| Ag Stuff |
5293 |
| ag_news |
11786319 |
| ag_news_csvs |
11798244 |
| ageGroupedCsv |
3565 |
| aggensemble |
17112439 |
| aggr dataset |
70589873 |
| aggr dataset |
70569969 |
| Agora Market Data JSONified (2014-2015) |
5868967 |
| Agricultural Survey of African Farm Households |
37921937 |
| Agricuture Crops Production In india |
104996 |
| AI-Challenger-Scene-Classification Dataset |
4417181869 |
| AI-Simulated Games of Machi Koro |
108536286 |
| AI2 Science Questions |
60617764 |
| AIC Logistic Model |
10576811 |
| Air Passengers |
1746 |
| Air pollutants measured in Seoul |
281689 |
| Air Quality Annual Summary |
994187783 |
| Air quality data from extensive network of sensors |
8530737 |
| Air quality in northern Taiwan |
18765395 |
| air_store_info_mod |
83501 |
| Air-Quality |
751049 |
| Airbag and other factors on Accident Fatalities |
2839783 |
| Airbnb dataset of barcelona city |
3017718 |
| Airbnb from insiderairbnb |
3337704 |
| Airbnb Property Data from Texas |
9350671 |
| Aircraft Accidents from 1908-2009 |
533951 |
| Aircraft Wildlife Strikes, 1990-2015 |
36443102 |
| Airline Database |
322026 |
| Airline Delay 2007 July Sample |
61687929 |
| Airline Delay Analysis |
589214 |
| Airline Fleets |
102267 |
| airline safety |
2265 |
| AirlineAirport |
24763 |
| Airlines |
102218 |
| Airlines Delay |
250323223 |
| Airlines Tweets Sentiments |
160763 |
| AirPassenger |
1746 |
| AirPassengers |
1746 |
| AirPassengers |
1746 |
| Airplane Crashes Since 1908 |
1595468 |
| Airport coordinates of flights - India |
17738 |
| Airport of next generation |
923087 |
| AirportList |
88020 |
| Airports, Train Stations, and Ferry Terminals |
1467576 |
| AirQuality |
785065 |
| airquality.csv |
3715 |
| aisles.csv |
2603 |
| Alaska Airport Data |
1076518 |
| Alc consumption and higher education |
113855 |
| alc_notitles |
954 |
| Alcohol and Drug Consumption of German Teens |
14139 |
| Alexa Skill Database |
368819 |
| Alexa Top 1 Million Sites |
10022849 |
| AlexNet |
226826326 |
| Algerian provinces by population |
1485 |
| algo_autre |
16169679 |
| Alice files |
59110781 |
| Alice In Wonderland GutenbergProject |
173595 |
| Alien PNG |
3830 |
| AliKAbeeel |
66 |
| All About Data Science |
813445 |
| All hospitals from webometrics |
780525 |
| All India Health Centres Directory |
20967450 |
| All Lending Club loan data |
370662409 |
| All Model Vehicles Years (1984 2017) |
16007284 |
| All Perish |
3262 |
| All Shark Tank (US) pitches & deals |
145852 |
| All the news |
669640768 |
| All UK Active Companies By SIC And Geolocated |
51931080 |
| All UK Active Company Names |
45000051 |
| All-Trans House Price Indx by Metro Area 2007 2015 |
7933 |
| allCSVfiles |
108755440 |
| Allen-Unger Global Commodity Prices |
31218324 |
| Alpha-Numeric Handwritten Dataset |
4814459 |
| Alpino Treebank |
21604821 |
| Alpino Treebank |
21604821 |
| Altair-BigMartdataset |
869537 |
| amazon |
5926077 |
| Amazon Access Dataset |
1942722 |
| Amazon baby dataset |
49439484 |
| amazon dataset RBL |
655131909 |
| Amazon Echo Dot 2 Reviews Dataset |
2459986 |
| Amazon Fine Food Reviews |
673703435 |
| Amazon Reviews |
155162134 |
| Amazon Reviews for Sentiment Analysis |
516929648 |
| Amazon reviews: Kindle Store Category |
278372938 |
| Amazon Reviews: Unlocked Mobile Phones |
131879567 |
| Amazon stock 2015 |
15235 |
| Amazon_review-full |
1219911 |
| Amazon.com_Employee Access Challenge |
1942722 |
| amazonv2 |
5926077 |
| ambassidorData |
32851 |
| Ambassodor |
112 |
| Ambiguity |
2229 |
| AMD and GOOGLE Stock Price |
237464 |
| Amending America |
5418159 |
| American Presidency Project |
343968592 |
| American Time Use Survey |
1643970845 |
| American University Data IPEDS dataset |
1240380 |
| Ames dataset |
912081 |
| Ames Housing Prices |
963738 |
| AMJ Metadata |
1423350 |
| amount of vehicles in Beijing |
152064 |
| AMran Marib KAP, WASH |
10806 |
| An Open Dataset for Human Activity Analysis |
454701982 |
| Analisis Dataset SO2 de Chimenea de planta |
1646338 |
| Analysis about crypto currencies and Stock Index |
681413 |
| Analysis Bay Area Bike Share Udacity |
641057 |
| Analysis on survival of life in titanic |
2843 |
| analytics |
61194 |
| analytics |
1397246 |
| Analytics_102 .. |
1397246 |
| analytics_dataset |
1397246 |
| Analytics102 |
1395830 |
| Analytics102Solution Dataset |
1397246 |
| anchal |
451405 |
| AND OR XOR |
78 |
| Andover Text |
1343 |
| android |
569254 |
| andy harless's models stack |
10426904 |
| Animal Bites |
691381 |
| animated test |
89404 |
| animated vs realistic |
300386 |
| Anime Recommendations Database |
112341362 |
| anime-ord |
1011124 |
| anime-utf8 |
1012559 |
| AnimeData |
26881506 |
| Anna University - Results Dataset |
29933943 |
| Anna University results May-June 2016 |
29946624 |
| annaunivclg |
9001 |
| anneng123 |
2614480 |
| Annotated Corpus for Named Entity Recognition |
172238510 |
| Annotated Corpus for Named Entity Recognition |
2317034 |
| Annual Nominal Fish Catches |
4385944 |
| anonymous_survey |
121107 |
| another |
1229 |
| anotherdatabase |
15278727 |
| Antimicrobial resistance - dataviz2015 |
56331 |
| AP Computer Science A Exam Dataset |
30677 |
| Apartment data |
1043072 |
| apj.jpeg |
216218 |
| app-price |
463538 |
| Apple Stock Prices from 2010-2017 |
232757 |
| Apple_Stock_price |
1617 |
| applestocks |
434879 |
| Appliances Energy Prediction |
11979363 |
| Appoints |
10850022 |
| APRIL_LSTM_SVR_GP_v2 |
2031592 |
| aps_example |
8789291 |
| Arabic - Egyptian comparable Wikipedia corpus |
276666788 |
| Arabic Handwritten Characters Dataset |
76616506 |
| Arabic Handwritten Digits Dataset |
259116071 |
| Arabic Natural Audio Dataset |
586942737 |
| arabic_tweets_vs_dialects |
226281 |
| Archive |
7549 |
| Archived_SmartMeter_Data |
59878235 |
| Area and Geography |
504712 |
| Argentina's Private Neighborhoods |
174189 |
| Aristo MINI Corpus |
103465506 |
| Armenian Online Job Postings |
96790782 |
| Armenian Pub Survey |
33208 |
| Armors, Exoskeletons & Mecchas |
43759 |
| Array of objects with two fields |
136 |
| Array of recipes |
12415067 |
| Arrest Related Violence in California |
20950056 |
| Arrests by Baltimore Police Department |
19915966 |
| Article Titles from TechCrunch and VentureBeat |
1397372 |
| articles |
26442972 |
| Articles from wikipedia |
1426478 |
| Articles sharing and reading from CI&T DeskDrop |
29942455 |
| Arxiv Astrophysics Collaboration Network |
10568976 |
| ARXIV data from 24,000+ papers |
5913087 |
| As 500 empresas que mais devem a previdencia |
39969 |
| asdadda |
514556 |
| asdasdasd |
8 |
| asdf 3456 e3d4f5 |
13593165 |
| asdf_v1 |
7974978 |
| asdfgdfghjk |
668 |
| asdfghjk |
454 |
| asdfghjkasdfghjk |
127723 |
| asdfghjklæø |
454 |
| asdfsds |
6547834 |
| Asian American Actors |
3129 |
| Asian.csv |
5450 |
| asian123.csv |
5450 |
| ASII 5 years |
171479 |
| ASII.jk |
229716 |
| AskDocs Posts |
222635 |
| assign |
18245 |
| Assignment 8 |
61194 |
| assignment1 |
752137 |
| Association of Tennis Professionals Matches |
11898898 |
| Association Rules |
301359 |
| Astronomy |
99358 |
| ASX Australia Equity Prices - 1997 to 2016 |
247367064 |
| Atlas of Pidgin and Creole Language Structures |
1564943 |
| ATM Transaction Data of City Union Bank |
847136 |
| Atom Dataset |
0 |
| ATP Matches, 1968 to 2017 |
33004965 |
| ATP Men's Tour |
9471729 |
| ATP Tennis Dataset |
2579313 |
| attempt2 |
17101377 |
| attractions |
740220 |
| attrition de clientes |
13087604 |
| Attrition Example |
235331 |
| attrition-csv |
3111278 |
| ATUS Data 2015 (Exercise Portion) |
766632 |
| ATVICSV |
158860 |
| atviprice |
201 |
| ATVIStockPrice |
158860 |
| Audio Cats and Dogs |
61536433 |
| Audio Features for Playlist Creation |
683544 |
| Audio features of songs ranging from 1922 to 2011 |
443424016 |
| audioa |
192264 |
| aug_data |
55285 |
| augment data |
31364 |
| Austin 311 Calls |
171303265 |
| Austin Bike Share Trips |
87346478 |
| Austin Crime Statistics |
19419689 |
| Austin Waste and Diversion |
63489783 |
| Austin Weather |
105734 |
| Austin Zoning Satellite Images |
596268822 |
| Australia NSW traffic penalty data 2011-2017 |
9060956 |
| Australian Broadcasting Commission |
4054966 |
| Australian Domestic Airline Traffic |
1332539 |
| Australian Football League Database |
7892768 |
| Australian Marriage Law Postal Survey |
300680 |
| Australian National University Courses |
740058 |
| Author Disambiguation |
24222133 |
| author_train |
1345945 |
| AuthorIdentification |
628563 |
| Auto Insurance in Sweden |
940 |
| Auto Insurance in Sweden (small dataset) |
765 |
| Auto MPG Data Set |
30286 |
| Auto-Mpg Data |
14080 |
| Auto-mpg dataset |
18131 |
| auto-price-train-data |
134964916 |
| AutoAssign |
18428 |
| automateassignment |
18070 |
| Automatic generation of Guard roles |
193223 |
| Automobile Dataset |
25070 |
| automobiles |
18131 |
| autompg |
19944 |
| autompg |
32149 |
| Autos - Consumo Gasolina Mexico |
371902 |
| Autos_Edited |
28673796 |
| AV datahack |
802079 |
| AV_8jan |
1270747 |
| AV_bank_cross_sell |
344589708 |
| av_cross_sell_train_data |
206958153 |
| av_hack |
387701398 |
| AV_hiring |
802079 |
| AV_Mckinskey |
700124 |
| av_vala |
387701398 |
| Average Fuel Consumption |
3722 |
| Average SAT Scores for NYC Public Schools |
81172 |
| Average Sun Spot Number |
5850 |
| Averaged Perceptron Tagger |
6138625 |
| AvgHappinessScore |
11903 |
| AvgHappyscore |
11903 |
| avglgmxgb |
1148751 |
| Aviation Accident Database & Synopses |
3908294 |
| awefwrgwfewefwe |
12131320 |
| Awesome Public Datasets as Neo4j Graph |
2956968 |
| AWS Spot Pricing Market |
1815291461 |
| Azerbaijan Voter List, 2016 |
739089516 |
| B6266B |
465754 |
| Baboon Mating and Genetic Admixture |
1573631 |
| Baby girl breast feeds |
199511 |
| Baby Photos |
116207 |
| Bach Chorales Data Set |
304998 |
| BachelorsDegreeWomenUSA |
5681 |
| Bad teeth, sugar and government health spending |
311044 |
| Bad words |
3477 |
| BADM_dataset |
3807560 |
| BadWordsGoole |
1474 |
| Bag of word meets bag of popcorn |
27246077 |
| Bag of Words Meets Bags of Popcorn |
54896086 |
| Bag of Words Meets Bags of Popcorn |
33556378 |
| Bag of Words Meets Bags of Popcorn unlabeled |
27649993 |
| Bag of Words Meets Bags of Popcorn: Data |
54896086 |
| Bagging |
18196682 |
| Bagrut grades in Israeli high schools (2013-2016) |
609824 |
| Baltimore 911 Calls |
295690533 |
| Baltimore 911 Calls For Service 2015- late 2017 |
64663547 |
| Baltimore 911 Calls for Service, 2015-2017 |
212145583 |
| Banco Imobiliário |
4117 |
| bancos |
134945 |
| Bancos |
248022779 |
| Bandwidth occupancy |
10643913 |
| Bangalore_Cell_ORR |
814652 |
| Bank Account Movements 01-01-2017 to 08-11-2017 |
69097 |
| Bank Churn Modelling |
684858 |
| Bank Fears Loanliness |
66100489 |
| Bank Loan Status Dataset |
20589209 |
| Bank Marketing |
461474 |
| bank marketing |
918960 |
| Bank Marketing |
918960 |
| Bank Marketing Dataset |
461474 |
| Bank Marketing Dataset |
918960 |
| Bank Marketing-Dataset |
465338 |
| Bank Markting Dataset Description |
3864 |
| bank notes |
45088 |
| Bank Telemarketing (moro et al.) |
489118 |
| Bank_Loan_data |
696953 |
| bankdata |
133638 |
| BankProject |
239185824 |
| Banks data |
687440 |
| Barcelona Accidents |
10518158 |
| Barcelona Accidents |
17866759 |
| Barcelona Unemployment |
41655 |
| Barclays Premier League Games Won 2010-16 |
858 |
| Base de dados de testes |
15737 |
| BASE DE DATOS |
380152 |
| base de teste |
143736 |
| base model |
14133049 |
| base_sin |
23247018 |
| base-weights |
22843928 |
| Baseball |
848362 |
| Baseball Data |
13289647 |
| Baseball Databank |
24711821 |
| Baseball_stats_LR_avg |
499829 |
| BaseballData-JohnKruschke |
33913 |
| baseballfield |
5138872 |
| Baseline |
28698153 |
| Baseline |
3258 |
| baseline |
74007502 |
| Baseline Results |
7237183 |
| baseline_ru_ |
7270173 |
| baseline_weight_toxic |
22786953 |
| baseline-script2 |
55512 |
| Basemanp |
2284 |
| Basemap |
1736475 |
| Basemap |
1796170 |
| Basemaps |
1796170 |
| Basic Classification Example with TensorFlow |
150 |
| Basic Computer Data |
296595 |
| Basic Income Survey - 2016 European Dataset |
3602700 |
| BasinCharacteristic_v1 |
10951 |
| Basket Ball Computer Vision |
8562527 |
| Basket Optimisation |
302908 |
| BasketBallShots |
12046 |
| BasketBallShotsLog |
870 |
| batches_meta_for_CFAR10 |
158 |
| Baton Rouge Crime Incidents |
69410481 |
| Baymax_test |
4979247939 |
| Baymax_train |
7464855374 |
| bbbbbb |
4072076 |
| bbbbbb |
19576768 |
| BBK Deep Learning lab trained weights |
2465781 |
| BBK Lab models |
7286507 |
| BBVA data challenge |
3111232 |
| BC-testing |
4967 |
| BCGENES |
59511 |
| BCtest3names |
5241 |
| Bctest4 |
58121 |
| Bctest4e |
1959 |
| BCtestEval3 |
989 |
| BCtesting2 |
4967 |
| BCtesting3 |
5035 |
| BCtesting3eval |
948 |
| BCtestingVal |
880 |
| BD_digits2017 |
8876037 |
| Beat The Bookie: Odds Series Football Dataset |
88433374 |
| Beautiful_Liar |
668613 |
| BeeSensors |
127622 |
| BeeSensorsTime |
152275 |
| Beginner Projects - Analyse subtitles for a movie |
8167871 |
| Beginner Projects - Ergonomic Study on Chopsticks |
2590 |
| Beginner Projects - P03 - Data Wrangling |
39879524 |
| Beginners |
460676 |
| Beginners_test |
451405 |
| Behavioral Risk Factor Surveillance System |
2879064925 |
| Beijing PM2.5 concentration |
759218 |
| Beijing PM2.5 Data Data Set |
2010494 |
| Bellwether Project 3 dataset |
35627627 |
| Ben Hamner's Tweets |
809545 |
| Ben's training dataset |
38013 |
| Benchmark |
9914219 |
| Bengali Digit Recognition in the Wild (BDRW) |
1460338 |
| Benz data |
75220 |
| best single model tested |
206347 |
| Bestseller books on Paytm |
2483706 |
| bestz3 |
4072076 |
| Betfair.com Market Analysis |
31376 |
| beth_20180112_3 |
4905469 |
| beth_20180113 |
4917916 |
| beth_20180116 |
4900805 |
| beth_20180116_1 |
4913642 |
| beth20180111 |
4710144 |
| beth20180111_2 |
4710152 |
| beth20180112 |
4771899 |
| beth20180112_2 |
4913555 |
| Better Life Index 2017 |
461364 |
| Better Life Index and Gross Domestic Product |
441253 |
| (Better) - Donald Trump Tweets! |
1703362 |
| Between Our Worlds: An Anime Ontology |
101255372 |
| BFRO Bigfoot Sighting Report |
510758 |
| Bi-LSTM Glove Toxic |
14303674 |
| bi-sep-2d |
2150 |
| Bias Media CAT |
75828094 |
| Bible Corpus |
448027096 |
| Bible Verses from King James Version |
5130834 |
| Big Bash Dataset(till 2017) |
6304660 |
| Big Data courses in chennai |
744359 |
| Big mart sales |
1397246 |
| BIG MART sales |
1397246 |
| BIG MART SALES PREDICTION |
1603339 |
| big_data |
113183 |
| bigavg |
5734230 |
| BigBangTweets |
11929 |
| Bigdata |
839 |
| bigdata |
13010289 |
| BigMart |
869537 |
| BigMart Dataset |
1397246 |
| Bike July & August |
29179 |
| Bike Share Daily Data |
1214305 |
| Bike Share Data |
3155333 |
| BikeShare Analysis |
12745432 |
| Billboard 1964-2015 Songs + Lyrics |
7953541 |
| billboard-exercise |
90190 |
| billion word imputation |
1791536775 |
| BinarClass |
106113693 |
| Binary 100 iv3 |
13884138 |
| binary 100 iv3 299 |
14123531 |
| Binary 2D Points |
2150 |
| binary CD 3956vs3954 iv3 224 |
21593750 |
| Binary_100-iv3 |
13884138 |
| Binary_CD11 |
73306495 |
| Binary_iv3_100 |
13884138 |
| Binary-100-inceptionV3 |
12191155 |
| Binary-100-iv3 |
12191155 |
| Binary-incemptionv3-100 |
13884138 |
| Bioassay Datasets |
225816146 |
| Biocreative PPI |
1537086 |
| Biodiversity in National Parks |
17505172 |
| Biogas Datafile |
1580 |
| Biomechanical features of orthopedic patients |
51144 |
| Bird Strikes |
9711657 |
| Birds' Bones and Living Habits |
25520 |
| Births in U.S 1994 to 2003 |
64494 |
| BITCOIN |
230935 |
| Bitcoin (USD) Price |
76441 |
| Bitcoin & Altcoins in 2017 |
827218 |
| Bitcoin CZK/USD 2017 12 07 |
281400 |
| Bitcoin Historical Data |
125130895 |
| Bitcoin historical price |
28645 |
| bitcoin merged |
111786 |
| Bitcoin Price over the years |
44854 |
| Bitcoin Price Prediction (LightWeight CSV) |
111826 |
| BitCoin stuff |
4967 |
| bitcoin twitter |
1236482 |
| Bitcoin twitter |
1236558 |
| Bitcoin Twitter Feed |
1236398 |
| Bitcoin Vericoin dataset (Poloniex + Mosquito) |
54084383 |
| Bitcoin_PriceMovement |
40131 |
| bitcoin_prices_coinbase_USD |
47846994 |
| bitcoin-pic |
22767 |
| Bitcoin,Etherium,Litecoin Exchange Price |
181778 |
| 'Bitcoin' volume on Google |
3327 |
| BitcoinData |
1686 |
| bitcoinData |
120208 |
| BitcoinData2 |
1499 |
| BitcoinData3 |
1413 |
| Bitfinex hourly BTCUSD |
2619778 |
| Biticoin Enigma |
40131 |
| Biticoin Kernel |
40131 |
| Biticoin price Movement |
40131 |
| Biticoin Price Movement over the years |
40131 |
| BIXI Montreal (public bicycle sharing system) |
174436124 |
| blaaaa |
4072081 |
| Blabla |
41096944 |
| blabla |
203 |
| Black Friday |
7870870 |
| Blend 1 |
7913980 |
| Blend 1 5 |
9334493 |
| Blend 1_1 |
7946488 |
| Blend 1_2 |
7946488 |
| Blend sub 2 |
7954022 |
| Blend1 |
7946488 |
| BLLIP Parser Model |
54298623 |
| Blog Authorship Corpus |
800419647 |
| Blood Cells |
306982020 |
| Blood donation in Brazil |
22258 |
| Blue Plaques |
26942816 |
| bmax2017 |
4082655 |
| BMTC data set for device id 150813052 |
7954109 |
| BMTC_data |
7954109 |
| Boa-png-title |
0 |
| Board Game Data |
1490899 |
| board games |
51277028 |
| Board Games Dataset |
147056640 |
| boats1 |
104475 |
| Body measurements |
58818 |
| body zones TSA |
269994 |
| Body Zones TSA |
269994 |
| BonCoin |
784851 |
| book_len |
43907601 |
| Border_collie_stylized |
1413276 |
| Boris/Santander Bikes London |
401920 |
| Boston |
44575 |
| Boston 311 non-emergency data 2015 |
18540424 |
| Boston 311 non-emergency service data 2015 |
18540424 |
| Boston Airbnb Open Data |
75598461 |
| Boston Celtics Roster Data 14-15 |
915 |
| boston Dataset |
45082 |
| Boston House Prices |
49082 |
| Boston Housing |
35883 |
| Boston Housing |
12925 |
| Boston Housing |
45082 |
| Boston Housing |
12435 |
| Boston housing dataset |
35008 |
| Boston_housing |
626120 |
| bottleneck features inception/xception |
23370804 |
| bottleneck_features |
152597337 |
| BoW Test data |
32724746 |
| boxdata |
35423835 |
| boxplot |
255187 |
| boxp ot |
419365 |
| BR on Sep 2017 |
19524 |
| brain_body |
1258 |
| brain_body1 |
1258 |
| Brainwave |
989899 |
| brainwave-1 |
845939 |
| Brainwaves-2 |
39652204 |
| Brainwaves2018_Hackathon_Q2_FraudulentTransactions |
39652204 |
| Brand Characteristics |
1093505 |
| Brazil Elections 2014 |
30224279 |
| Brazil Gdp & Electricity Consumption |
1153 |
| brazil_chambers_of_deputies_2015_2017 |
32595274 |
| Brazil's House of Deputies Reimbursements |
412860343 |
| Brazil's House of Deputy Refunds |
333806963 |
| Brazil's Parliamentary Quota - Cota Parlamentar |
80335104 |
| Brazilian Aeronautics Accidents |
629609 |
| Brazilian Coins |
410011522 |
| Brazilian congress |
121196414 |
| Brazilian Federal Legislative activity |
55316944 |
| Brazilian Motor Insurance Market |
1853678 |
| Brazilian National Congress' open data - 2016 |
8306780 |
| Brazilian Portuguese Literature Corpus |
23629080 |
| Brazillian Sexual Gender |
19978527 |
| Breakaway |
7598 |
| Breakdown of Titanic Passengers by Class |
99663 |
| BreasCance Predication |
125773 |
| breast cancer |
125204 |
| Breast Cancer (Diagnosis) Wisconsin Data Set |
125204 |
| breast cancer dataset |
19889 |
| Breast Cancer Dataset |
125141 |
| Breast Cancer Dataset |
14552 |
| Breast Cancer Excercise |
927975 |
| Breast Cancer Proteomes |
12440701 |
| Breast Cancer Wisconsin |
125141 |
| Breast Cancer Wisconsin - Data Set |
125773 |
| Breast Cancer Wisconsin (Diagnostic) Data Set |
125773 |
| Breast Cancer Wisconsin (Diagnostic) Data Set |
125204 |
| Breast Cancer Wisconsin (Prognostic) Data Set |
125204 |
| Breast Histology Images |
41647052 |
| Breast Histopathology Images |
1644892042 |
| Breast_Cancer_Prediction |
757015 |
| Breast-Cancer Diagnosis |
124103 |
| Breast-Cancer Wisconsin |
20723 |
| breast-cancer_fixed |
20028 |
| breastcanceranalysis |
125204 |
| BreastCancerDataset |
125204 |
| breastdata |
125204 |
| Breathing Data from a Chest Belt |
56598 |
| Breweries & Brew Pubs in the USA |
23206956 |
| BRFSS 2001-2010 |
3995416294 |
| Brighter Monday Job Listings |
131585 |
| BRIS_SOL |
4598266 |
| bris_solar |
4598215 |
| BRIS3solar |
1004626 |
| BRIS4solar |
978902 |
| bris5soalr |
978902 |
| bris6solar |
978902 |
| Brisbane-solar |
16114172 |
| British Birdsong Dataset |
664058938 |
| British Queen's Oversea Visits |
44580 |
| Brown Corpus |
3314357 |
| Brown_corpus |
2311316 |
| BSETestingData |
394601 |
| btc test dataset |
254 |
| btc train dataset |
120314 |
| BTC-daily-to-2017-12-22 |
175131 |
| BTC-predict-daily-direction-exchange-rate |
4520696 |
| BTC2-echange-rate |
799402 |
| btc2data |
800397 |
| btc2data2 |
1599799 |
| BTCUSDKRAKEN |
113646 |
| buddha |
156888 |
| Buenos Aires public WiFi access points |
1468312 |
| Bug Triaging |
3331086 |
| Bugcr1 |
642961 |
| Build Bridges, Not Walls |
312885086 |
| Building Management System Analysis |
6837022 |
| Buildings in Vyronas, Athens |
80606350 |
| Burritos in San Diego |
68238 |
| Bus Breakdown and Delays NYC |
34426888 |
| Busiest Airports by Passenger Traffic |
38193 |
| Business and Industry Reports |
42122543 |
| buxbuxx |
4053304 |
| C Core train data set |
61145796 |
| c++ output |
3928163 |
| C++ ROCKET SIMULATION |
26602 |
| C++ submission |
4080826 |
| calc_case_description_train_set .csv |
925310 |
| CalCOFI |
269479923 |
| calendars |
3530 |
| calhousingclean |
956962 |
| California cities dataset |
68212 |
| California Crime and Law Enforcement |
100594 |
| California DDS Expenditures |
41947 |
| California Electricity Capacity |
16677076 |
| California Facilities Pollutant Emissions Data |
1289427 |
| California Housing |
1423529 |
| California Housing Prices |
409342 |
| California Housing Prices |
1423529 |
| California Kindergarten Immunization Rates |
7615380 |
| California Wire Tapping |
16254187 |
| Call Tests Measurements for MOS prediction |
5849559 |
| cambridge_net |
41367 |
| cambridge_net_titles |
36703 |
| camera_dataset |
86961 |
| Campaign Finance versus Election Results |
608167 |
| Can You Predict Product Backorders? |
140115122 |
| Canada National Justice Survey 2016 |
4510407 |
| Canadian Car Accidents 1994-2014 |
369929843 |
| Canadian Disaster Database |
2413370 |
| Cancer Data 2017 |
2859 |
| Cancer Inhibitors |
1997120952 |
| Cancer inhibitors cdk2 protein |
30978162 |
| cancer_test |
6144 |
| cancer_train |
25762 |
| cancerdata |
119889 |
| Cannabis Strains |
424888 |
| capital-tpdatos |
228297591 |
| Captcha Images |
2108196 |
| Car brands (1970 to 2016) |
683595 |
| Car Emissions data |
821983 |
| Car Evaluation |
51867 |
| Car Evaluation Data Set |
53593 |
| Car Features and MSRP |
1475504 |
| Car Insurance |
31424312 |
| Car Insurance Cold Calls |
974312 |
| Car Mileage |
1783 |
| Car Sale Advertisements |
538237 |
| Car sales |
16018 |
| Car sales |
399 |
| Car trips data log |
274521770 |
| Car_sales |
16774 |
| Car_sales.csv |
16774 |
| Caravan Insurance |
1712632 |
| Caravan Insurance Challenge |
1762896 |
| Caravana : Dont get Kicked |
14487324 |
| Carbon Dioxide Levels in Atmosphere |
31974 |
| Carbon Emissions |
627195 |
| Carbon Monoxide Daily Summary |
2289514800 |
| card_glm |
2126191 |
| Cardset |
2862995 |
| CargoDataCsv |
287859225 |
| Cars Data |
37716 |
| Cars Data |
8724 |
| Carsales |
12017 |
| CART, RF train and test datasets |
1075370 |
| CartolaFC |
8880984 |
| Case Data from San Francisco 311 |
666969758 |
| caseData |
8440826 |
| Cat Image Test |
190857 |
| CAT Scan Localization |
81517067 |
| cat vs dog |
854597350 |
| cat_17 |
17081526 |
| cat_out |
17105052 |
| Catalonia GDP by demand components (2000-2016) |
1973 |
| catboost |
17078300 |
| catboost data |
20650662 |
| Catboost_best_1 |
25324 |
| catboost-porto |
4931645 |
| catboost1223 |
8224176 |
| catboost122302 |
7913283 |
| catboost122303 |
7886839 |
| catboost1224 |
7939814 |
| catboost122402 |
7904888 |
| categories |
441411 |
| Categories |
416158 |
| categories |
53079 |
| Caterpillar Tube Pricing Dataset |
2781029 |
| catndog |
24007391 |
| catndog |
24007391 |
| Catndog |
45441084 |
| Cats and Dogs |
870693599 |
| Cats Versus Dogs |
65851768 |
| Cats Vs Dogos |
284321224 |
| cats_vs_dogs |
17944920 |
| cats&dogs |
227731756 |
| CatsNDogs mini |
231180273 |
| CatVsDogPKLfile |
615497712 |
| CAUSES OF DEATH IN THE WORLD 2014 |
15495023 |
| CCAA_pupulation |
32846 |
| cccccc |
1464646 |
| ccfant |
423245510 |
| CD_11_100_iv3_224 |
73306495 |
| CD11 100 iv3 224 |
73306495 |
| CD11 100 iv3 224 |
73306495 |
| Cdbc 300 iv3 180 1stImg |
7724015 |
| CDbc 300 iv3 224 |
13727516 |
| cdbc-1000ts-iv3-180-1stimg |
25757753 |
| CDbc300iv3-180-1stimg |
7724015 |
| CDC 500 Cities |
586177 |
| Cdiscount image classification submission samples |
732 |
| cdiscount_data |
631150 |
| CdiscountDataset |
7856670 |
| Celebrity Deaths |
2153226 |
| Celebrity Tweets |
441218 |
| Census |
478999 |
| Census data |
3448964 |
| Census Income Dataset |
667697 |
| Census India 2011 |
5663455 |
| census USA |
1019724 |
| censusdata |
317951 |
| Centers for Medicare & Medicaid Service Area Data |
5910190 |
| ceral analysis |
14409 |
| ceral_ |
14409 |
| cereal |
5157 |
| cereal dataset |
5063 |
| cereals dataset |
5063 |
| Cervical Cancer Risk Classification |
102059 |
| Cervical cancer tumor vs matched control |
108016 |
| CESS Treebanks |
7617080 |
| cfnai real time data |
5268348 |
| Chacha ami! |
18340 |
| Challenge : Day2 |
3195 |
| Challenge Data |
4722734 |
| Challenge day 1 |
7441975 |
| challenge_output_data_training |
93423 |
| Chance |
9292156 |
| Chance the Rapper Lyrics |
69613 |
| changed |
1025240 |
| Chapter2 |
2099 |
| chapter3-python |
789 |
| chapters |
557939 |
| char_num_dataset |
5670 |
| Character Encoding Examples |
970146 |
| Charguana |
364464 |
| #Charlottesville on Twitter |
186136781 |
| Chase Bank Branch Deposits, 2010-2016 |
975562 |
| Chat 80 |
63817 |
| Chat messages |
124927221 |
| Check 3x3 Sudoku is Valid |
719514 |
| Cheltenham Crime Data |
1053273 |
| Cheltenham's Facebook Groups |
61610188 |
| Chemical Health Effects and Toxicities |
1505141 |
| Chemical Substance Registry (CAS registry numbers) |
9752891 |
| Chennai Bus Route Data |
58992 |
| Chennai Bus Route Dataset |
59085 |
| chennai house pricing |
1270747 |
| Chess Black Wins |
1904072 |
| Chess Game Dataset (Lichess) |
7672655 |
| chestxraytest |
4979445554 |
| chestxraytrain |
7465411423 |
| Chewable |
5691 |
| chi-sqare |
31025 |
| Chicago - Citywide Payroll Data |
2269754 |
| Chicago census data by community area |
5709 |
| Chicago Crime |
376322518 |
| Chicago Crime Data |
15613459 |
| Chicago Red Light Violations |
48686731 |
| Chicago Restaurant Inspections |
184756352 |
| Chicago Taxi Rides 2016 |
2172282078 |
| Chicago Towing Records |
423464 |
| chicago_weather |
2864907 |
| Chicken |
1617 |
| chicks |
717 |
| Childhood Blood Lead Surveillance |
174623 |
| Chile Presidential Debate |
128810 |
| China RDP |
496782 |
| China RDP 2 |
496780 |
| China RDP v2 |
496780 |
| China RDPv2 |
496780 |
| Chinese Characters Generator |
209080186 |
| Chinese Delivery Drive |
58161 |
| Chinese Stocks |
224023 |
| chipotle |
364975 |
| Chipotle |
364975 |
| Chocolate Bar Ratings |
127723 |
| Choosing the best Feature |
63014 |
| Chosen ones |
1063529 |
| chris' face |
156004 |
| Christmas Tweets |
73584129 |
| Chronic Disease Indicators |
122899180 |
| Chronic illness: symptoms, treatments and triggers |
140920255 |
| Chronic KIdney Disease dataset |
48551 |
| Church Reuse Inventory |
85784 |
| churn classfication |
506359 |
| Churn datasets |
684858 |
| Churn in Telecom's dataset |
310007 |
| churn_ |
2191057242 |
| Churn_Basic |
635954 |
| Churn_Modelling |
684858 |
| Churned Users |
7439338 |
| churnModel |
684858 |
| churnTest |
51260725 |
| churnTrain |
53317710 |
| Cifar-10 |
170062354 |
| CIFAR10 |
169672749 |
| cifar10 |
170062600 |
| Cifar10 |
170062354 |
| cifar10 |
170062600 |
| cifar10 |
134 |
| cifar10 |
186213868 |
| CIFAR10 |
186213868 |
| cifarData |
141720199 |
| cifer 10 |
170550174 |
| Circadian Rhythm in the Brain |
1132336889 |
| City & Country |
79768 |
| City Database |
4096 |
| City Lines |
2547825 |
| City of Baltimore |
2804 |
| City of Baltimore Map |
2804 |
| City Payroll Data |
93050081 |
| Claim Close Gap |
52644037 |
| Claim Close Gap Prediction |
52644037 |
| Claims Data |
17545298 |
| Claims data_1 |
29407903 |
| ClaimsData |
17087291 |
| Clap Emoji in Tweets |
729749 |
| Clash royale Dataset |
4996 |
| Clash Royale Matches |
415441375 |
| Clásicos del fútbol Argentino |
54405 |
| class_order |
104653 |
| Class3a |
853 |
| Class4 |
949 |
| Class4B |
949 |
| Class4d |
949 |
| Classic Literature in ASCII |
129967536 |
| classification |
30775109 |
| Classification of Handwritten Letters |
76027645 |
| Classification of Student Evaluation data |
391968 |
| Classification_tutorial |
47370969 |
| Classified Ads for Cars |
419466302 |
| classified data |
194323 |
| classifier |
31679 |
| Classifying wine varieties |
10958 |
| classPredictions |
8164154 |
| clean_text |
52792929 |
| Cleaned lingerie data from different brands |
321457 |
| cleaned sentiment140 - not stemmed |
38974721 |
| Cleaned version of multipleChoiceResponses |
325692 |
| Cleaned Weather Dataset |
212218 |
| cleaned_ner_ds |
2679160 |
| cleaned_senitment140 |
9058119 |
| cleanTest |
29336 |
| cleanTrain |
59411 |
| cleanTrain |
59411 |
| Cleveland Cavaliers |
4494 |
| clf2_new |
142132 |
| click_here |
6347752 |
| Climate Change: Earth Surface Temperature Data |
600625277 |
| ClimateData |
3763 |
| Clinical |
3026295 |
| Clinical Trial data |
3026295 |
| Clinical Trials on Cancer |
186114041 |
| Clinical, Anthropometric & Bio-Chemical Survey |
335503326 |
| Cloth folding videos |
247633399 |
| clubName |
842879 |
| cluster_labels |
13769188 |
| clustering_basins |
6243 |
| Clustering_Excercise |
885172 |
| Clustering_Excercise2 |
6173 |
| CM_MATRIX |
916674 |
| CMAX applied to BRIC stock markets index |
10358 |
| CMP data set |
12670 |
| CMS Open Payments Dataset 2013 |
2470335100 |
| CMU Book Summary Dataset |
16815835 |
| CMU Dictionary |
3824638 |
| CMU Pronouncing Dictionary |
3618062 |
| cnn_18.18 |
240755 |
| cnn-text-classification-tf |
1238901 |
| CO2 PPM - Trends in Atmospheric Carbon Dioxide |
31745 |
| CO2-Emissions |
594201 |
| Coal Production Referenced from data.gov.in |
35225 |
| cobaaniris |
5107 |
| Cocacola en Youtube |
145943 |
| Cocktail Ingredients |
213123 |
| Code Mixed (Hindi-English) Dataset |
161013001 |
| Code of Federal Regulations |
351797510 |
| Code_echantillon |
1933 |
| Codechef Competitive Programming |
1263797927 |
| Coffee Drinking |
77 |
| Coffee Growing Countries |
16272 |
| Cognitive childs and their mothers |
11237 |
| coin_price |
1731985 |
| Colbert 1k |
4471428 |
| Coles and Woolworths Prices |
1048 |
| College Football Statistics |
33947123 |
| College Football/Basketball/Baseball Rankings |
60576679 |
| College life Missuri Institute |
11253 |
| College Scorecard Data 2007 2008 |
130651899 |
| College Scorecard Data 2008 2009 |
130555114 |
| College Scorecard Data 2009 2010 |
135807805 |
| College Scorecard Data 2010 2011 |
138657071 |
| College Scorecard Data 2011 2012 |
145137728 |
| College Scorecard Data 2012 2013 |
147074999 |
| College Scorecard Data 2013 2014 |
145836006 |
| Colombian Coffee 2016 |
125272 |
| Colonia Corpus of Historical Portuguese |
79996432 |
| Color terms dataset |
5401 |
| color_image |
170062512 |
| color1_image |
170062512 |
| Colorado Shelter Euthanasia Animation DB |
384812 |
| Column label |
703 |
| Column labels |
705 |
| combination1 |
537890 |
| combine |
314968789 |
| combined wine data |
448109 |
| Combined_candy_usip |
15706 |
| COMBO-17 Galaxy Dataset |
1714279 |
| Comcast Consumer Complaints |
11476961 |
| COMET COMDS0x |
348074 |
| Comic Books Images |
2425186250 |
| comments |
1068768 |
| CommentsData |
15132 |
| Commercial Bank Failures, 1934-Present |
405611 |
| Commercial Paper |
1354785 |
| Commercial Register Estonia |
43842273 |
| commit_ridge |
6359598 |
| Common Brazilian Names and Gender |
74389 |
| Common Voice |
12902930268 |
| Commuter train timetable |
163399031 |
| Commuter train timetable |
163399031 |
| CoMNIST |
110594455 |
| compacts |
30946962 |
| Company Sentiment by Location |
58693020 |
| company_credit_rating_normalized_sp |
1117373 |
| Comparative Sentences |
774200 |
| Comparing Numerical Movie Review Scores |
15728 |
| Comparing RF and the multi-output meta estimator¶ |
3631 |
| COMPAS Recidivism Racial Bias |
23722513 |
| Compb17 |
3152016 |
| compet |
35831972 |
| Competetition-1 |
17022819 |
| Compiled_Ether_Data_Set |
83743 |
| Complete Ayah Dataset |
3586177 |
| complete dataset |
2304856 |
| Complete FIFA 2017 Player dataset (Global) |
8979684 |
| Complete Historical Cryptocurrency Financial Data |
2262400 |
| completedata |
11590849 |
| CompleteData |
726926 |
| completeData |
80558031 |
| Computer Network Traffic |
429946 |
| Computer Parts Dataset (CPU, GPU, HDD...) |
1407971 |
| ComTrans Corpus Sample |
35387522 |
| ConceptNet |
718225 |
| Concrete Compressive Strength Data Set |
59010 |
| Congress Trump Score |
2496860 |
| congressEducation |
34625 |
| Congressional Election Disbursements |
1060333799 |
| Congressional Voting Records |
534603056 |
| CONLL Corpora |
17680836 |
| Conmebol_Russia2018Qualifiers |
11677 |
| Connecticut inmates awaiting trial |
120135212 |
| conormacbride |
224080 |
| Consonance and Dissonance Results |
9663 |
| Consumer Business Complaints in Brazil |
425961054 |
| Consumer Price Index |
66123274 |
| Consumer Price Index by Year since 1913 |
1170 |
| Consumer Price Index in Denver, CO |
3509989 |
| Consumer Reviews of Amazon Products |
18386219 |
| Consumo de energia |
6518 |
| ConsumoRefrigerador |
6365897 |
| Consumption of fuels used to generate electricity |
797251 |
| Contribuintes ativos por UF |
54027 |
| Contributions to Presidential Campaigns (real) |
22820098 |
| control_data |
1125 |
| conver |
1696200 |
| Conversation JSON |
3881 |
| ConversationAI |
83578341 |
| ConversationAIDataset |
83578341 |
| Cook County Asset Forfeiture (Chicago, IL) |
3163876 |
| coolest |
287728 |
| Coordinates Map |
847 |
| coordinates-country |
87122 |
| copy of santa gift matching dataset |
19059086 |
| Copy of wikipedia-language-iso639 |
2519 |
| corn.csv |
11979 |
| Corporacion Favorita unpacked |
127861780 |
| Corporate Prosecution Registry |
946178 |
| corporita-sampled train data |
518253183 |
| Corpus of bilingual children's speech |
956206 |
| Corpus of Brazilian Portuguese Literature |
23629080 |
| correct_submission |
9978 |
| Correlates of War: Interstate Wars |
107046 |
| Correlates of War: World Religions |
642238 |
| Correlation Solutions |
50606111 |
| Corruption Perceptions Index |
23204 |
| Council Plan performance indicators |
77489 |
| Count1 |
9704 |
| Counties geographic coordinates |
8601 |
| Counties with Smoking Ban |
3984620 |
| Countires and number of respondents |
312634 |
| Countries |
1346 |
| Countries and number of respondents spatial object |
312634 |
| Countries Info |
60799 |
| Countries ISO Codes |
9451 |
| Countries of the World |
256950 |
| Countries Population |
134321 |
| Countries Shape Files |
9146048 |
| countries_lon_lat |
1702 |
| country code |
4166 |
| country continent codes |
5224 |
| Country Profile |
4918 |
| Country Silhouette Images |
817318 |
| Country Socioeconomic Status Scores, Part II |
92201 |
| Country Socioeconomic Status Scores: 1880-2010 |
118350 |
| country-cordinates |
87122 |
| County Smoking Ban |
3569 |
| County_W_SM_Ban |
437136 |
| Course transaction |
2894492 |
| Coursera - Machine Learning - SU |
8024 |
| Coursera Data Science Capstone Datasets |
493860249 |
| courseraloan |
20322341 |
| courses_20171206 |
143520 |
| Coursework2 |
4206156 |
| Cousin Marriage Data |
933 |
| cp_1month |
54460905 |
| cprofiling_1 |
2505853 |
| CPU Data Cleaned |
1095 |
| CPU Utilization Data |
11597 |
| Craft Beers Dataset |
182596 |
| Crashes 2014 |
81635508 |
| Crashes 2014 csv |
134853071 |
| creativity |
566778 |
| Credit Card Applications |
35641 |
| Credit Card Data from book "Econometric Analysis" |
73250 |
| Credit Card Fraud Detection |
150828752 |
| credit_card_database |
6632243 |
| credit-bank-data |
133638 |
| creditcard |
150828752 |
| CreditScores |
4672098 |
| CreditTestData |
4983329 |
| Crescimento da População Brasileira |
1266 |
| Cricinfo Statsguru Data |
2721164 |
| Cricketer Info From espncricinfo |
8716715 |
| crime senior citizen |
12848 |
| crime against women in India |
249856 |
| crime analysis |
10620 |
| crime analysis |
27719 |
| crime analysis |
11788 |
| Crime analysis |
9463 |
| crime classifcication |
107979 |
| Crime Classification dataset |
407663 |
| Crime committed against Senior citizen |
11788 |
| Crime Data in Brazil |
842874744 |
| Crime in Baltimore |
41173772 |
| Crime in Bulgaria, 2000 to 2014 |
135102 |
| Crime in Context, 1975-2015 |
263935 |
| Crime in India |
12841047 |
| Crime in Los Angeles |
377870521 |
| Crime in the U.S. |
186880 |
| Crime in Vancouver |
58924580 |
| Crime Investigation |
14026 |
| crime report |
88064 |
| Crime Statistics for South Africa |
24559707 |
| crimean |
663701 |
| crimecsv |
12848 |
| crimenbogota |
593186 |
| Crimes Committed in France |
98316 |
| Crimes de São Francisco |
23670377 |
| Crimes in Chicago |
1991120451 |
| Criminal |
1584533 |
| Criminal |
1563180 |
| Criminal Dataset |
1584533 |
| Criminal DataSet |
1584621 |
| criminal_train |
1584533 |
| Criminals |
1584533 |
| Criminals |
1584533 |
| crittical |
294163 |
| Crop Data Analysis |
698951 |
| Crop Nutrient Database |
287615 |
| Cross-position activity recognition |
83372747 |
| Cross-sell: target the right customer |
206939073 |
| CrowdAnalytx_Tennis_pREDICTION |
832353 |
| Crowdedness at the Campus Gym |
3447605 |
| Crowdfunding Data (Reg CF) |
329917 |
| crttical |
294163 |
| Crubadan |
11256183 |
| crunchbase_monthly1 |
1706822 |
| Crypto |
14827433 |
| crypto |
24717 |
| Crypto Currencies |
2855340 |
| Crypto Currencies |
2341674 |
| Cryptocoins Historical Prices |
20518707 |
| Cryptocurrencies |
9049796 |
| Cryptocurrencies Price |
210068 |
| Cryptocurrency Data |
2271662 |
| Cryptocurrency Historical Data |
648785 |
| Cryptocurrency Historical Prices |
1708056 |
| Cryptocurrency Market Capitalizations |
143774 |
| Cryptocurrency pricing recent history |
5513413 |
| CryptoCurrency Trade History |
326560906 |
| CS 405 NLP |
67479286 |
| CS_MIT_6.00x_2012_NON_US_Students |
4559876 |
| CS_MIT_US |
1736515 |
| CS:GO Competitive Matchmaking Data |
384043159 |
| CS228 Materials on python |
59309 |
| CSC 630 Datasets |
201869670 |
| csd.123 |
216 |
| csd1234 |
189 |
| csd1234 |
731 |
| csd12345 |
731 |
| csl406 |
103642069 |
| csv format |
1180166 |
| csv_inception |
7942529 |
| CT Accidental Drug Related Deaths 2012-June 2017 |
802658 |
| CT Medical Image Analysis Tutorial |
458149327 |
| CTGData |
181381 |
| Cuff-Less Blood Pressure Estimation |
5281643644 |
| Cuneiform Digital Library Initiative |
201318316 |
| curated_stackoverflow_dataset_for_Q_&_A |
349299 |
| CuratedDataSource |
37321171 |
| Currencies |
1708058 |
| currency name |
6624 |
| Current Population Survey |
314148794 |
| Current Properati Listing Information |
486186644 |
| Cuss words and Deaths in Quentin Tarantino Films |
63940 |
| Custom data |
22450588 |
| custom_layers |
5306 |
| CUSTOMER CHURN |
977501 |
| customer churn |
1192408760 |
| Customer Churn |
26402063 |
| Customer Data |
2682651 |
| Customer Predictive Analysis |
329217 |
| Customer Support on Twitter |
175038646 |
| Customer Visits Data |
13718 |
| Customers |
45420 |
| Customers Data |
237238 |
| Customers final |
237238 |
| Customers Visits |
13712 |
| CUSTOMPLOT |
940 |
| cusume_layer |
7623 |
| cusume_layers |
8680 |
| Cyber crime |
663701 |
| Cyber Crime Motives - India 2013 |
2628 |
| Cycle of grass growth |
8658 |
| Cycle Share Dataset |
47724176 |
| D_test |
28629 |
| D_train |
61194 |
| D.C. Metrorail Transportation Ridership Data |
1240025 |
| D00001 |
5733501 |
| DACA Recipients |
1265698 |
| dae test |
9825 |
| Daikon (Diachronic Corpus) |
118301154 |
| Daily and Intraday Stock Price Data |
437898965 |
| Daily Fantasy Basketball - DraftKings NBA |
130299509 |
| Daily Happiness & Employee Turnover |
51605003 |
| Daily minimum temperatures |
68050 |
| Daily News for Stock Market Prediction |
14884372 |
| Daily returns for Apple and Microsoft stock |
173351 |
| Daily Sea Ice Extent Data |
4491537 |
| Daily views in Netflix |
22958 |
| Dairy Hub Baseline and Scooping survey Embu |
121313 |
| Dairy Hub Baseline Survey, Nyandarua |
149430 |
| Dairy Hubs Baseline and Scooping survey-UasinGishu |
978624 |
| Dallas Police Department Reported Incidents |
197281346 |
| damiiii |
23930 |
| Danube Water Quality Monitoring data |
59819810 |
| Dark Destiny(in development) |
60631 |
| Dark Net Marketplace Data (Agora 2014-2015) |
8071801 |
| Darknet Market Cocaine Listings |
806564 |
| Data Product Name Lazada Indonesia |
833473 |
| data 1-train |
32828332 |
| Data Analysis Assessment |
5986954 |
| Data Exploration |
249117780 |
| Data exploration energy prediction |
1185330 |
| Data for mc |
261912642 |
| Data for my self-learning |
217812 |
| Data for public services on Brazil |
4088603 |
| Data from OBD (On Board Diagnostics) |
233512 |
| Data from worlds 2017 |
773365 |
| Data Lab |
2558105 |
| Data Management Dataset |
566778 |
| Data Newb or is it Noob, sorry, I'm new to this |
373764 |
| Data of GDP for all countries |
662372 |
| Data Preprocessing |
226 |
| Data s |
7314359 |
| Data sample |
40478505 |
| Data Science |
2321526 |
| Data Science Jobs around the world |
1636642 |
| Data Science London + Scikit-learn |
1971469 |
| Data Scientist Survey Project |
7938934 |
| Data Scientists by countries |
312634 |
| Data Scientists vs Size of Datasets |
5917 |
| data sensors |
3647432 |
| Data Set |
61194 |
| data set |
377414237 |
| data set for happines |
29536 |
| data set for yelp |
477907 |
| Data set to predict Conversion Rate |
6863400 |
| Data Sets |
93081 |
| Data Shares Updated |
1925039 |
| data source |
196737128 |
| Data Stories of US Airlines, 1987-2008 |
5732078 |
| Data test |
28314435 |
| data test for python |
62792 |
| Data upload test |
17498477 |
| Data Visualization Final Project |
863606 |
| Data Wilayah Republic Indonesia |
2701722 |
| Data Wrangling |
809 |
| _data_ |
189979994 |
| Data__3 |
1810756 |
| data_banknote_authentication |
45030 |
| data_extract |
196737128 |
| data_final1 |
4045017 |
| data_final2 |
4044975 |
| data_final3 |
4045041 |
| data_final4 |
4045084 |
| data_img |
221091747 |
| Data_load |
196737128 |
| data_new |
60033 |
| data_properties |
11510318 |
| Data_resume |
4309778 |
| Data_Schizo |
215922329 |
| Data_Set |
269807 |
| Data_set_for default_creditors. |
72420922 |
| data_sms |
5984996 |
| data_sn |
7305227 |
| Data_titanic_disater_prediction |
61194 |
| data_train_tita |
61194 |
| data_with_all_conts |
8301811 |
| data_x1 |
4045075 |
| data_x2 |
4045013 |
| data-mercari |
196737128 |
| data-salary.txt |
149 |
| Data-Siebel |
1179612 |
| data.csv |
256585 |
| data.csv |
125204 |
| data.txt |
18732 |
| data1.csv |
125204 |
| Data10 |
1810753 |
| data10000 |
967307 |
| data111 |
2176079 |
| data1111 |
2176079 |
| data12 |
170760 |
| data12017 |
673800536 |
| data2.csv |
125204 |
| data2017 |
673800536 |
| data201712 |
4163417 |
| DATA2here |
11899 |
| data4deshawcode |
78381308 |
| dataaa |
29420 |
| Database of Android Apps |
84000942 |
| database.sqlite |
34297213 |
| database.sqlite |
313090048 |
| database2 |
45056 |
| DataBundle |
182428995 |
| DataCampTraining(Titanic) |
2843 |
| datadata |
196737128 |
| datadata1 |
4558 |
| DataExample |
531 |
| DataExoTrain |
30534811 |
| DataForProject |
287859225 |
| DataForTesting |
85723 |
| DataImager Dataset |
48183945 |
| datainput1 |
1135215 |
| datainput2 |
1136571 |
| datairis |
5107 |
| Datalearning |
5 |
| Datamining |
3119593 |
| datams1 |
674950 |
| datanew |
335 |
| datanews |
11899 |
| dataout2017 |
273386 |
| datasci101 |
34682 |
| Datascience Universities across US |
350113 |
| DataSeer |
29170333 |
| dataseparation |
33720022 |
| dataset |
196737128 |
| dataset |
64080981 |
| Dataset |
171048828 |
| dataset |
188114545 |
| dataset |
1908375 |
| dataset |
1270 |
| Dataset |
196737128 |
| dataset |
4388554 |
| dataset |
2198879 |
| dataset |
61194 |
| dataset |
26881506 |
| dataset |
1145608 |
| dataset |
196737128 |
| dataset |
18762 |
| dataset |
55152040 |
| DataSet |
5104423 |
| DataSet |
196737128 |
| dataset |
37819635 |
| dataset |
469399139 |
| dataset |
464472240 |
| dataset |
2304944 |
| dataset |
11986629 |
| dataset |
1968558 |
| dataset |
344589708 |
| Dataset |
341425901 |
| dataSet |
93081 |
| Dataset |
515387 |
| Dataset |
77123 |
| dataset |
1155353 |
| dataset |
322390820 |
| DataSet |
48706 |
| Dataset |
10307653 |
| Dataset - Udacity's Intro to Data Analysis course |
946272 |
| dataset by mistake |
3295644 |
| dataset compete |
2304944 |
| Dataset for 2016 US Election |
24845711 |
| Dataset For Bayesian Classifier |
2423318 |
| Dataset for collaborative filters |
31250807 |
| Dataset for HMM Clustering |
2379723 |
| Dataset for Insect Sound |
2617200 |
| Dataset for Mercari Competition |
134964916 |
| Dataset for Mercari Competition_test |
61772212 |
| Dataset for Various Classification Algorithm |
2290261 |
| Dataset for Various Clustering Algorithm |
2367761 |
| Dataset malware/beningn permissions Android |
276896 |
| Dataset of customer purchase |
35123906 |
| Dataset of SMS messages |
515387 |
| Dataset of Standard cards Magic:The Gathering |
379802 |
| Dataset on company clients satisfaction |
545 |
| Dataset tryout |
24 |
| Dataset v2 coma |
415597 |
| DataSet Vinos |
12306 |
| DATASET WINE |
11394 |
| DataSet_Analytics102 |
1397246 |
| dataset_clientes |
734853 |
| dataset_entre |
3640989 |
| Dataset_mercari_descompactado |
196737128 |
| dataset_sup |
9199904 |
| dataset_unzip_mercari |
196737128 |
| dataset- kaggle |
196737128 |
| DataSet(Traffic flow) |
2283861 |
| Dataset0 |
2434632 |
| dataset1 |
18762 |
| Dataset1 |
2605105 |
| dataset12 |
18762 |
| Dataset123 |
99895 |
| dataset2 |
23654760 |
| dataset2 |
7327877 |
| Dataset2 |
2863661 |
| dataset3 |
7318232 |
| dataset44 |
45821350 |
| Dataset8 |
64097906 |
| DatasetDataset |
18217460 |
| datasethere |
1302 |
| DataSetPartidos |
2747380 |
| Datasets for ISRL |
582659 |
| datasets of iceberg |
302435 |
| Datasets-Extras-Gobierno-Ciudad |
73169216 |
| datasets-uci-breast-cancer |
141096 |
| Datasets1 |
162534349 |
| DatasetStacking |
391381 |
| DatasetTest |
2058061 |
| DataSetTitanic |
89823 |
| datasettop |
151646 |
| DatasetTrain |
391381 |
| datasource1 |
19546258 |
| datasource2 |
4132092 |
| datasource3 |
18624463 |
| datastockindex |
8460361 |
| datastocks |
44603115 |
| datatest |
4163417 |
| datatest_R |
4163417 |
| datawithusers |
226 |
| date_info_same_dow |
3530 |
| datos_titanic |
89823 |
| Datset under development |
172104359 |
| Day One CSV File |
7549 |
| DBDA2-ja |
2059 |
| DBLPTrainset |
639795 |
| dc h1b |
99611854 |
| DC Metro Crime Data |
113933745 |
| DCAD data |
428969136 |
| dcnn fhv lee 15k 4 |
8204027 |
| DCNN fhv lee 16 |
7334532 |
| DCNN fhv lee12 |
7619172 |
| dcnn fhv lee16 |
7334532 |
| DCNN fhv Lee4 |
8092963 |
| DCNN fhv lee8 |
7998286 |
| DCNN IE Aug part |
8075475 |
| DCNN model |
24837626 |
| DCNN model18 |
7974710 |
| dddddd |
16920430 |
| dddddddd |
53967 |
| ddddddddddddddd |
5993 |
| dddddddddddddddddddd |
16974329 |
| ddffdssd |
5993 |
| DDLJ 666 |
855780 |
| DE Temp EC |
222623 |
| DEA Drug Slang Code Words |
22092 |
| Deadly traffic accidents in the UK (2015) |
19235021 |
| dear genie kickstarter |
25608454 |
| Death in the United States |
4334522180 |
| Death Metal |
74263119 |
| Deaths related to the Northern Ireland conflict |
477805 |
| dec_numerai |
103040127 |
| Deceptive Opinion Spam Corpus |
1349623 |
| DecisionTree |
974484 |
| DeconstructedGTD |
21059917 |
| Deep Learning A-Z - ANN dataset |
684858 |
| Deep Sea Corals |
146105985 |
| Deep-NLP |
679231 |
| deeplearning |
684858 |
| defaite |
7533658 |
| defaite2 |
4760019 |
| Default of Credit Card Clients Dataset |
2862995 |
| DELETE |
403343 |
| delete_zero_price_item1 |
7361212 |
| DELETED |
315905791 |
| Delhi Weather Data |
6652900 |
| Delpher Dutch Newspaper Archive (1618-1699) |
150761642 |
| delta_pred |
2967586 |
| demo_model |
530993226 |
| demofile |
1271 |
| DemographicData |
8360 |
| Demographics |
1022 |
| demonetisation-tweet |
919538 |
| demonetizatiom |
231571 |
| Demonetization in India |
40572843 |
| Demonetization in India Twitter Data |
5258200 |
| Demonetization talk on Twitter |
92171087 |
| Demonetizing Rupee |
27822361 |
| DemoProject |
54 |
| demoset |
135919418 |
| deng-dataset |
41591408 |
| Dengue cases |
52153 |
| Dengue cases |
52153 |
| dengue cases 1 |
52153 |
| Dengue Cases in the Philippines |
52153 |
| dense child matrix |
303897380 |
| DenseEP10B1B2IMDG |
255821 |
| DenseNet-121 |
30330932 |
| DenseNet-161 |
110722606 |
| DenseNet-169 |
54060694 |
| DenseNet-201 |
76541998 |
| Densnet121+fine tuning |
30311364 |
| Denver International Airport |
34599 |
| Dependency Penn Treebank |
1069540 |
| Depth Generation - Lightfield Imaging |
202078164 |
| derfff |
7978017 |
| Derivation |
562394592 |
| Derivation |
562394592 |
| Derivatives Trading |
1122607 |
| dernier |
440983 |
| des2017 |
78381308 |
| Describing New York City Roads |
23009781 |
| descripciones1-tpdatos |
317372677 |
| descripciones2-tpdatos |
386955524 |
| descripe |
13788274 |
| Descript_Meta |
16094554 |
| Despesas Notas de Empenho |
15570511 |
| Detailed data from italian Serie A |
23656 |
| Detailed NFL Play-by-Play Data 2009-2016 |
70282651 |
| Detailed NFL Play-by-Play Data 2015 |
15488579 |
| details |
44502 |
| Details of resigned employees from Jan-17 |
53547 |
| Details of Resigned Employees from Jan-2017 |
71724 |
| Determine the pattern of Tuberculosis spread |
871348 |
| Devanagari Character Dataset |
9834839 |
| Devanagari Character Dataset Large |
66308602 |
| Devanagari Character Set |
126630805 |
| Developers and programming languages |
6734627 |
| DFFF blood |
215899084 |
| dfffflfl |
5993 |
| dfgvbhnjk |
103 |
| dftarin |
45719119 |
| dftrain |
68371984 |
| Diabetes |
30474 |
| Diabetes 130 US hospitals for years 1999-2008 |
20652298 |
| Diabetes Analysis1 |
1147017 |
| Diabetes by Demographies |
2658 |
| diabetes_columns |
14875 |
| diabetes.csv |
23873 |
| Diabetic |
3314579 |
| Diabetic Foot Pressure Analysis |
62033419 |
| diabities |
174158 |
| diabities |
4787 |
| Diagnose Specific Language Impairment in Children |
632972 |
| Dialogues |
7654 |
| Diamonds |
3192560 |
| diamonds_arun |
9740 |
| Dictionary |
1185995 |
| dictionary & baseline generated from external data |
170033045 |
| Dictionary for Sentiment Analysis |
1052 |
| Dictionary of American Regional English (DAREDS) |
657924 |
| dictionary1 |
34685 |
| dictss |
1799 |
| Did it rain in Seattle? (1948-2017) |
761976 |
| different from our method of SFE |
14030378 |
| different submission files |
114549146 |
| Diffusion Mapping for Drug Combinations |
5483535 |
| DigiDB Dataset |
59898 |
| DigiDB_digimonlist |
15354 |
| Digimon Database |
59898 |
| Digit Recognition |
7502265 |
| Digital Media |
695185 |
| DigitRecognition |
7502265 |
| digits dataset |
264712 |
| Dilma impeachment Twitter Raw Data |
6280924 |
| Diplomacy Betrayal Dataset |
53056025 |
| Disaster/Accident Sources |
2406589 |
| Discourse Acts on Reddit |
54391204 |
| Discurso Macri (inauguracion Metrobus del bajo) |
4753 |
| disease |
960 |
| Diseased Person Dataset |
33859 |
| Disk Space Data |
853924 |
| Disputed Territories and Wars, 1816-2001 |
1388531 |
| Distance Cycled vs Calories Burned |
3878 |
| Divactory 2017 Warm Up Case |
42329822 |
| Diversity Index of US counties |
192899 |
| dj_lgb.csv |
17103032 |
| DJIA 30 Stock Time Series |
6479382 |
| djtest |
17097734 |
| dl_baseline |
7362746 |
| dlearning_help |
17083528 |
| dmia_sport |
99361568 |
| DMproject |
93081 |
| dnet 16 |
292448 |
| dnet 20 |
115440 |
| dnet 24 |
292448 |
| dnet 32 |
292448 |
| dnet 40 |
292448 |
| dnet 48 |
292448 |
| dnet 8 |
292448 |
| DNet10 |
39008 |
| Do Conference Livetweets Get More Traffic? |
15308 |
| DO NOT CONSIDER |
588761 |
| DO NOT CONSIDER |
591893 |
| Doctor and lawyer profiles on Avvo.com |
5869263 |
| Doctor Vs Non_Clinical_Correlation-HSN April'17 |
2654 |
| document |
42634214 |
| Documents dataset |
515387 |
| dododo |
93081 |
| Dog_breed_identification_dataset |
724499986 |
| dog_cat_subset |
47799254 |
| dog-project/lfw |
196739509 |
| doggoghj |
454914562 |
| dogImages |
1132023110 |
| Dogs of Zurich |
1568984 |
| Dogs of Zurick |
1568984 |
| Dogs vs Cats |
854397158 |
| DogVGG16Data |
152597337 |
| DolarToday & SIMADI Scrap |
2256 |
| Dolch Words |
1917 |
| Donald J. Trump For President, Inc |
87473 |
| Donald Trump Comments on Reddit |
23502571 |
| Donald Trump Forbes 400 Rankings |
3365 |
| Donald Trump Tweets |
5090580 |
| Dota 2 Matches |
1411281355 |
| Dota 2 Matches Dataset |
13465690 |
| Dota 2 Professional Games Hero Picks |
774891 |
| dota-ML |
39756882 |
| dotsmusic |
6738 |
| Douban Movie Short Comments Dataset |
405610647 |
| Dow Jones 1/jan/2000 to 6/dec/2017 |
2872115 |
| downloadedsolution |
57524622 |
| dp_prediction |
5680377 |
| dpnet 40 |
4671600 |
| Dreem_Data |
2864579944 |
| drinks |
4973 |
| Driver |
287859225 |
| Drone Attacks |
161953 |
| Drosophila Melanogaster Genome |
482805179 |
| Drug Induced Deaths |
38362 |
| ds_11122017 |
91162 |
| ds1aaa |
43781526 |
| DSA.XLS |
10620 |
| DSA.xlsx |
10620 |
| dsfsdfsd |
5993 |
| DSI_kickstarter |
4389347 |
| DSL Corpus Collection (DSLCC) |
57695248 |
| Du L ch H i An 1 Ngày Khám Phá m Th c V êm |
111513 |
| Dummy dataset |
96556 |
| Dummy_sales |
741 |
| Dutch Parliament Elections 2017 - Amsterdam |
469735 |
| Dutch texts |
1841 |
| Dutch Weather |
531979 |
| DVLA Driving Licence Dataset |
1943040 |
| dwqdqw |
28 |
| dzoulou |
3314579 |
| E commerce data set |
7548646 |
| E commerce data set |
7548646 |
| E-commerce |
1026267 |
| E-Commerce Data |
45580638 |
| E-sales Data |
82537 |
| Earn here |
12692 |
| Earthquakes <-?-> Solar System objects? |
4503737 |
| Easy To Analyse Ion Channel Data |
470271 |
| Easyjet Stock Prices |
326009 |
| Eating & Health Module Dataset |
19621665 |
| Ebay Motorcycle Prices |
1448083 |
| Ebola Cases, 2014 to 2016 |
1422467 |
| EC_TEMP |
190590 |
| ecac-feup |
35933463 |
| ECB Official Euro Exchange Rates |
478239 |
| ECG Analysed Data |
2515339 |
| Ecoli Data Set |
19487 |
| ecoli_data |
19487 |
| ecoli_dataset |
19487 |
| ecoli_datasets |
19487 |
| Ecommerce Dataset |
7548761 |
| eCommerce Item Data |
566516 |
| Economic calendar (EC) Forex (2011-2017) |
3437463 |
| Economic Indicators |
14180 |
| Economies |
1448 |
| Economy Rankings |
16741 |
| Ecuador Geo info |
598306 |
| Ecuadorian Presidential Candidate Tweets |
289982 |
| ecuardor_geojson |
1976116 |
| EDF_CHALLENGE |
9763899 |
| EdFacts Graduation Rates |
16907253 |
| Edges data |
1467 |
| edited_data |
60033 |
| editPAM50 |
18635 |
| edrt2345ewfgdfgdgertg |
1635878 |
| Education in India |
2286416 |
| Education Index |
562613 |
| Education Statistics |
310761005 |
| eeeeee |
4044919 |
| eeeeee |
1800698 |
| eeeeeee |
2 |
| EEG Analysis |
19653773 |
| EEG brain wave for confusion |
120391255 |
| EEG data from basic sensory task in Schizophrenia |
1776693160 |
| EEG MY DATA1 |
702300 |
| EEG-Alcohol |
928273586 |
| EffectOfGenderBodyTemperaturesAndRestingHeartRate |
1424 |
| Effects of Population on Crimes |
18859 |
| eho112 |
26738090 |
| ejercicio53 |
259116 |
| ejercicio53_ |
259116 |
| ejercicio5321 |
10352 |
| El Nino and La Nina Historical Data |
3976 |
| El Nino Dataset |
10068291 |
| Election Day Tweets |
219456413 |
| Election News Headlines |
77888 |
| Election News Headlines Cleaned |
69592 |
| Electoral Donations in Brazil |
72751650 |
| Electoral Integrity in 2016 US Election |
584992 |
| ElectricityBills |
138082 |
| Electron Microscopy 3D Segmentation |
519677428 |
| Electronic Music Features Dataset |
1122051 |
| elemental_properties |
2461 |
| Elementary Python Functions 7 |
58637664 |
| Elementary school admission Romania 2014 |
46574095 |
| Elevation Data meets SF Fire Department Calls |
718631987 |
| Elevators in New York City |
13586064 |
| ELO for EPL |
1201 |
| ELO for EPL 15 matchday |
793 |
| ELO for EPL matchday 15 |
793 |
| Elon Musk Tweets, 2010 to 2017 |
402077 |
| Elon Musk's Tweets |
452905 |
| ema sd19 10 percent |
129926992 |
| EMA-transportation |
2058015 |
| Email Campaign Management for SME |
5163148 |
| Email Dataset |
3441137 |
| Email of Hacking Team |
31011560 |
| Email Status Tracking |
3141881 |
| Emails |
4659 |
| emap_analysis |
3263442 |
| emap_data_analysis_ |
3644791 |
| emap_db |
70862814 |
| embedding |
160398284 |
| Embeddings |
146390928 |
| embedingCatData |
939576596 |
| Emergency - 911 Calls |
11064369 |
| EMNIST (Extended MNIST) |
1335705026 |
| Emoji sentiment |
159906583 |
| EmojiNet |
7171480 |
| EmoSim508 |
261594 |
| emotion recognition |
301072766 |
| emotion_analysis |
101279952 |
| Emotion, Aging, and Sentiment Over Time |
41194684 |
| Emotions Sensor Data Set |
116604 |
| empirical |
4072096 |
| Empirical Analysis of Network Data |
7780 |
| Employee Attrition |
228496 |
| Employee Attrition |
1060363 |
| Employee Attrition |
7318626 |
| EmployeeData |
58 |
| EmployeeSet |
416930 |
| EmployeeVancancy |
1011488 |
| Employment (All) |
6001 |
| Employment in Manufacturing |
1354 |
| EMPRES Global Animal Disease Surveillance |
2850933 |
| EmpVacancy |
584848 |
| En Part-Of-Speech tags |
92764294 |
| ENADE SCORE |
51212029 |
| Encoded shortest path sequences for NYC taxi trip |
141265930 |
| encoded_brand_name_category_name |
13343065 |
| Encrypted Stock Market Data from Numerai |
36569930 |
| Encuesta USO WEB 2.0 |
39106 |
| ENEM - ENADE |
8738872 |
| ENEM 2015 |
2419260871 |
| ENEM 2016 |
1226584118 |
| EnemAcertos |
40247 |
| Energy Consumption |
4529 |
| Energy Efficiency Dataset |
40713 |
| England Obesity Stats 2017 |
265639 |
| English Premier League in-game match data |
2466790 |
| English Premier League Penalty Dataset, 2016/17 |
10005 |
| English Premier League Player data 2017-2018 |
822374 |
| English Premier League Players Dataset, 2017/18 |
34635 |
| English Stopwords |
4351 |
| English surnames from 1849 |
232390 |
| English Word Frequency |
4956252 |
| English words all uppercase |
1123958 |
| Enriched Hotel Reviews Dataset |
57201126 |
| Enron Person of Interest Dataset |
53721 |
| Ensamble |
28269197 |
| ensemble |
20415050 |
| ensemble |
6356379 |
| Ensemble |
7974919 |
| ensemble |
59904 |
| ensemble |
15948812 |
| Ensemble Data |
18499009 |
| Ensemble Grocery 01 |
92955783 |
| ensemble_5 |
306790 |
| ensemble_ma_lgbm_cat |
21238240 |
| ensemble_results |
568587 |
| ensemble-test |
1505679 |
| Ensembler2 |
14175099 |
| Ensembling |
42444296 |
| enterenter |
9019406 |
| entre_h_1 |
3599989 |
| environment |
439 |
| Environmental Sound Classification 50 |
160010487 |
| Epicurious - Recipes with Rating and Nutrition |
90508284 |
| Epicurious Meta-Category Script |
20931 |
| Epileptic Seizure Recognition |
7635689 |
| epl_predicted_values |
35386 |
| EPL, 15 matchday |
1670 |
| Equitable Sharing Spending Dataset |
10627362 |
| Equivalence relations |
41177195 |
| ERA-Interim 2m temperature anomalies |
9972 |
| ERC Seasonal Graph Database |
7164928 |
| errors |
7978017 |
| ESA' Mars Express orbiter telemetry data |
174412660 |
| ESA's Mars Express Operations Dataset |
374128433 |
| ESL Competitive Games |
3113 |
| Est. Population US States & Puerto Rico 2010-2017 |
9901 |
| Estimated speed using fastest route |
111370409 |
| Estimates |
367 |
| et_submission.csv |
6297326 |
| Ethereum Historical Data |
424477 |
| ethnicity |
39691 |
| Ethnicity_Dataset |
669874 |
| etiquetasmodificadas |
27313 |
| eur/usd |
136103 |
| Eurfa Welsh Dictionary |
16049152 |
| euro12 |
2319 |
| Eurojackpot results |
35753 |
| Europarl |
41396100 |
| Europarl annotated for speaker gender and age |
398525304 |
| European Soccer Database |
6365 |
| European Soccer Database |
313090048 |
| European Soccer Database Supplementary |
61354455 |
| European Soccer Dataset : La Liga |
53417 |
| Eurovision Song Contest scores 1975-2017 |
3405728 |
| Eurovision YouTube Comments |
373333 |
| eurusd |
8887 |
| EURUSD - 15m - 2010-2016 |
15130384 |
| EurUsd 60 Min |
112159 |
| EURUSD from 1971 EURUSD 2017 |
648661 |
| EURUSD H4 |
53217642 |
| EVA_classified |
99046 |
| EVA_cleaned |
98292 |
| EVA_cleaned_classified |
197338 |
| EVA_general_corpus |
240108 |
| EVA_newactivity |
4992 |
| EVA_newactivity |
4986 |
| Evan's Fruit Dataset |
935396 |
| evergreen |
21972916 |
| Every Cryptocurrency Daily Market Price |
15118189 |
| Every Pub in England |
6287796 |
| Every song you have heard (almost)! |
630333419 |
| EveryPolitician |
44308991 |
| EveryPolitician |
44308991 |
| ewrwrwerwrrww |
272893711 |
| ex1_cars |
357 |
| example |
143914 |
| example converge |
3258 |
| Example Dataset |
218 |
| Example Submission File |
212908 |
| Example Web Traffic |
196803 |
| example2 |
0 |
| examplecsv |
443837 |
| ExampleData |
5407973 |
| exchange rate |
247 |
| Exchange rate |
9719 |
| Exchange rate BRIC currencies/US dollar |
9214 |
| Exchange Rates |
355525 |
| Exchange Rates |
2081675 |
| Executed Inmates 1982 - 2017 |
652451 |
| Executions in the United States, 1976-2016 |
157451 |
| Executive Orders |
198521 |
| Executive Orders, 1789-2016 |
4229 |
| Exercice |
93 |
| Exercise Pattern Prediction |
12237502 |
| exercise1 |
1359 |
| EXL_Data |
8025781 |
| Exoplanet Hunting in Deep Space |
291130416 |
| exoTest |
5896401 |
| ExoTrain.csv |
30534811 |
| exp_titan |
89823 |
| Expat Insider 2017 |
3636 |
| ExpediaTrainingSet |
612082209 |
| Expenses |
35 |
| experiment_data |
1122 |
| exploring soccer analysis |
2202337 |
| ExpressionNet |
370274 |
| Extemal |
547620 |
| Extinct Languages |
754406 |
| Extracted |
642546 |
| Extracted Dataset |
23898986 |
| Extremely_Randomized_Trees_Classification |
28495130 |
| Exxon Mobile |
4857 |
| Exxon Mobile stock data |
4857 |
| Eye Gaze |
700460726 |
| EyesOpenClosed |
37677210 |
| F-train |
7726881 |
| F-train2 |
7726881 |
| F1_ddbb |
6242967 |
| FAA Laser Incident Reports |
1180822 |
| FAA laser with days of week |
1549619 |
| fabletext |
490763 |
| face detection |
24229590 |
| Face Images with Marked Landmark Points |
521234420 |
| face_key_point |
238064810 |
| Facebook keyword extraction competition |
3249061408 |
| Facebook V Results: Predicting Check Ins |
2789281797 |
| Facebook_dataset |
1919867 |
| FaceBook-Dummy |
6096575 |
| faces_dataset |
36088044 |
| facesdata |
36088044 |
| Facial Expression of Emotion |
5423670 |
| Facial keypoint |
238064810 |
| Facial Keypoint Detection |
297886951 |
| Facial keypoints |
820599132 |
| Facial Keypoints dataset |
297886951 |
| Facial Keypoints Detection |
80858260 |
| Facial_Key_Points |
0 |
| faciallandmark |
257159 |
| FacialRecognition |
122495646 |
| FacialSemanticAnalysis.csv |
301072766 |
| Fact-Checking Facebook Politics Pages |
364786 |
| Factorial Digit Frequencies |
369214 |
| factors affecting mobile banking adoption |
54629 |
| FADPL2015 |
29435 |
| Fair's "Affairs" dataset |
23148 |
| Fake News detection |
5123604 |
| Fake_Dataset |
1563278 |
| Fall Detection Data from China |
625610 |
| Fantasy Premier League |
408602 |
| Fantasy Premier League |
717954130 |
| Fantasy Premier League - 2017/18 |
398450 |
| Fantasy Trading |
115900961 |
| Farmers Markets in New York City |
11013 |
| FAS data set 2016 |
14816 |
| Fashion |
30888348 |
| Fashion Mnist |
5860382 |
| Fashion MNIST |
72149861 |
| fashion_mnist dataset |
133047193 |
| Fashion-mnist_train |
35194014 |
| Fashionmnist |
5860382 |
| Fashon_MNIST train and test data |
41051803 |
| FAspell |
149934 |
| fasttext |
111680401 |
| FastText |
111680399 |
| fasttext |
861404431 |
| fastText |
95607 |
| fasttext embeddings |
141365456 |
| fastText English Word Vectors |
689870086 |
| fastText English Word Vectors Including Sub-words |
1035700419 |
| fastText Pre-trained word vectors English |
7883839860 |
| fastvideo category to words |
1108713 |
| fastvideo data category to title words |
1108739 |
| fat_chickens |
1145 |
| Fatal Police Shootings in the US |
3371757 |
| Fatal Police Shootings, 2015-Present |
196862 |
| Fatalities in Road Accident india(2001-2012) |
842752 |
| Fatality Facts & Safety While Driving |
267391414 |
| Fatchicken |
717 |
| fatchickens |
719 |
| Fatigue striations marked on SEM photos |
5775575500 |
| Fault Prediction |
1453672 |
| Fault prop |
2980383 |
| Faulty Steel Plates |
298004 |
| Favicons |
877700988 |
| favorita |
474221153 |
| favorita 1 |
72938089 |
| favorita 10 |
88984621 |
| favorita 11 |
143591683 |
| favorita 12 |
69047441 |
| favorita 13 |
75286158 |
| favorita 14 |
86454093 |
| favorita 15 |
146712778 |
| favorita 18 |
89651736 |
| favorita 19 |
68840616 |
| favorita 2 |
34180674 |
| favorita 20 |
72899862 |
| Favorita 21 |
90178885 |
| favorita 22 |
85572260 |
| favorita 23 |
87184697 |
| favorita 24 |
88090025 |
| Favorita 3 |
52696046 |
| favorita 4 |
34203687 |
| favorita 5 |
67435394 |
| favorita 6 |
37522139 |
| favorita 8 |
92233581 |
| favorita 9 |
69193838 |
| Favorita light |
14552213 |
| favorita mix |
49767923 |
| Favorita Un-7z |
168307438 |
| Favorita Un-7z 1 |
200778612 |
| Favorita_ddvz |
7700245 |
| favorita1 |
514556 |
| fbddfbfdb |
58459 |
| FCC Net Neutrality Comments |
8466965 |
| FCC Net Neutrality Comments (4/2017 - 10/2017) |
207678865 |
| FCC Net Neutrality Comments Clustered |
203169719 |
| FCC Net Neutrality Comments Vectorized Sample |
299712531 |
| FCC Public Comment Survey Results Deidentified |
16610487 |
| fd2222 |
815482 |
| FDA Enforcement Actions |
1095223092 |
| FDetect |
33774512 |
| fdhdbbdb |
38 |
| feat files |
804161924 |
| feature |
355 |
| feature |
202926583 |
| Feature Subset Selection |
242652 |
| feature_2 |
1316 |
| feature_798 |
496180 |
| feature_mensile |
6949 |
| feature1 |
286 |
| feature1200 |
17886 |
| feature1600 |
23261 |
| feature200 |
2975 |
| feature400 |
5933 |
| feature800 |
11722 |
| Featured |
597345624 |
| FeatureIndex |
27589 |
| Features |
1164474 |
| features_.csv |
1316 |
| features.csv |
1316 |
| features.csv |
1571 |
| Features&Targets |
7365741 |
| fecalma |
9354 |
| Feder Decalogue of Priorities |
12086 |
| Federal Air Marshal Misconduct |
373826 |
| Federal Emergencies and Disasters, 1953-Present |
5875126 |
| Federal Firearm Licences |
12038707 |
| Federal Holidays USA 1966-2020 |
15186 |
| Federal Reserve Interest Rates, 1954-Present |
26464 |
| feet files |
724350246 |
| FEM simulations |
624780 |
| FendaData |
38874706 |
| Fentanyl Pharmacy Dispensations in NJ 2011-2016 |
3057 |
| Fertility Rate By Race |
16613 |
| fffff. vghnb n2e |
366 |
| ffffff |
148359627 |
| fhv lee 15k10 |
4738784 |
| fhv lee 15k15 |
4732266 |
| fhv lee 15k20 |
4733529 |
| fhv lee 15k5 |
4681687 |
| FICS Chess Games |
1552017 |
| fifa 17 dataset |
2018895 |
| fifa 17 datasets |
8128096 |
| fifa 17 datasetss |
1904157 |
| FIFA 18 calculated ratings |
1133364 |
| FIFA 18 Complete Player Dataset |
15928513 |
| Fifa 18 More Complete Player Dataset |
5653816 |
| FIFA worldcup 2018 Dataset |
2794 |
| fifa2017 |
4773096 |
| fifa2017 full data |
3930217 |
| file_for_smart2 |
2034976 |
| file45646 |
273539030 |
| filestc |
51818 |
| Filipino Family Income and Expenditure |
22664315 |
| fill_brand_name |
7976847 |
| Film Fest |
4388554 |
| Film Locations in San Francisco |
320475 |
| fim.so |
792496 |
| Fin Model 2Sigma |
580023307 |
| final project |
15419602 |
| Final Project |
277285 |
| Final Project Dataset |
22204041 |
| Final project: predict future sales |
15419602 |
| final_best14 |
184162 |
| final_project |
1397246 |
| final_project_dataset |
37883 |
| Final_Prop |
36567820 |
| final_test |
57039479 |
| final_train |
120047052 |
| final2 |
7971308 |
| finalData |
170760 |
| FinalData |
10307653 |
| FinalDatasets |
8646651 |
| finaledata |
170760 |
| finalmodel |
4686417 |
| finalproject |
16874333 |
| finance study |
3170972 |
| Finance - India |
49579 |
| Finance_kaggle_sample |
1600059 |
| Financial Distress Prediction |
834637 |
| Financial Statement Extracts |
3747170542 |
| finData |
164688 |
| Finding and Measuring Lungs in CT Data |
662532978 |
| Finding Bubbles in Foam |
39156812 |
| Fine-grained Context-sensitive Lexical Inference |
2817570 |
| Finishers Boston Marathon 2015, 2016 & 2017 |
12668752 |
| Finishers Boston Marathon 2017 |
4196246 |
| Fire Emblem Heroes Survey |
1005864 |
| Fire-detection-model-Keras |
15267435 |
| Fire-detection-model-Keras for video |
15267435 |
| Firearm licenses |
3596986 |
| Firearms Provisions in US States |
443005 |
| Fireballs |
52005 |
| Firefighter Fatalities in the United States |
278358 |
| Firefox: How Connected Are You Survey |
105295883 |
| Fires vs. Thefts |
1704 |
| Firm_data |
3396185 |
| First Attempt |
38061 |
| First Features Spooky |
2051459 |
| First GOP Debate Twitter Sentiment |
8525068 |
| First Person Narratives of the American South |
45361713 |
| First Quora Dataset Release: Question Pairs |
61325254 |
| first submission |
7976172 |
| first try |
369638 |
| First Voyage of Christopher Columbus |
327061 |
| first_london |
800554 |
| First_Matching_Without_Limitation |
4550860 |
| first_submission |
7307405 |
| first_submit |
7257447 |
| first_submit_santa |
4053360 |
| first.csv |
7936696 |
| first7.csv |
6823410 |
| FirstGB |
8012408 |
| firstpred |
7327752 |
| FirstSubDetek |
3736741 |
| firsttrain |
10384 |
| FirstTry |
7267946 |
| Fish list |
548 |
| Fish Relatedness |
349085 |
| Fishtown Comps |
2902 |
| Fitness Trends Dataset |
4400 |
| FiveThirtyEight |
14347029 |
| Flaredown Checkin Data |
155387168 |
| Flight Route Database |
2377278 |
| flights |
213824264 |
| Flights in Brazil |
42517112 |
| Flipkart Products |
38114963 |
| Floresta |
16414136 |
| Flower Color Images |
51350460 |
| flowers |
571238 |
| flowers recognition |
235781000 |
| fold_1 |
960168 |
| folder23 |
5739444 |
| folderText |
185 |
| Foo data |
67 |
| Food 101 |
5041406373 |
| Food choices |
5564659 |
| Food Data |
1632444 |
| Food Images (Food-101) |
694960931 |
| Food Ingredient Lists |
5347183 |
| Food preference |
5564659 |
| Food Prices for January 2016-June 2017 (Nigeria) |
4211 |
| Food searches on Google since 2004 |
4206909 |
| Foodborne Disease Outbreaks, 1998-2015 |
1538069 |
| FoodClassification |
58057 |
| foodmart.sales |
268322 |
| FoodTruck |
1359 |
| foood1 |
119468 |
| fooooo |
14 |
| Football Delphi |
6279168 |
| Football Events |
182915890 |
| Football features |
151782 |
| Football Manager Data (150,000+ players) |
38327717 |
| Football Matches of Spanish League |
384504 |
| Football Players |
19965974 |
| Football score prediction |
208891 |
| Football striker performance |
216145 |
| football_ddbb |
22142654 |
| FootballData |
60026 |
| for coefficients |
2296928 |
| for glmnet |
1131987 |
| for testing |
700108 |
| for text2vec glmnet |
3040362 |
| Forbes Top 2000 Companies |
514058 |
| Forecasting Currency conversion rate USDAUD |
32096 |
| Forecasts for Product Demand |
51253380 |
| Foreign Affairs(VISA)Immigration India 2010-2014 |
5776610 |
| Foreign Direct Investment in India |
7992 |
| Foreign Exchange (FX) Prediction - USD/JPY |
1546803 |
| forest |
19020 |
| forest cover data |
21701 |
| Forest Cover Type Dataset |
75170064 |
| Forest Fires Data Set |
25478 |
| FOREX: EURUSD dataset |
3148567 |
| fork model v2 aug 24 |
19562657 |
| Formspring data for Cyberbullying Detection |
3966755 |
| Formula 1 points data. 2000-2016 |
25018 |
| Formula 1 points data. 2000-2016 |
26258 |
| Formula 1 Race Data |
6242967 |
| Fortnite: Battle Royale - Weapon Attributes |
2950 |
| Fortnite: Battle Royale Chest Location Coordinates |
4205 |
| Fortune 500 Companies of 2017 in US [Latest] |
40868 |
| Fortune 500 Diversity |
471313 |
| Forza and Pascal |
24682257 |
| Fotojäädvustus |
7121 |
| Four Shapes |
22554944 |
| FourSquare - NYC and Tokyo Check-ins |
102320461 |
| FourSquare - NYC Restaurant Check-Ins |
1472659 |
| Foursquare Tips |
19124220 |
| Fracking Well Chemical Disclosure Datasets |
573227327 |
| Framenet |
168547806 |
| Framing |
391381 |
| Framingham Heart study dataset |
191803 |
| Fraud Atm Pin Data |
636 |
| Fraud Detection Societe Generale |
33774512 |
| Fraud Transaction |
34301254 |
| fraud_analysis |
150828752 |
| fraud_test |
68342 |
| fraud_train |
684819 |
| fraud_trans_test |
20181347 |
| fraud_trans_testdata |
20181347 |
| fraud_transaction |
13593165 |
| fraud-ps2 |
33774512 |
| Fraudulent E-mail Corpus |
17344435 |
| frauldenttransactions |
39652204 |
| free public fictions |
207539 |
| freeCodeCamp Chatroom in Gitter 2015-2017 |
393256406 |
| freeCodeCamp Students Data Jan-Dec 2015 |
361462031 |
| Freedom of Information Act Requests |
103028 |
| Freedom of the Press, 2001-2015 |
44572 |
| Freesound: Content-Based Audio Retrieval |
5644751852 |
| Freight Analysis Framework |
653415200 |
| French elections : Most searched candidate by city |
778477 |
| French employment, salaries, population per town |
360679360 |
| French firms evolution 2017 in paris neighborhood |
7337239 |
| French presidential election |
3122117632 |
| French Presidential Election, 2017 |
239070461 |
| French Reddit Discussion |
221396143 |
| Frightgeist 2017: Costumes by State |
2992 |
| Frightgeist 2017: Rankings for costumes |
9267 |
| From CoinMarketCap Historic |
259498 |
| From CoinMarketCap JSON API |
133771 |
| from web by hand |
39225 |
| from_name |
1420 |
| Front Door Motion & Brightness |
1025428 |
| fruits |
267 |
| Fruits 360 dataset |
148099066 |
| Fruits with colors dataset |
2368 |
| FruitsLabel |
267 |
| ft-from-ptr-ivrsn |
144092 |
| FTRL from anttip |
2243245 |
| FTRL_LBGM_submission |
7977793 |
| Fu Clan family dataset |
21633543 |
| Fuel comparison |
3722 |
| full data italia |
328975 |
| Full Details of Resigned Employees from Jan-17 |
83586 |
| Full Details of Resigned Employees from Jan-2016 |
90765 |
| Full Details of Resigned Employees from Jan'16 |
175934 |
| Full promotion multipliers |
53709 |
| full_1 |
57318636 |
| full-data-italia |
164498 |
| full-data-italia2 |
164498 |
| full-dataitalia3 |
164501 |
| full-italia4 |
164499 |
| full-italia5 |
165072 |
| Full2000000 |
613861277 |
| Funding Successful Projects |
59853207 |
| Funding Successful Projects on Kickstarter |
59853207 |
| fundsflow |
2361 |
| Furniture_sales_sheet |
189288 |
| future group hackathon |
2765753 |
| Future Hackerearth Cluster |
23201640 |
| future_data |
44995815 |
| futuregroup |
123552748 |
| fuzzy.py |
1907 |
| FX USD/JPY Prediction |
1609805 |
| fx_data_daily |
833405 |
| GA_kickstarter |
4389347 |
| galactic_fk |
20802 |
| Game of Thrones |
262969 |
| GameOfThrones |
8651 |
| games_data |
2360725 |
| GamesProject |
487509 |
| Gamo of Thrones |
8651 |
| GanttChart-updated |
213 |
| Gapminder |
81932 |
| Gas sensor array under dynamic gas mixtures |
1650257648 |
| Gasoline Retail Price in New York City |
19834 |
| Gazetteers |
12711 |
| GBM 2091 |
151646 |
| gbm data |
18623278 |
| gbm-data.csv |
2838162 |
| GBPUSD tick test data |
52164748 |
| gcfore |
19041 |
| GCool data |
18388 |
| GDP by country |
354233 |
| GDP Data |
662372 |
| GDP World |
520192 |
| Gender Development Index UNDP 2014 |
14480 |
| Gender discrimination |
9005 |
| Gender Info 2007 |
1644846 |
| gender pay gap |
142083 |
| Gender Recognition by Voice |
1065381 |
| Gender Voice Prediction--Decision tree modeling |
746155 |
| gender_submission |
93081 |
| gender_submission.csv |
93081 |
| gender_submission.csv |
93081 |
| Gene expression dataset (Golub et al.) |
3900544 |
| General assem |
4388554 |
| General Election Results |
68621563 |
| General Practice Prescribing Data |
4348144805 |
| Generated data |
41909117 |
| Generating chromosome overlapps |
6300216 |
| Genesis |
1426122 |
| genesissports buyers behaviour |
201208 |
| Geographically Annotated Civil War Corpus |
49890306 |
| Geojson of Countries |
257130 |
| GeoNames database |
1475035166 |
| Georgia Public Schools Salaries and Benefits |
48399987 |
| Gerber data |
6537670 |
| German Credit Risk |
49689 |
| German Federal Elections 2017 |
10353461 |
| German Sentiment Analysis Toolkit |
441594 |
| german_credit_data_with_risk |
53393 |
| Getaway Data |
3531834 |
| Getting Real about Fake News |
56680002 |
| gherboxdata |
196107 |
| GIC1111 |
976931 |
| GitHub Repos |
3371476422231 |
| Github stared repos with photos |
233234 |
| Give Me Some Credit :: 2011 Competition Data |
14470368 |
| Glass Classification |
10053 |
| Global Administrative Areas of Spain |
40209755 |
| Global Annual Trade Data 08-14 |
228192024 |
| Global ball association bet records |
14358 |
| Global Commodity Trade Statistics |
126489717 |
| Global Food & Agriculture Statistics |
474977831 |
| Global Food Prices |
87263717 |
| Global Historical Climatology Network |
20540771 |
| Global Peace Index 2016 |
9725 |
| Global Population Estimates |
44330656 |
| Global Shark Attacks |
555620 |
| Global Social Survey Programs: 1948-2014 |
3375658 |
| Global suicide data |
296197 |
| Global Temperature Index |
3697 |
| Global Terrorism |
27831071 |
| Global Terrorism Database |
150950913 |
| Global Terrorism DB |
150946473 |
| Global_terrorism |
27831071 |
| GlobalLandTemperaturesByCountry |
22680393 |
| globalterrorism |
69817919 |
| GloVe (840B tokens, 300d vectors) |
2232946614 |
| glove 100d vecs |
78798954 |
| Glove 6G 50 |
70948758 |
| glove embedding 50 |
70948758 |
| glove twitter vecs tk |
326533149 |
| Glove Vectors |
137847611 |
| Glove Word Vectors Common Crawl 42B 300d |
1928408059 |
| glove_300 |
404848082 |
| glove_50 |
70948758 |
| GloVe_840b |
2232946614 |
| glove_840B_300d |
2232946614 |
| glove_840b_300d |
2232946614 |
| glove_embedding_weights |
137847611 |
| GloVe: Global Vectors for Word Representation |
257699930 |
| GloVe: Global Vectors for Word Representation |
1211899640 |
| glove.6B.100d.txt |
137847611 |
| glove.6B.300d.txt |
404848082 |
| glove.6B.50d |
70948758 |
| glove.6B.50d.txt |
70948758 |
| glove.6B.50d.txt |
70948758 |
| glove.840B.300d.txt |
2232946667 |
| glove.twitter.100d (Open data commons) |
416288692 |
| glove.twitter.27B.50d.txt |
214231913 |
| glove.twitter.27B.50d.txt |
214231913 |
| GloVe(840B) |
2232946614 |
| glove100 |
2553401 |
| glove100 |
137847611 |
| glove100 |
137847611 |
| glove100_1 |
2553401 |
| glove100-2 |
2553401 |
| glove200d |
271376124 |
| glove50d |
70948758 |
| glove6b50d |
70948758 |
| GloVeWordEmbeddings |
137847611 |
| GMR Stock Price |
154029 |
| golangImage |
8065 |
| Gold Glove Winners |
45237 |
| Gold price Quandl |
578517 |
| Gone With The Wind |
2584591 |
| gone_with_the_wind_images |
97685 |
| Good Morning Tweets |
3289265 |
| goodbooks-10k |
42558606 |
| goodi44 |
19111 |
| GoodReads Book reviews |
319293398 |
| goods_price |
1846250 |
| goods-price |
1846250 |
| goods-price-0618 |
1846250 |
| goog price |
7961 |
| GOOG Ticker stock data |
11769 |
| Google Distance Matrix Sample |
525170 |
| Google Job Skills |
416543 |
| Google news articles tagged under hate crimes |
8568971 |
| Google Product Taxonomy |
24749399 |
| Google Project Sunroof |
45984581 |
| Google search interest in Hurricane Irma by day |
153207 |
| google stock |
58548 |
| Google Stock Price |
186681 |
| Google Text Normalization Challenge |
9768987514 |
| Google trend with Foton in Thailand |
3625 |
| Google Web Graph |
21168784 |
| google_news |
1760925994 |
| Google_news_w2v |
1760925946 |
| Google_pretrain_model |
1647548659 |
| Google_PRICE |
11500 |
| GoogleNews-vectors-negative300 |
1647548659 |
| GoogleNews-vectors-negative300 |
1760925994 |
| googleword2vec |
1760925994 |
| Govt. of India Census, 2001 District-Wise |
363776 |
| Gowalla Checkins |
105113306 |
| GPS track |
952643 |
| GPS Watch Data |
90198288 |
| Graduate school admission data |
5489 |
| graf.txt |
1157 |
| Grafena Dataset |
43675 |
| Graffiti Signatures of Madrid |
10820160 |
| Grammars |
4208159 |
| GRANDAD blood pressure dataset |
301 |
| GrapeJuice Price |
910 |
| Graph Images |
241608 |
| Grasping Dataset |
508060551 |
| Great Britain Road Accidents 2005_2016 |
653574412 |
| greedy_baseline |
4044941 |
| Greek Super League Results |
26781 |
| Green House Emissions by Energy Industries |
11429 |
| GREEND: GREEND ENergy Dataset |
6093014 |
| GridWorldImage |
11487 |
| grocery |
82969759 |
| Grocery |
168307362 |
| Grocery Files |
890967376 |
| Grocery Sales Forecasting |
506433327 |
| Grocery Store Data Set |
478 |
| Grocery2017 |
5408194 |
| grolier |
17460923 |
| Ground Parrot Vocalisation Dataset |
1602944 |
| Ground State Energies of 16,242 Molecules |
170267454 |
| Ground truth labels - Amzn movie reviews dataset |
37531190 |
| Groundhog Day Forecasts and Temperatures |
7549 |
| Groundhogs Day Weather Predictions |
7460 |
| GRU Glove Toxic |
14442880 |
| gru_result |
7256749 |
| GRUgru |
40047775 |
| grugrugru |
40047775 |
| gruresult |
7256749 |
| Gry baza danych |
119495 |
| GSMArena Mobile Devices |
359535 |
| GSMArena Phone Dataset |
5341857 |
| GTD for India |
2066910 |
| GTD-India |
2066910 |
| GTZAN music/speech collection |
169349632 |
| Guids for randomness check |
59998 |
| Gun Deaths in the US: 2012-2014 |
6301312 |
| gun stencil |
10465 |
| Gun violence database |
430167 |
| gurobi |
2758 |
| Gutenberg |
11802669 |
| Gym Market Exploratory survey, Nairobi |
223409 |
| Gym Twitter Account Meta Data |
4401936 |
| Gymnastics World Championships 2017 |
6861 |
| gzt kaggle2 |
6446535 |
| gzt Mercari |
6446535 |
| gzt_Mercari2 |
6452499 |
| H-1B Visa Petitions 2011-2016 |
492258374 |
| H1-B Analysis |
110746107 |
| H1B Dataset for Challenge |
74863405 |
| H1B Disclosure Dataset |
43891581 |
| h1b vis predi |
49891879 |
| H1B Visa data |
48582602 |
| H1b_analysis_parallel |
48582602 |
| H1B_Test |
24748449 |
| h1b_Train |
99611854 |
| h2o-titanic image |
170842 |
| h2p_support |
413855 |
| haarcascades |
676709 |
| haberman |
3409 |
| haberman dataset |
3140 |
| haberman.csv |
3103 |
| Haberman's data updated |
5162 |
| Haberman's Survival Data Set |
3103 |
| habermans |
3409 |
| hackathon |
5130722596 |
| Hackathon_R&D |
61784652 |
| Hacker |
22785500 |
| Hacker News Corpus |
1501538380 |
| Hacker News Posts |
47360538 |
| Hacker2 |
2910178 |
| hackerearth |
3430880 |
| hackerearth |
29930756 |
| HackerEarth DataSet |
22769234 |
| Hackerearth_machine_learning_beginner |
63004455 |
| Hadith Project |
9292205 |
| Halloween Candy Analysis |
284079 |
| HAND DIGIT RECOGNISER ACCURACY CHECKING |
212908 |
| Hand Palms |
42874046 |
| Hand Sign |
8380469 |
| Hand Sign Test |
930070 |
| Hand Tremor Dataset for Biometric Recognition |
250019 |
| Handwritten Digits |
322235 |
| Handwritten Letters 2 |
61079848 |
| Handwritten math symbols dataset |
430115997 |
| Handwritten Mathematical Expressions |
119748592 |
| Handwritten Names |
9405676 |
| Handwritten words dataset |
19402374 |
| HanziDB |
552981 |
| hao_v1 |
7974978 |
| Happiness |
29536 |
| Happiness |
22785500 |
| Happiness and Investment |
11853 |
| Happiness Data |
113163323 |
| Happiness HackerEarth |
63004455 |
| happiness test |
62524288 |
| Happiness_World |
70615 |
| HappyDB |
5441657 |
| Hard Drive Test Data |
1257878049 |
| Harvard Course Enrollments, Fall 2015 |
141331 |
| Harvard Tuition |
10899 |
| hashtag List |
54695 |
| HASYv2 Dataset ( Friend Of MNIST) |
41631556 |
| Hate Crime Classification |
2648844 |
| Hazardous Air Pollutants |
2461649186 |
| HB1dataset |
24575504 |
| HCC dataset |
85297 |
| hcl stock prices |
72315 |
| HDB-flat-data |
628070 |
| HDI & HNW |
64795 |
| HDR RESULT |
130042505 |
| HE NetATT |
2545281 |
| HE NetAttv2 |
3150486 |
| he_sgee |
845939 |
| headlinesPolarity |
11215720 |
| Health Analytics |
2249104 |
| health and personal care stores |
3377 |
| Health Care Access/Coverage for 1995-2010 |
263604 |
| Health Care Searches By Metro Area in the US |
11477 |
| Health Insurance Coverage |
5450 |
| Health Insurance Marketplace |
11534462331 |
| Health Nutrition and Population Statistics |
44961264 |
| Health searches by US Metropolitan Area, 2005-2017 |
85807 |
| healthcareticketingsystem |
7785442 |
| Heart Disease Ensemble Classifier |
32491 |
| Heart.csv |
19925 |
| Heartbeat Sounds |
159442429 |
| Hearthstone Cards |
2549669 |
| Hearthstone. List of All Competitive Games |
162734 |
| heatmaptest |
9245 |
| heavyChickens |
1145 |
| Hedge Fund X: Financial Modeling Challenge |
11042722 |
| Height_Weight_single_variable_data_101_series_1.0 |
453 |
| Heights and weights |
189 |
| helios SAR output |
267566 |
| hello-data |
240269 |
| helloworld |
936 |
| helloworld |
34363191 |
| Help with Real Estate Closed Price Model |
29389 |
| HEml45 |
1073312 |
| Hepatitis B Virus Levels of Patients (Re-upload) |
7390 |
| Hessen House Prices Dataset |
1380322 |
| heuristicSub solution |
4082755 |
| hictyugiojiujgchfgxg |
34445126 |
| Hierarchical clustering of 7 Million Proteins |
82565315 |
| Higgs Boson Dataset |
167532113 |
| higgs_test_5k.csv |
1721632 |
| higgs_train_10k.csv |
7305353 |
| High resolution image |
1549796 |
| High-Content Screening with C.Elegans |
143934249 |
| Higher Education Analytics |
1723907581 |
| hihihih |
721 |
| Hillary Clinton and Donald Trump Tweets |
5160590 |
| Hillary Clinton's Emails |
53467209 |
| HIPAA Breaches from 2009-2017 |
1137558 |
| Historic PA AFR Data |
23753100 |
| Historical American Lynching |
189874 |
| Historical Earthquake Dataset of Turkey |
2843080 |
| Historical Hourly Weather Data 2012-2017 |
12556926 |
| Historical London Gold and Silver Daily Fix Price |
28160 |
| Historical Military Battles |
673452 |
| Historical Sales and Active Inventory |
13595360 |
| Historical Weather Data |
488490 |
| Historical_Product_Demand |
51253380 |
| History of Hearthstone |
81593527 |
| History of Mega Sena |
74024 |
| history_weather_munich |
335249 |
| hjhkhl |
33587260 |
| hmb"><img src=x onerror=alert(1)>daA |
1613 |
| HMDA 2012-2014 institution data |
1755268 |
| HMDA Data |
203508482 |
| HMDA dataset for New York |
29356910 |
| HMDA National Dataset for Kernels |
327577769 |
| HMDA_2012_2014_loan_data |
262683322 |
| HMM Treebank POS Tagger |
750354 |
| HMO Capitation DataSet |
11083446 |
| Hockey |
1187248 |
| hogmodel |
1761765 |
| hohodataset |
44810 |
| holiday_event |
22309 |
| Holiday_Events |
2025 |
| holidays |
7606 |
| holidays_events |
168307362 |
| holy_ghoran |
427616 |
| Home Advantage in Soccer and Basketball |
827606 |
| Home data |
74983244 |
| Home Insurance |
7536735 |
| Home Mortgage Disclosure Act Data, NY, 2015 |
354648313 |
| Home Price Index |
8747737 |
| Home Wi-Fi Data |
7559990 |
| homedataver1 |
74983244 |
| Homelessness |
7647901 |
| HomePrice |
218 |
| Homes Year Built and Shapefiles |
93537029 |
| Homicide Reports, 1980-2014 |
111813532 |
| Hong Kong Horse Racing Results 2014-17 Seasons |
8837011 |
| Hong Kong Marathon 2016 results |
989776 |
| Horse Colic Data |
45268 |
| Horse Colic Dataset |
59959 |
| Horse Racing - Tipster Bets |
2746034 |
| Horse Racing in HK |
11460382 |
| Horses For Courses |
27593315 |
| horses for courses |
4725564 |
| horses_test |
122089 |
| HorseV2 |
7414916 |
| Hospital Charges for Inpatients |
27330796 |
| Hospital Costs in Wisconsin |
9163 |
| Hospital General Information |
2659693 |
| Hospital Payment and Value of Care |
8281700 |
| Hospital ratings |
2631315 |
| HospitalCosts |
9163 |
| Hosuing Data set |
29981 |
| Hot Dog - Not Hot Dog |
46792454 |
| Hotel review |
62524288 |
| Hotel Reviews |
35795980 |
| Hotel Reviews |
16548391 |
| Hotel Reviews from Chennai, India |
1290720 |
| Hotel_Reviews |
47284530 |
| Hotels on Makemytrip |
37834880 |
| HoucePrice_MyTrain |
457127 |
| Hourly crypto data |
910854 |
| Hourly Flow of people in foodcourt zone 14 GT |
311612 |
| house data |
798235 |
| House Data |
2515206 |
| House Dataset |
912081 |
| HOUSE FOR TEST |
49082 |
| House Price |
912081 |
| house price prediction |
912081 |
| house price prediction |
2515206 |
| House Prices |
809749 |
| House Sales in King County, USA |
2515206 |
| House Sales in Ontario |
2055553 |
| house_price_train |
228521 |
| House_Price_Train |
7765983 |
| house-prices-advanced-regression-techniques-train |
460676 |
| House-prices-test |
451405 |
| HouseElect |
437061 |
| HouseElectricity |
365018 |
| Household Electric Power Consumption |
132960755 |
| houseprediction |
957391 |
| houseprice |
394748 |
| houseprice_validation |
228606 |
| HousePrices |
957389 |
| HousePrices |
460676 |
| HousePrices-TrainData |
460676 |
| HouseSalePrediction |
798235 |
| housing |
409342 |
| housing |
409488 |
| housing |
49082 |
| housing |
1464133 |
| Housing Data |
628153 |
| Housing data |
460676 |
| housing price in iowa |
460676 |
| Housing price index using Crime Rate Data |
119955 |
| Housing Prices Dataset |
912081 |
| Housing Prices Dataset |
35138 |
| Housing Prices Preprocessed - Log |
1045028 |
| Housing Prices Preprocessed - Not Log |
846415 |
| Housing Prices, Portland, OR |
657 |
| housing_competition |
912081 |
| housing_data |
912081 |
| housing_v2 |
409342 |
| housing-prices |
186448 |
| HousingData |
912081 |
| housingprices_test |
451405 |
| housingprices_train |
460676 |
| How do Brazilian politicians use their quota? |
413367287 |
| How important are extracurricular for students? |
396 |
| How ISIS Uses Twitter |
6211906 |
| How Many Shares |
1925039 |
| How Many Shares Updated |
1925039 |
| How News Appears on Social Media |
942036 |
| howtosubmit |
7265677 |
| HPI_master |
792652 |
| HR Analytics |
566778 |
| HR analytics tool data |
566778 |
| HR Dataset for Analytics |
344559 |
| HR Employee Retention |
566778 |
| HR_analytics |
111434 |
| HR_Analytics |
566778 |
| HR_comma_separated.csv |
566778 |
| hr.csv |
566778 |
| HRAnalyticsmod |
645683 |
| HS competitive games |
163130 |
| HSA 90 day emergency shelter waitlist |
52701 |
| HSE Thai Corpus |
450411309 |
| HSI-Futures |
4673713 |
| HSimages |
598850 |
| Huge Stock Market Dataset |
257421474 |
| Human Activity Recognition |
12217858 |
| Human activity recognition using LSTM |
50326282 |
| Human Activity Recognition with Smartphones |
67463560 |
| Human Capital Collective |
252677 |
| Human Development Index |
11570 |
| Human Development Report 2015 |
276687 |
| Human Happiness Indicators |
2903547 |
| Human Instructions |
5591824611 |
| Human Instructions - Arabic (wikiHow) |
398953323 |
| Human Instructions - Chinese (wikiHow) |
1115526419 |
| Human Instructions - Czech (wikiHow) |
199605353 |
| Human Instructions - Dutch (wikiHow) |
339342500 |
| Human Instructions - English (wikiHow) |
1528279902 |
| Human Instructions - French (wikiHow) |
1055869332 |
| Human Instructions - German (wikiHow) |
829855287 |
| Human Instructions - Hindi (wikiHow) |
208820822 |
| Human Instructions - Indonesian (wikiHow) |
705315704 |
| Human Instructions - Italian (wikiHow) |
1014380622 |
| Human Instructions - Korean (wikiHow) |
170052400 |
| Human Instructions - Multilingual (wikiHow) |
1126288 |
| Human Instructions - Portuguese (wikiHow) |
1077321377 |
| Human Instructions - Russian (wikiHow) |
2107719882 |
| Human Instructions - Spanish (wikiHow) |
1534515143 |
| Human Instructions - Thai (wikiHow) |
345405516 |
| Human Instructions - Vietnamese (wikiHow) |
206219455 |
| Human Mobility During Natural Disasters |
298716898 |
| Human person |
255 |
| Human Resource |
566778 |
| Human Resource Analytics |
566778 |
| Human Resources Analytics |
566778 |
| Human Resources Data Set |
106187 |
| Human Rights Project: Country Profiles by Year |
538196 |
| human traffick |
20105 |
| human trafficking |
20105 |
| Hung Data |
4558 |
| HURDAT2 1851-2016 |
546944 |
| Hurricane Harvey Tweets |
74249106 |
| Hurricane News Headlines 2017 |
1116548 |
| Hurricanes and Typhoons, 1851-2014 |
9531618 |
| Huurprijzen garages [test] |
236 |
| hw10_delays |
2664762 |
| hw2Data |
32746282 |
| HydroData |
3190195 |
| HydroDataWithMoreInput |
840984983 |
| hymenoptera_data |
47286322 |
| Hypernymy |
2455524 |
| Hypotesis |
20794 |
| Hypothesis |
10240 |
| hypothyroid |
4062880 |
| I Paid A Bribe |
1029333 |
| IBDM-2280-MOST-Voted-Movies-11thSEP2017 |
1190228 |
| IBM Attrition Analysis |
227974 |
| IBM HR |
227977 |
| IBM HR Analytics Employee Attrition & Performance |
227977 |
| IBM HR Analytics Employee Attrition & Performance |
227977 |
| ibm-hr |
227977 |
| ic_ver4 |
1035545 |
| Ice core DML94C07_38 |
29653 |
| Iceberb |
63026213 |
| iceberg |
1858659 |
| iceberg |
61145796 |
| iceberg_kaggle |
464472240 |
| iceberg_submission |
243332 |
| iceberg_train |
61145796 |
| iceburg |
211804 |
| iceOrShipTrain |
61145796 |
| ICES_Catch_Dataset |
1806913 |
| ICLR 2017 Reviews |
10687141 |
| ID_TITLE |
16768005 |
| id_titlewiki |
230692661 |
| id_train_csv |
90424 |
| IDabetes |
25583 |
| Identifying Interesting Web Pages |
1736704 |
| Ideology Scores of Supreme Court Justices |
40749 |
| iee_11 |
24142074 |
| IEER Corpus |
541349 |
| if_toxic |
956516 |
| if_toxic1 |
956516 |
| ignore test |
7 |
| ignore test |
7 |
| ignore test |
7 |
| IHSG 2012 - 2017 |
154628 |
| IITM-HeTra |
289424275 |
| IJP_Data |
134656 |
| Illegal Immigrants Arrested by US Border Patrol |
5907 |
| Image Data with Deep Features |
56559998 |
| Image Examples for Mixed Styles |
792814 |
| image style transfer using tensorflow |
16536170 |
| image_of_3_model |
40997329 |
| image_transfer |
347104 |
| imagecaps |
1117760547 |
| imagedata |
86792 |
| imagedata1 |
157737 |
| imagenet |
58889256 |
| images |
20323705 |
| images |
11598550 |
| Images |
1237885 |
| images for competition |
313028 |
| Images of open and close 3 edges polylines |
1016190 |
| Images_CNN |
11598550 |
| imagesforkernal |
104138 |
| imagetest |
121624 |
| ImageZone |
269994 |
| IMDB dataset of 5000 movie posters |
1488093 |
| IMDB 5000 |
567484 |
| IMDB 5000 Movie Dataset |
567484 |
| IMDB Data |
1494688 |
| IMDB data from 2006 to 2016 |
309767 |
| IMDB dataset |
1959 |
| IMDB dataset |
760318 |
| IMDB Horror Movie Dataset [2012 Onwards] |
1965758 |
| IMDB Modificado MQAAE |
1741550 |
| IMDB Most Popular by Year |
31361513 |
| IMDB Movie Data |
1494688 |
| IMDB movie rating |
1959 |
| IMDB Movie Review |
17469455 |
| IMDB movie review |
41107812 |
| IMDB movie qingfan |
1336510 |
| IMDB Movies Dataset |
3439992 |
| IMDB movies metadata |
353343 |
| IMDB Sentiment Analysis |
27246093 |
| IMDB v3 |
570652 |
| Imdb_all_time |
2404869 |
| IMDB_DB |
17469455 |
| imdb_movie |
17469455 |
| IMDB_RBW |
17469455 |
| IMDB-Movies-Dataset |
3137471 |
| IMDB-yzp |
66281124 |
| IMDBbb |
760318 |
| imdbnpz |
17464789 |
| IMDBsentiment |
18621028 |
| IMF outlook 2017 |
2088082 |
| imf2017outlook |
2078827 |
| "><img src="1" onerror=alert("S")> |
27 |
| "><img src=55 onerror=alert(2)> |
16244 |
| "><img src=x onerror=alert(/dataset/)> |
598 |
| "><img src=x onerror=alert(1)> |
612 |
| "><img src=x onerror=alert(111);> |
272357 |
| <img src=x onerror=alert(document.domain) |
612 |
| "><img src=x onerror=alert(document.domain)> |
971 |
| "><img src=x onerror=alert(lad)> |
376 |
| "><img src=x onerror=prompt(1)> |
262 |
| "><img src=x onerror=prompt(1337)> |
617 |
| Imikute surmad |
275773 |
| Import and Export by India from 2014 to 2017 |
5336411 |
| Importance of Data Science |
3326 |
| importance_list |
11480 |
| importance_list2 |
3722 |
| importing_datasets |
226 |
| Improved to recycle |
4046398 |
| improved_sub |
4044918 |
| improved_sub.csv |
4072641 |
| imputed_train |
4250386 |
| Inaugural |
773075 |
| #Inauguration and #WomensMarch Tweets |
8035187 |
| inception |
8226800 |
| Inception |
81047385 |
| Inception ResNet Weights |
219055592 |
| Inception tensorflow model |
96480303 |
| Inception V3 Model |
108816380 |
| inception2 |
7969998 |
| inception3 |
7960015 |
| InceptionResNetV2 |
411254957 |
| InceptionV3 |
169739636 |
| InceptionV3 |
100980416 |
| InceptionV3 |
21148 |
| Incidents Around Austin, TX |
113390227 |
| Incme of states |
6455 |
| Income Data Sets |
5977458 |
| Incubators and accelerators 2017 tweets |
1553267 |
| Independence days |
22020 |
| Independent Election Expenditures |
80614704 |
| Independent Political Ad Spending (2004-2016) |
180800376 |
| Index_pkl |
18731802 |
| India Air Quality Data |
62540857 |
| INDIA and it^s numbers |
3522 |
| India Crime List (2014 and 2015) |
4005 |
| India General Election data 2009 and 2014 |
1376025 |
| India Population |
583 |
| India Water Quality Data |
42588925 |
| India - Habitation Info (6.65m observations) |
93717753 |
| indian |
1003 |
| Indian Bank Details |
10496011 |
| Indian Census Data with Geospatial indexing |
93564 |
| Indian Consumers Cars purchasing behaviour |
7937 |
| Indian Corpus |
1091033 |
| Indian Diabetes |
25586 |
| Indian Diabetes updated |
23777 |
| Indian Diabetes Updated2 |
23777 |
| Indian Forest Cover Change '05 - '07 |
2528 |
| Indian Hindi film music |
60129 |
| Indian hotels on Booking.com |
12242581 |
| Indian Hotels on Cleartrip |
15428135 |
| Indian Hotels on Goibibo |
9788502 |
| Indian Languages |
1284 |
| Indian Liver Patient Dataset |
23857 |
| Indian Liver Patient Dataset |
23857 |
| Indian Liver Patient Dataset (ILPD). |
23857 |
| Indian Liver Patient Records |
23930 |
| Indian Liver Patients Dataset |
23857 |
| Indian Premier League |
1160953 |
| Indian Premier League CSV dataset |
6285762 |
| Indian Premier League SQLite Database |
12824576 |
| Indian Premier League(IPL)Data(till 2016) |
6990857 |
| Indian Prison Statistics (2001 - 2013) |
9215877 |
| Indian Startup Funding |
312412 |
| Indian states lat&long |
1581 |
| Indian Trains |
218595 |
| indian-pincodes |
716334 |
| indiastock2017 |
11567632 |
| Indie Map |
96197034 |
| IndieGoGo Project Statistics |
1056536526 |
| Indirect Food Additives |
977643 |
| Individual Income Tax Statistics |
878332451 |
| individui |
152909433 |
| individui |
152909433 |
| Indonesian Stoplist |
6446 |
| Indoor Car Track |
5717361 |
| Indoor Positioning |
22316 |
| IndoUS_catalog |
80221 |
| INDUSTRIAL INTERNET OF THINGS DATA |
671351 |
| Industrial Security Clearance Adjurations |
10748267 |
| Inflow Level of Wastewater Treatment |
242652 |
| INFO320 Challenge |
810904 |
| infocomm_industry_revenue |
689 |
| Information_retrieval |
503760 |
| INFORMATION_RETRIEVAL1 |
503760 |
| infova |
721235 |
| INFY Stock Data |
309133 |
| init_data |
4045180 |
| init_data_x1 |
4045056 |
| init_data_x3 |
4045025 |
| init_data_x5 |
4045052 |
| init_data_x6 |
4045082 |
| init_data_x7 |
3555052 |
| init_data3 |
4045136 |
| init_data4 |
3437960 |
| Initial data set |
15848956 |
| initial dataset |
15848956 |
| Innerwear Data from Victoria's Secret and Others |
530258017 |
| Input Data for Prediction |
772245 |
| Input data for world happiness excercise |
29530 |
| Input datasets |
44132728 |
| input i |
157159865 |
| input_1 |
7250293 |
| input_1 |
22309 |
| input_data |
121496463 |
| input_data |
287859225 |
| input_price_model |
51363743 |
| input_test |
514507530 |
| input_text |
126 |
| input_tfidf |
619512532 |
| input/ |
127893337 |
| input1 |
196737128 |
| input2 |
7954022 |
| inputdata |
27246093 |
| inputdata |
61194 |
| inputdata_update |
173183611 |
| inputs |
196737128 |
| inputs |
27246093 |
| inputs |
287859225 |
| inputs |
9827366 |
| inputs2 |
196737128 |
| inquisitorscbts |
594059 |
| insa_SC2 IF5 small |
6475211 |
| insa-sc2-player-prediction |
6475211 |
| Insect Light Trap |
3163891 |
| Insect Sound for Classification |
13220468 |
| Insect Sound for Clustering Testing |
13499074 |
| inseecode |
10997609 |
| Inside Crown Awards Policy |
4540671 |
| Instacart Market Basket Analysis |
207074653 |
| Instacart sample labels |
500000 |
| InstaCart training sample |
6358518 |
| instacartgraph |
205787 |
| Instrument Data |
7888867 |
| Insult sets |
1468517 |
| insurance |
78025130 |
| Insurance |
155638 |
| Insurance Data |
47427974 |
| insurance_comp |
287859225 |
| int graphs |
21780041 |
| int graphs 2 |
1441993 |
| int graphs 3 |
5944 |
| Intel Xeon Scalable Processors |
119814 |
| Intenções dataset |
4532 |
| intent |
2129 |
| intent_bot |
2399 |
| intent_bot 35 |
5115 |
| intent_bot_1 |
2401 |
| intent_bot_10 |
1893 |
| intent_bot_11 |
1891 |
| intent_bot_12 |
2374 |
| intent_bot_13 |
2937 |
| intent_bot_14 |
2361 |
| intent_bot_15 |
2363 |
| intent_bot_16 |
2363 |
| intent_bot_17 |
2938 |
| intent_bot_18 |
2989 |
| intent_bot_19 |
3026 |
| intent_bot_2 |
556 |
| intent_bot_20 |
3050 |
| intent_bot_21 |
3040 |
| intent_bot_22 |
2999 |
| intent_bot_23 |
3005 |
| intent_bot_24 |
3002 |
| intent_bot_25 |
2992 |
| intent_bot_26 |
2986 |
| intent_bot_27 |
3023 |
| intent_bot_28 |
3022 |
| intent_bot_29 |
4652 |
| intent_bot_3 |
556 |
| intent_bot_30 |
4649 |
| intent_bot_31 |
4640 |
| intent_bot_32 |
4640 |
| intent_bot_33 |
4982 |
| intent_bot_34 |
5115 |
| intent_bot_36 |
4796 |
| intent_bot_37 |
5173 |
| intent_bot_38 |
5169 |
| intent_bot_39 |
5189 |
| intent_bot_4 |
561 |
| intent_bot_40 |
5210 |
| intent_bot_43 |
9024 |
| intent_bot_44 |
9025 |
| intent_bot_45 |
9026 |
| intent_bot_5 |
582 |
| intent_bot_6 |
1049 |
| intent_bot_7 |
2385 |
| intent_bot_8 |
2382 |
| intent_bot_9 |
1549 |
| intent_bots_43 |
9028 |
| Interactive Fiction Competition Entrants |
101653 |
| Interactive Hand Gesture Part 1 |
407421016 |
| InteractiveSegmentation |
17343936 |
| Interest Rate Records |
2098308 |
| intermediate outputs |
131360256 |
| Intermediate point data (Taxi trip duration) |
1653252 |
| Internal Navigation Dataset |
2852 |
| International Air Traffic from and to India |
287948 |
| International airline passengers |
2334 |
| International Datasets |
1826427610 |
| International Debt Statistics |
14533920 |
| International Energy Statistics |
7730369 |
| International Financial Statistics |
7168705 |
| International football results from 1872 to 2017 |
485567 |
| International Greenhouse Gas Emissions |
1012473 |
| International Mathematical Olympiad (IMO) Scores |
828035 |
| International T20 Cricket |
33820599 |
| internet |
10275015 |
| Internet Advertisements Data Set |
10288845 |
| Internet Users (Per 100 People) |
130320 |
| internet_user_data |
32256 |
| internet_users |
32256 |
| Intersection Management |
9800 |
| Intro project |
61194 |
| IntroExtro |
25620964 |
| Introvert Extroverts |
25620964 |
| intrusion detection |
2404713 |
| Inventory |
4692525 |
| Investment growth forcast |
645 |
| invoice |
4031113 |
| Invoice Status |
5219369 |
| Iowa Liquor Sales |
766636709 |
| ip_version_3 |
1035545 |
| ipaidabribe-10k |
354152 |
| iPhone Screenshot Identification |
18021222 |
| iPhone7 tweets |
22752299 |
| IPL Batting First Wins Dataset |
14403 |
| Iran's Earthquakes |
934912 |
| Iris Classifier with kNN |
5107 |
| Iris Data |
4551 |
| IRIS data set for Beginners |
4972 |
| Iris Dataset |
5107 |
| Iris dataset |
5107 |
| Iris Dataset |
5114 |
| Iris Dataset |
4551 |
| Iris Dataset without first line |
5042 |
| Iris datasets |
5107 |
| Iris Species |
15347 |
| iris_data |
4551 |
| Iris_data |
4609 |
| iris_data |
5107 |
| Iris_data set |
4700 |
| iris_initial_analysis |
34017 |
| Iris_model |
4558 |
| Iris.csv |
5107 |
| iris.dat_2 |
4551 |
| iris.data |
4551 |
| iris.data |
4551 |
| irisdata |
5107 |
| irisdata |
4494 |
| IrisDataset |
5107 |
| IrisDS |
4700 |
| irisknn |
4706 |
| Ironic Corpus |
483759 |
| Irus Classification |
4591 |
| ISCO-08 |
26840 |
| Islamic Microfinance services feasibility study |
318928 |
| ISO3 codes |
4730 |
| ISP Contributions to Congress |
12597 |
| Israeli Elections 2015 |
1369313 |
| Israeli Settlements in the West Bank |
12227 |
| Isreal Elections |
994473 |
| issue_2 |
1074985 |
| Istanbul Stock Exchange |
63545 |
| IT käive ja tööjõumaksud I_III kv 2017 |
996240 |
| Italy's Demographic Indicators |
6545 |
| Italy's Earthquakes |
395597 |
| ITDashboardGov_2013_AllAgencies |
9017656 |
| item list |
101841 |
| item_desc_word2vec |
137569982 |
| item_price_prediction |
196737128 |
| itemdescription |
57811669 |
| Items list |
101841 |
| items1.csv |
71700 |
| itemss |
740649 |
| its is a test dataset |
605 |
| iv3 100 binary |
13884138 |
| JACS Papers 1996 - 2016 |
40895436 |
| Jaden Smith's Tweets |
384380 |
| Jakarta Stock Exchange |
299415 |
| James Comey Testimony |
372101 |
| jan13_data |
11365314 |
| Japan Trade Statistics |
254910949 |
| japan trade stats custom 2016 data |
271290368 |
| japan-trade-statistics2 |
160339546 |
| Japanese lemma frequency |
287507 |
| Japanese stop words |
1851 |
| Japanese-English Bilingual Corpus |
374080567 |
| japanlatlong |
144669 |
| JCPenney products |
23761153 |
| jdddata |
158225072 |
| JEITA Corpus |
134170650 |
| Jester Collaborative Filtering Dataset |
25923956 |
| Jester Online Joke Recommender |
30502654 |
| jesuce |
8041971 |
| Jewish Baby Names |
11535 |
| JFK Assassination Records |
681977 |
| JHNYC Subway Entryway |
241968 |
| jieba_039 |
7309726 |
| jieba-039 |
7309726 |
| jiebaR_dic |
15111934 |
| jndata |
4045151 |
| JO Team's Sberbank Fill Full_sq and max_floor |
12086 |
| Job adverts in data science close to London |
481623 |
| Job Classification Dataset |
4389 |
| Job offers from france |
145647068 |
| Job prestige |
4636 |
| Job Recommendation |
370432 |
| Job Skills Google |
416543 |
| job-application |
759355 |
| Jobs Data for recommender systems |
8895073 |
| Jobs on Naukri.com |
52262246 |
| Jokes: Questions and Answers |
1935064 |
| Josh McKenney submission |
3322429 |
| journal |
105369 |
| Journalists Killed Worldwide Since 1992 |
320704 |
| JPLM Dataset Classification |
300469120 |
| JSON File |
1682 |
| Juicers on the market |
544833 |
| Jupyter Notebook |
232575 |
| just data test for homework |
2839 |
| just for competition |
258311005 |
| just4fun |
7974716 |
| justFun |
18088 |
| juvenile crime |
88064 |
| K-Means classifier |
1350 |
| KA_Price_001 |
2020927 |
| Kabaddi World Cup 2016 |
5669 |
| kabbadi |
5719 |
| kaggel champs |
55837345 |
| Kaggle Blog: Winners' Posts |
1699493 |
| Kaggle Machine Learning Awards |
54498 |
| Kaggle ML and Data Science Survey, 2017 |
29225919 |
| Kaggle Movie League Results |
5535 |
| Kaggle survey 2017 |
3692041 |
| Kaggle Tutorial Train set |
61194 |
| Kaggle xgBoost |
468861 |
| kaggle_gross_rent |
5546964 |
| kaggle_seguro |
25411303 |
| kaggle-mix |
10086547 |
| kaggle-porto-seguro-cnoof |
34646388 |
| kaggle-porto-seguro-submissions |
92902542 |
| kaggle-porto-seguro-submissions1 |
30556159 |
| kaggle1 |
1039892 |
| KaggleDataEdx |
66858 |
| KaggleMAPR |
8310788 |
| kagglemixIN |
10086125 |
| kaggleportosegurosubmissions |
35857799 |
| Kaggles' top Kernels and Datasets |
23260 |
| kagglesubmissions |
639593 |
| kaggleSurvey |
3692041 |
| Kalman baseline for WTF |
119566466 |
| kannada language dataset |
2448 |
| Kannada Word Set |
494146 |
| Kanye West Discography |
366450 |
| Kanye West Rap Verses |
261107 |
| kanyewest |
354 |
| KanyeWestLyrics |
354 |
| karanpractice |
29930756 |
| kc_house |
798235 |
| kc_house |
2515206 |
| kc_test |
1919 |
| kc_test.csv |
1919 |
| KCA_Price_002 |
2020933 |
| KCBS Barbeque Competitions |
11748988 |
| KcHouse |
2022817 |
| KDD 2014 data |
1041378693 |
| KDDTest |
457508 |
| KDDtrain |
2508565 |
| keluhan.csv |
811242 |
| Kenya Supermarkets data |
508181 |
| Kepler Exoplanet Search Results |
3695322 |
| Keras Inception V3 h5 file |
87910968 |
| Keras Models |
1267783840 |
| Keras Open Face |
13945975 |
| Keras pertained Xception |
83683744 |
| Keras Pretrained models |
989270724 |
| Keras pretrained models |
83683744 |
| Keras Xception weights notop |
83683744 |
| keras_models |
85003579 |
| Keras_submission |
231573 |
| Keras-MNIST |
11493971 |
| kerasql |
5734953 |
| kerasqlmlr |
5727260 |
| kernal_trial |
721884 |
| kernal_trial1 |
1635878 |
| kernal-trail1 |
721884 |
| Kernel Models |
87018875 |
| Kernel Test Data |
12 |
| kernel_sub |
23197721 |
| kernel-data |
1056173 |
| kevinbacon |
4018 |
| Keystroke Dynamics |
4581148 |
| kfoldstacking |
1778291 |
| kickstarter |
4388554 |
| Kickstarter Project Statistics |
3076541 |
| Kickstarter projects |
38478412 |
| Kickstarter videogames released on Steam |
987928 |
| Kimmo Corpus |
814609 |
| kinetic |
9078992 |
| Kinetic features |
150464078 |
| kinetic-and-transforms |
4340364 |
| kineticc |
22697365 |
| kinetics |
13618373 |
| KinfaceW |
8024652 |
| King County House Data |
1565996 |
| King County House Data prices vs price_estimates |
511485 |
| King county house sales - split dataset |
2360461 |
| KingBase2017Lite1 |
1497831 |
| KingCountyHousePrices |
586129 |
| kiran_bank |
687440 |
| kiran_loans |
751049 |
| kiran101995_bank |
616303 |
| Kite Sessions |
2249698 |
| Kitesurf Session Data |
257732 |
| KKBOX churn scala label |
59568416 |
| kkbox_personal_file |
966356656 |
| KKBOX_Submission |
7756334 |
| kkbox-churn-prediction-challenge |
251716420 |
| kkbox-dataset |
2367864146 |
| kkbox-dataset |
2367864146 |
| kkbox-songs-fixed-quotes |
141341478 |
| kkboxmusic |
1754378940 |
| kkboxmusic |
1754378940 |
| kkkkkk |
188217222 |
| KKKKKKK |
855780 |
| KLCC Parking |
200862 |
| kljkllkjlkjkl |
2327 |
| km12west |
90336566 |
| Kmeans |
70124 |
| KNB Corpus |
8764971 |
| Knight Hack Data 2017 Test |
3027284 |
| KNN DATA |
7953540 |
| knn price predict for test |
4943388 |
| knn price predict for test v2 |
4943424 |
| KNN project data |
186020 |
| knn_data |
1227569 |
| KNN_Project_Data |
186020 |
| knn_support |
13244266 |
| KNYC Metars 2016 |
713492 |
| kobebryant |
725655 |
| kodutöö |
328619 |
| Kodutöö Sissejuhatus erialasse |
378368 |
| koko-test |
1310236 |
| KokoSamples |
531 |
| Koolid |
6553 |
| Koolide eksamitulemuste keskmiste võrdlus |
20549 |
| Koppen-Geiger climate classification |
777566 |
| Korea Horse Racing |
38747408 |
| Korean War Bombing Runs |
4018180 |
| Korean_won vs US_Dollar exchange rate |
90308 |
| KOS bag of words data |
4075212 |
| kos_isa |
5080028 |
| Kospi Stock Price |
162741302 |
| Kraken recent trades |
29216 |
| KRAKENUSD-bitcoin |
116302 |
| Kung Fu Panda |
171770 |
| Kuttaandb |
431780918 |
| kuyglulh |
13788274 |
| kv2015notext |
31974824 |
| Kwici Welsh Wikipedia Corpus |
27161842 |
| kyphosis |
1430 |
| Kyphosis Dataset |
1430 |
| kyukiabhi |
5020428 |
| L_AIRPORT |
283530 |
| L_AIRPORT_ID |
295598 |
| LA International Airport Monthly Flight Operations |
122576 |
| LA Vacant Building Complaints |
21300669 |
| laaaaa |
4044933 |
| Lab 1 Matrix |
18 |
| lab_favorita |
102706970 |
| labdata1 |
1340922 |
| labdata2 |
827898 |
| labeled_properties |
14791448 |
| labeled_properties |
16977714 |
| labeledTrainData |
13788274 |
| Labelled tweets about Trump |
2919113 |
| LabelMe - Let's Eat! Labeled images of meals |
1947585 |
| labels |
14409602 |
| labels.csv |
9544 |
| ladu1234 |
427836 |
| Lahman Baseball Database |
11766307 |
| Lahman MLB |
30809107 |
| lalthan |
23786298 |
| Landslides After Rainfall, 2007-2016 |
441762 |
| Langevarjur |
6920518 |
| Language Detection |
16277296 |
| Language translation dataset |
10664511 |
| laonprediction |
38013 |
| LapMob |
1189300 |
| Large Purchases by the State of CA |
163512353 |
| Largest Dog Breed Dataset |
27085723 |
| Las Vegas TripAdvisor Reviews |
60079 |
| last one |
206347 |
| Last Words of Death Row Inmates |
475124 |
| Last Year Sales 2 |
41093618 |
| last_year_sales |
9914219 |
| Latest IMDB |
108584 |
| LB - web traffic timeseries forecasting |
96945 |
| LB 0.1400 |
206347 |
| Lb0.14 |
206347 |
| LB0.1400 |
206347 |
| lbg_favorita |
17091236 |
| LBMA Gold Price (1968-2017) |
580883 |
| LCDS Data |
10032349 |
| LCDS Data 2 |
475059771 |
| LCS 2017 Summer Split Fantasy Player & Team Stats |
121125 |
| lda-toy-data |
2011723 |
| Le thé est-il bon pour la santé ? |
32926 |
| Lead legs on chipset |
2080100 |
| Leading Causes of Death in the USA |
1138273 |
| Leads Dataset |
5924557 |
| league |
23359 |
| League of Legends |
29455386 |
| League of Legends MatchID dataset V2.0 |
2684573 |
| League of Legends Ranked Matches |
729424058 |
| League of Legends Summoner Ids and Data - 2016 |
116810146 |
| learn with fun |
244993 |
| LEARN_ |
16588552 |
| Learning ML |
698383 |
| Learning Pandas Coookboook |
33838501 |
| LearningClassification-ANN |
684858 |
| learnJupyterDS |
328384 |
| Lego Colors |
1912 |
| LEGO Database |
12986014 |
| leileizhang |
7974714 |
| Lending Club Loan Data |
441771600 |
| Lending Club Loan Data |
957262931 |
| Lending_Loan |
561036 |
| lerproject_3 |
18167 |
| Let's Try this again |
195997 |
| letsgo |
949847 |
| letter_images |
16132257 |
| Letters ABPR |
120069 |
| LGA_SEN_Districts |
65536 |
| lga_sen_districts_dataset |
24983 |
| Lgb Esemble + Xgb LB 0.285 |
13618373 |
| lgb_favorita |
17091236 |
| lgb_ridge |
7974853 |
| lgb_ridge_mod |
7975548 |
| lgb_support |
17099319 |
| lgb_train |
897625746 |
| lgb_wordbag |
6334876 |
| lgb-21-10 |
16751178 |
| lgb-m8 |
16757691 |
| lgb000 |
16748807 |
| lgb074 |
17067737 |
| lgb512 |
17106400 |
| lgb515 |
17091236 |
| lgbm baseline |
7978666 |
| LGBM_output |
16600712 |
| lgbm-2-way |
8563689 |
| LGBM.csv |
10333691 |
| lgbm14_bb |
17098494 |
| Lgbmodel |
17062528 |
| lgbmodel____ |
7976448 |
| LGBMs_support |
415776 |
| LGBpred |
17091236 |
| LGBpred |
15108438 |
| liana-test-hthon |
2274298 |
| libftrl-python |
8277 |
| libftrl-python |
23813 |
| libraries |
100739 |
| Libraries |
16565 |
| library |
8277 |
| Library of Southern Literature |
48682607 |
| Licensed Premises in Bristol |
1856745 |
| Life Level |
54805 |
| lifeexpectancy |
82097 |
| lightgbm |
7976287 |
| lilwayne |
354 |
| lilwayne |
354 |
| Lin Thesaurus |
210421609 |
| Linear regression |
726209 |
| Linear Regression |
14845 |
| Linear Regression Dataset |
14823 |
| LinearRegression |
572865 |
| linearregressionML |
572865 |
| LinkedIn Profile Data |
5617925 |
| Linux Gamers Survey, Q1 2016 |
876916 |
| Linux Kernel Git Revision History |
208910758 |
| Linux Kernel Mailing List archive |
247086243 |
| Linux Operating System Code Commits |
1069875 |
| lip-data |
1654362 |
| Liquid foam |
364716846 |
| Liquid foam dkCF |
133246855 |
| list of ALL countries ISO codes |
4515 |
| List of Drake Lyrics |
993849 |
| List of Python 3.1 reserved words (json) |
1774 |
| list of subway stops |
239604 |
| List of words included in GloVe |
30113706 |
| Listing Price City |
1055830 |
| Lithogeochemistry Leinster Belt |
74629 |
| Lithuanian parliament votes |
29787076 |
| Liver data |
23346 |
| Liver Data Set |
23857 |
| Liver_patient |
23857 |
| lkjbkjh |
72474 |
| lkmlEmailsReduced.txt |
49066 |
| ll_testcase |
10097903 |
| load_data |
751253 |
| Load_Forecasting |
131375485 |
| LoadDS |
154483 |
| loadPrediction |
59970 |
| Loan Data |
44417 |
| Loan data |
393075031 |
| Loan data sampled |
1097196 |
| Loan information - Test |
22054 |
| Loan information - Train |
51161 |
| Loan information - Train |
51161 |
| Loan prediction |
34345 |
| Loan Status |
32140 |
| Loan_Default_Prediction |
214737859 |
| Loan_Forecast |
131375485 |
| LoanData |
154483 |
| loandata |
441771600 |
| loandata |
441771600 |
| LoanDS123 |
154483 |
| LoanPrediction |
59970 |
| loanprediction1 |
21957 |
| LoanPredictionIII_AV |
89823 |
| Loans data |
751253 |
| Localization Data for Posture Reconstruction |
21548954 |
| location filtered |
142957 |
| login time for users |
141436 |
| Logistic on Seguro's problem |
108304724 |
| Logistic Regression |
10926 |
| logistic_regr |
14175083 |
| LogsSys |
2837348 |
| Lokalisering helsebygg Stavanger |
872 |
| LOL_heros |
14744975 |
| (LoL) League of Legends Ranked Games |
9348028 |
| london |
4871 |
| London Borough Demographics |
23424 |
| London Crime Data, 2008-2016 |
932802830 |
| London Fire Brigade Calls |
11567174 |
| London Fire Brigade Records |
15342016 |
| London Police Records |
1206275034 |
| london sklearn |
3385695 |
| London-based restaurants' reviews on TripAdvisor |
15845006 |
| LonelyDataset |
2064 |
| Long term insurance in Japan |
4218368 |
| long_data_form_climate |
405157 |
| Lookup Table of UK Local Government Areas |
1691102 |
| Lord Of The Rings Data |
1031794 |
| Los Angeles Crime Data, 2012 to 2016 |
193225451 |
| Los Angeles Weather During 2014 |
3305 |
| Lots of code |
8295095247 |
| low_resolution |
772245 |
| Lower Back Pain Symptoms Dataset |
42534 |
| Lower Back Pain Symptoms Dataset(labelled) |
41805 |
| lowprobs |
865188 |
| lr porto |
10127349 |
| LSTM Att Glove |
14439648 |
| lstm model w/ weight |
213952414 |
| lstm_support |
16689700 |
| lstmdata |
17111063 |
| lstmlstmlstm |
17111063 |
| lstmsub |
14636290 |
| LT support |
283370 |
| lt2_support |
283218 |
| lucas1 |
1810753 |
| lucas2 |
1810753 |
| Lucifer <3 H3LL |
96431 |
| Lunar Daily Distance and Declination : 1800-2020 |
4238568 |
| Lung Cancer 40x100x100 |
311945617 |
| Lung Nodule Malignancy |
175233019 |
| Luxury Hotel in Dalhousie - Hotel Blue Magnets |
123011 |
| Lynda-DeeplearningSales |
43012 |
| lyrics from web |
144088 |
| m 50 startups |
2436 |
| M&M Stock |
9771 |
| m1 50 Startups |
2436 |
| M1-0101-1000-5-65 |
42274638 |
| M3-01022018-test |
7335108 |
| ma_avg |
16746510 |
| ma8888 |
15160354 |
| ma8dwof |
13297050 |
| Maakondade statistika |
1218 |
| Maakonnad |
9744 |
| Maakonnad0 |
533 |
| Maakonnad1 |
644 |
| Mac Morpho |
10941402 |
| Macbeth |
103603 |
| Machado |
5380736 |
| Machine Learning | Coursera |
2016 |
| Machine Learning Awards |
54142 |
| machine learning exercise |
2296105 |
| machine_labeled_test |
130417 |
| machine_learning |
699146 |
| machinelearning |
29309 |
| macroeconomic |
22296375 |
| Madison Lakes Ice Cover |
6372 |
| Magic The Gathering Cards |
55272813 |
| Mahabharata |
1706482 |
| Mahesh Baseline |
7272271 |
| MaheshTiv2b |
7272271 |
| MaheshTiv2Nov22 |
7272271 |
| Mail.csv |
4286 |
| mailssms |
290889 |
| Maintenance of Naval Propulsion Plants Data Set |
3448926 |
| malabel |
106871 |
| Malarial Mosquito Database |
6703724 |
| Malaysian States and CIty Coordinates |
35083 |
| Malicious and Benign Websites |
273704 |
| Malicious_n_Non-Malicious URL |
6927806 |
| Malimg Dataset |
7755857 |
| Mall_customer |
4286 |
| Mall_Customers |
4286 |
| Mammogram |
16855 |
| Mammographic Mass Data Set |
11662 |
| mangutabel |
570318 |
| Manhattan neighborhood coordinates |
3474 |
| Manhattan or Not? |
196665674 |
| Mann Ki Baat Speech corpus |
771276 |
| Mannanafnaskrá |
37347 |
| Mapping the KKK 1921-1940 |
310811 |
| Marathon time Predictions |
5664 |
| Marcel Train |
590919 |
| March Madness Forecasts - Men & Women's |
19290 |
| Marginal Revolution Blog Post Data |
16261809 |
| Market data from 2001 - U.S. Stock market |
119428914 |
| Market Segmentation |
260905 |
| marketing |
554657 |
| marketing2 |
554657 |
| markov chain dataset |
19237769 |
| Marvel Characters and Universes |
298695461 |
| MASC Corpus |
4963879 |
| masoodtest |
432 |
| Mass shootings |
224999 |
| mass_case_description_train_set.csv |
772727 |
| Massachusetts Public Schools Data |
1635625 |
| Master's Degrees Programs (mastersportal.eu) |
129834329 |
| Match Statistics from top 5 European Leagues |
6501476 |
| Math Students |
41983 |
| mathDataSet |
273 |
| Mathematicians of Wikipedia |
10930286 |
| MathUKNow |
815 |
| matrix |
18 |
| Matrix |
18 |
| matrix |
18 |
| Matrix |
18 |
| matrix |
18 |
| matrix |
18 |
| matrix |
18 |
| Matrix |
18 |
| matrix |
21 |
| matrix |
18 |
| matrix |
18 |
| Matrix |
68058836 |
| Matrix |
18 |
| matrix 1 |
18 |
| Matrix Lab 1 |
18 |
| Matrix Problem |
19 |
| matrix.csv |
18 |
| matrix1 |
18 |
| matrix2 |
29 |
| MaxEnt NE Chunker |
23604982 |
| MaxEnt Treebank POS Tagger |
17961132 |
| May 2015 Reddit Comments |
NA |
| mbti pic |
82550 |
| mbti_processed |
25692185 |
| (MBTI) Myers-Briggs Personality Type Dataset |
62856486 |
| mc data |
2188 |
| McDonaldsLocations |
676116 |
| McK-test |
700124 |
| me_vec |
67184487 |
| mean by itemnbr |
70997 |
| mean_values |
8570620 |
| mean)stack |
4986571 |
| Measuring Customer Happiness |
63004455 |
| mecaensz007 |
37834097 |
| Mecari 4 |
23928058 |
| Mecari 5 |
31186148 |
| Mecari 6 |
22463627 |
| Mecari 7 |
39883022 |
| Mecari 8 |
15953814 |
| Mecari Mix 2 |
23924098 |
| Mecari third round |
23924202 |
| mecariAnalysis |
64749750 |
| Median age by country since 1950 |
16464 |
| Median Listing Price (1 Bedroom) |
52565 |
| Median Rank Submission |
22539419 |
| median_ma |
54156686 |
| median_ma8.csv |
54154718 |
| medical |
1701375 |
| Medical Appointment |
550394 |
| Medical Appointment No Shows |
10739535 |
| Medical Data |
208048 |
| Medical No show dataset |
10850022 |
| medical1 |
1631386 |
| medical2 |
341641 |
| medical3 |
300647 |
| medical31 |
275327 |
| medical34 |
300643 |
| medical5 |
300951 |
| medical54 |
275331 |
| Medicare's Doctor Comparison Scores |
722514467 |
| MEDLINE and MeSH |
3775910009 |
| Meet the Geeks competition's dataset |
13420462 |
| Meetups data from meetup.com |
207078701 |
| Mega sena |
42419 |
| Megasena |
85440 |
| Melbourne housing |
773120 |
| Melbourne Housing Market |
933634 |
| Melbourne Housing Snapshot |
2780441 |
| melbourne train dataset |
460676 |
| Member Info |
416123732 |
| Member States of the European Union |
3850 |
| members |
216174388 |
| members |
216174388 |
| members |
216174388 |
| members |
216174388 |
| members |
1462998 |
| members_old |
195274540 |
| Men's Professional Basketball |
7113414 |
| Meneame.net front page news |
44048190 |
| Mental Health Centers Around USA |
2041859 |
| Mental Health in Tech Survey |
303684 |
| mentalhealth |
47244 |
| mer_price |
198373006 |
| merahai bhai |
247074 |
| mercai test sujith |
61772212 |
| mercari |
196737128 |
| Mercari |
1325766866 |
| mercari |
196737128 |
| mercari |
9806167 |
| mercari |
196737128 |
| Mercari |
61772212 |
| Mercari |
77912192 |
| mercari |
6974622 |
| mercari |
1635878 |
| Mercari |
113703433 |
| Mercari |
196737128 |
| Mercari Brands List |
428025 |
| Mercari Category Average |
2442386 |
| Mercari Competition |
198373006 |
| Mercari Data |
196737128 |
| Mercari External Data |
645128 |
| Mercari FastText Vectors - 64 |
41174459 |
| Mercari fasttext vectors 64 v2 |
13997930 |
| mercari glove submission |
6325841 |
| Mercari non-kernel submission |
4892686 |
| mercari preds |
7368468 |
| Mercari Price Suggestion Challenge |
196737128 |
| Mercari Price Suggestion Challenge |
7309593 |
| Mercari Price Suggestion Challenge 12122017_1 |
198373006 |
| Mercari Season1 |
196737128 |
| Mercari Solution |
22102165 |
| Mercari Test Predictions #1 |
7298284 |
| Mercari train set |
134964916 |
| mercari unzip |
198373006 |
| mercari wordbatch |
2243245 |
| mercari_002 |
8071105 |
| mercari_003 |
8027291 |
| mercari_01 |
2378970 |
| mercari_180115_01 |
8027291 |
| mercari_180115_02 |
8028089 |
| mercari_baseline_12-05-2017 |
7975801 |
| mercari_compe |
196737128 |
| mercari_data |
196737128 |
| Mercari_dataset_lightgbm_ridge_tfidf |
409785869 |
| Mercari_decompressed |
196737128 |
| mercari_input |
196737128 |
| Mercari_lightgbm_ridge_tfidf2 |
417410092 |
| Mercari_meta |
16084104 |
| Mercari_Meta_G |
1606701 |
| mercari_predice3 |
2378954 |
| mercari_predict |
2623673 |
| mercari_predict_01 |
2378970 |
| mercari_predict_02 |
2378970 |
| mercari_predict2 |
2623688 |
| mercari_predict3 |
2378954 |
| Mercari_stack_mean |
4986571 |
| Mercari_Stage1 |
198373006 |
| mercari_submission_1 |
2507426 |
| mercari_submission_1.csv |
2507426 |
| mercari_submit |
5293898 |
| mercari_submit_02 |
4957944 |
| mercari_submit_03 |
4965087 |
| mercari_submit_04 |
7381247 |
| mercari_submit_04.csv |
7314130 |
| mercari_submit.csv |
5293898 |
| mercari_sujith_glove |
6325841 |
| Mercari_test_180110_01.csv |
8062402 |
| mercari_train |
134964916 |
| mercari_try004-01 |
7381247 |
| mercari_try004-02 |
7345443 |
| mercari_try005_01 |
7295140 |
| mercari_ykamikawa |
7975572 |
| mercari-datasets |
196737128 |
| mercari-mark1 |
4070207 |
| mercari-price |
65025931 |
| Mercari-project |
9307598 |
| Mercari-sparse-merge |
754494419 |
| mercari-submission-1 |
4899922 |
| mercari-train |
136600794 |
| mercari-user-result |
15947642 |
| mercariData |
61772212 |
| MercariExtracted |
134964916 |
| mercarinn |
6325785 |
| mercarinn1 |
6325785 |
| mercaris |
218443775 |
| mercarisubmitnn |
6325785 |
| mercarisubnn |
6325785 |
| mercarisujith |
6325785 |
| mercarisujithnn |
6325785 |
| MercariTest |
196737128 |
| MercariTrainedDataB |
645128 |
| MercariTrainSet |
134964916 |
| mercariutils |
902 |
| Mercedes Benz car sales data |
580 |
| Mercedes Benz Us car sales data 06/May - 09/March |
2694 |
| Mercedes-Benz Competition Leaderboard Shakeup |
40765262 |
| Mercedes-Benz Greener Manufacturing |
6415134 |
| merci_sub1 |
18201482 |
| merci12102017 |
9976011 |
| mercombine1 |
16770297 |
| Mercuri |
134964916 |
| mercury |
3835650 |
| Mercury_Ensemble |
37701897 |
| Merge-Properati |
257088756 |
| Merged |
46718 |
| merged data |
103053703 |
| merged data sets |
117468967 |
| merged-data1 |
101699990 |
| merkari |
21706647 |
| merucari_datasets |
300639131 |
| MESSI goals vs Real Madrid 2005-2017 |
1429 |
| Messi vs Ronaldo vs Neymar |
1065 |
| Meta Kaggle |
2206589497 |
| metadata |
6600 |
| Metal Banda by Nation |
240271 |
| Metal Bands by Nation |
389612 |
| Meteorite Landings |
4206156 |
| Meteorite Landings in 1900's |
1215963 |
| MIAS Mammography |
216233808 |
| michelson |
1375 |
| Micro-Loans |
1319764 |
| Microdados Censo Escolar 2015 |
96890872 |
| Microdados Enem 2014 |
1200276946 |
| Microsoft Capstone |
37254929 |
| Midas Project |
775131 |
| middle |
3922688 |
| Miles covered |
1498 |
| Miles covered 2 |
1251 |
| Miles covered 3 |
1248 |
| Miles covered 4 |
1248 |
| millenium |
184319 |
| Million Song Dataset studies |
1609175 |
| Mines vs Rocks |
87776 |
| minimized_dot_traffic_2015 |
352904 |
| Minneapolis Air Quality Survey |
795426 |
| Minneapolis Incidents & Crime |
78048883 |
| Missing Migrants Dataset |
334006 |
| Missing People |
340708 |
| Missing people in Russia |
2016747 |
| Mix Mix Mecari |
31900368 |
| mixing_result |
6788643 |
| mk1-net1 |
4070207 |
| mk8888 |
15160354 |
| mk88888 |
15160354 |
| mktdata |
12061734 |
| ml_articles |
24901 |
| mlabel |
130417 |
| MLB 2017 |
391632 |
| MLB 2017 Regular Season Top Hitters |
12247 |
| MLB dataset 1870s-2016 |
476218 |
| MLB Home Run Exit Velocity: 2015 vs. 2017 |
386537 |
| MLB Stats |
72825 |
| mlbBat10 |
72825 |
| mlbBat10.txt |
72825 |
| MLchallenge |
2864595697 |
| MLearningScrapped |
54039 |
| mljar_ |
62414 |
| mljar2 |
62689 |
| MLUdemy |
684858 |
| MMARTfeb |
24327699 |
| mnet 27 |
4670617 |
| MNIST as .jpg |
18413932 |
| MNIST CSV |
9605983 |
| MNIST data |
15991536 |
| mnist data |
11594722 |
| MNIST data |
17051982 |
| MNIST Data for Digit Recognition |
11598550 |
| mnist dataset |
11493971 |
| MNIST dataset |
15991536 |
| MNIST Dataset |
15948570 |
| MNIST Digit Recognizer |
76775041 |
| MNIST Exdb Lecun Uncompressed1 |
9938128 |
| MNIST Exdb Lecun1 |
9944478 |
| MNIST FASHION |
11594722 |
| MNIST Fashion Test + Train |
41054396 |
| MNIST Fashion Train & Test |
11592478 |
| MNIST Fashion Train and Test |
11592478 |
| mnist for tf |
16168813 |
| MNIST From Tensorflow Tutorial |
11598550 |
| Mnist Model |
17787560 |
| MNIST original |
15948570 |
| MNIST Original |
18841667 |
| MNIST Simple |
16046181 |
| MNIST train and test data |
11598550 |
| Mnist_01_11_18 |
73700 |
| mnist_6k |
11493971 |
| mnist_data |
11592478 |
| mnist_dataset |
15948570 |
| MNIST_examples |
336780 |
| mnist_image |
11913 |
| Mnist_model_sl |
5981672 |
| MNIST_stdm_2017 |
20531112 |
| mnist-data-cnn |
11592478 |
| MNIST-Handwritten Digit Recognition Problem |
15991536 |
| MNIST-Pytorch |
110390848 |
| mnist-submission |
212908 |
| MNIST: 60,000 hand written number images |
127865437 |
| mnist.pkl |
16979733 |
| mnist.pkl.gz |
16132257 |
| mnist.pkl.gz |
16132257 |
| MNIST.Rdata |
23475959 |
| MNIST.Rdata |
20499651 |
| Mnist+contamination(private test) |
76776148 |
| mnistcuboulder |
16168860 |
| mnistd |
1654072 |
| mnistdata |
11598550 |
| MNISTLalthan |
11493971 |
| mnistmodel |
15555128 |
| mnistmydata |
16132257 |
| Mobile location history of 10/2014 |
6149910 |
| Mobile phone activity in a city |
1533030064 |
| mobilenet_1_0_128_tf.h5 |
17225924 |
| mobilenet_1_0_224_tf.h5 |
17225924 |
| mod pnet 10 |
4935242 |
| Model Control |
13824 |
| model v2 24 |
4614238 |
| model v2 32 |
4578793 |
| model v2 aug 16 |
4735378 |
| model v2 test |
4690171 |
| model_checkpoint |
47003451 |
| model_preds |
25794361 |
| model_weights |
14298472 |
| model_weights |
4922056 |
| model_weights_010_F_d17 |
4051567 |
| model-m11 |
16771825 |
| model-m48 |
17259353 |
| model1 |
1399069 |
| model3 |
879356 |
| model3_weights |
14717808 |
| ModelFile |
144724 |
| modelm14 |
16772336 |
| modelm16 |
16772965 |
| modelm20 |
15160346 |
| modelm32 |
17091094 |
| modelm36 |
17099303 |
| Models |
10720278 |
| models |
2814 |
| models |
163492396 |
| ModelsPlus |
4585079 |
| Modified corn dataset |
11979 |
| Modified Data for corporacion favorita grocery |
1762305063 |
| modified pnet 10 |
4935242 |
| modified_train.csv |
10386 |
| Module fym |
326656 |
| Money Supply M2 BRIC economies |
23826 |
| Moneyball |
67157 |
| Monthly Salary of Public Worker in Brazil |
18676853 |
| Monthly Sales |
1019 |
| monthy_milk |
4390 |
| Montreal bike lanes |
31178 |
| Montreal Street Parking |
121638070 |
| Monty hall |
2584 |
| Monty Python Flying Circus |
3944448 |
| Monty Python Flying Circus |
1056462 |
| Monty Python's Flying Circus |
1060891 |
| MOOC Dataset |
8701582 |
| MOOC Dataset |
12488985 |
| MOOC Kaggle dataset |
126153 |
| More data beats better algo |
562357 |
| More Linear Regression |
1586687 |
| More Stacking |
11379395 |
| more_lgbm_2 |
7975028 |
| Mortality by Age IHME |
2079315 |
| Mortality Projection by Worldwide Health Org. |
13019648 |
| Moscow Ring Roads |
3629518 |
| Moses Sample |
10985045 |
| Most Common Wine Scores |
384954 |
| Most Popular Quotes on Goodreads |
1527563 |
| Mother Jones Mass Shootings |
164520 |
| motionData |
19875707 |
| Movebank: Animal Tracking |
22288597 |
| Movehub City Rankings |
100814 |
| Movement coordination in trawling bats |
14194970 |
| Movie Data |
27246093 |
| Movie Dataset |
760318 |
| Movie Dataset |
1494688 |
| Movie Dialog Corpus |
30116727 |
| Movie dialogue corpus part1 |
2834799 |
| Movie dialogue corpus part2 |
2969008 |
| Movie Dialogue Segment Extraction |
4056 |
| Movie Genre from Its Poster |
26789506 |
| movie id title |
49292 |
| Movie Industry |
976097 |
| movie lens |
34849899 |
| Movie lens |
236356 |
| Movie Lens dataset |
5315716 |
| Movie Ratings |
21781 |
| Movie Review |
54848164 |
| Movie Review |
8481022 |
| Movie Reviews |
1843846 |
| Movie Reviews |
4009415 |
| Movie reviews IMDB |
137881715 |
| movie_lens_dataset |
6783244 |
| movie_metadata.csv |
567484 |
| movie_rating_data |
551445949 |
| Movie_ratings |
1041 |
| movie_ratings.json |
1228 |
| movie_review extended |
80292456 |
| movie_reviews_set |
77298342 |
| movie-dialogue-analysis |
17901671 |
| movie-sentiment-analysis |
55178882 |
| Movie&WorldGDP |
1639121 |
| movie5740goodgoodstudy |
45742895 |
| movied |
13788274 |
| moviedata |
6783244 |
| movielens |
198702078 |
| MovieLens |
42500756 |
| Movielens (Small) |
3130294 |
| MovieLens 100K Dataset |
16100896 |
| MovieLens 20M Dataset |
928454686 |
| MovieLens DataSet |
1358614 |
| Movielens DataSet |
6316858 |
| MovieLens Dataset |
1352932 |
| MovieLens DataSet |
2795889 |
| MovieLens_1 |
140248124 |
| movielens2 |
205067583 |
| moviereviews |
13601750 |
| Movies |
1659058 |
| moviesIDB |
1659058 |
| Moviestart |
1659058 |
| Moving Objects from VISTA Survey (MOVIS) |
6015047 |
| mpnet 10 |
4935242 |
| MPQA Subjectivity Lexicon |
662621 |
| mpstest |
61965408 |
| mpstrain |
135342858 |
| Mr Donald Trump Speeches |
13708122 |
| MRI and Alzheimers |
50010 |
| MRI and Alzheimers scan by the OASIS project |
21720 |
| MSD2017 |
22601 |
| MSdata |
324594847 |
| mtcars |
1700 |
| muftimm : Data Testing |
27117975 |
| muftimm : Data Training |
6823239 |
| Mujhe Kiyun Nikala |
1287689 |
| MULTEXT |
122461442 |
| Multilingual word vectors in 78 languages |
176671673 |
| MultipleLinearRegression |
5656 |
| Multispectral Image Classification |
4924096098 |
| Munic docs |
410594 |
| Murder Accountability Stats 2016 |
16975636 |
| MurderRate |
256 |
| MurderRate1 |
226 |
| MurderRate2 |
226 |
| Murders |
1972 |
| Museum of Modern Art Collection |
34825107 |
| Museum Reviews Collected from TripAdvisor |
10640933 |
| Museums, Aquariums, and Zoos |
6817303 |
| Mushroom Classification |
374003 |
| MushroomDatafile |
374003 |
| Mushrooms |
374003 |
| Mushrooms edibility |
374003 |
| Music notes |
89874178 |
| music_churn_data |
31583974 |
| MusicData |
391356 |
| MusicDataset |
391381 |
| Mussel Watch |
163609059 |
| Mutual Funds |
47155186 |
| mvc_graph |
2422395 |
| mvt data |
7317543 |
| My Chess Games |
1920551 |
| My Clash Royale Ladder Battles |
167700 |
| My Complete Genome |
15683529 |
| My data set (Taxi data set) |
312327125 |
| My dataset |
15347 |
| My dataset for fun |
81 |
| my files |
128715521 |
| my first test |
4272986 |
| My Kaggle |
2043644 |
| My Neta Data 2014 |
685332 |
| my plume |
271744 |
| my prediction |
8946112 |
| My Ridge 1 |
8114774 |
| My Settlers of Catan Games |
16689 |
| My Test 2 |
172104359 |
| My Test 3 |
7379191 |
| My trip data |
35693290 |
| My Uber Drives |
86369 |
| MY work |
855780 |
| my_data |
4043708 |
| My_first_project |
869537 |
| My_Kernels |
79575104 |
| my_mnist |
54950048 |
| My_model |
11256837 |
| my_NY_Taxi |
59064305 |
| my_res |
10366748 |
| my_sales_prediction |
697785 |
| my_solution |
3679 |
| my_sub_6 |
251018 |
| my_submit |
4072663 |
| My_Subs |
36545515 |
| My_Temp_dataset |
1058278 |
| my_test |
3150486 |
| my-data |
20103200 |
| my-keras-ff |
243159 |
| my-submission |
6343729 |
| MyBaseline |
5264890 |
| MyCheckins_small |
372624 |
| MyChessGames |
4738039 |
| myCSAV |
344608811 |
| mydata |
14524768 |
| Mydata |
869537 |
| MyData |
568100 |
| mydata |
1029225 |
| mydata stuff |
61194 |
| mydata_lightgbm_ridge_tfidf |
409785821 |
| Mydata1 |
869537 |
| Mydata1 |
3970605 |
| MyData2 |
9426934 |
| Mydata2 |
869537 |
| mydatabase |
15243828 |
| mydataset |
2176225 |
| MYDATASET |
852175 |
| Mydataset |
724 |
| mydataset |
14563117 |
| myDataSet |
89 |
| mydatasets |
7247319 |
| MyFinal |
86439 |
| myfirst |
563388170 |
| MyFirstSubmission |
4098 |
| MyGmailData |
58013 |
| mymydata |
3293153 |
| mynewdata |
1810753 |
| myNNep_2_1221 |
7284858 |
| myNNep_2_bs_1536_lrI_0.013_lrF_0.009_dr_0.25 |
7266601 |
| myNNsubmission |
6333357 |
| mypractice |
20678541 |
| MyRepublicID Twitter Data |
3437834 |
| myRidgeWOzeros |
7945772 |
| myself_modules |
23968 |
| mysubmission |
7341635 |
| mysubmission |
7316365 |
| mySubmission |
7316365 |
| mysubmissions |
6343728 |
| mysubmit |
7305775 |
| mysubmit_1221 |
7975130 |
| mysubmit_dec |
2430798 |
| mysubmit_tanh |
7303010 |
| mysubmit2_1221 |
7975130 |
| mytest |
18162563 |
| mytestdata |
23146 |
| mytitle |
5993 |
| mytrain |
475652919 |
| mytrainingdata |
129135359 |
| mywork1ml4 |
3150486 |
| myxml1 |
3476 |
| n601042018test |
328228 |
| naives |
4044915 |
| NALCS Summer 2017 All Pro Votes |
8675 |
| Name element categories for cereals |
5515 |
| Name pronunciations in videos |
618 |
| name_feature |
1424 |
| Names Corpus |
56572 |
| namescores |
5739450 |
| Narrativity in Scientific Publishing |
11373016 |
| NASA Astronauts, 1959-Present |
81593 |
| NASA Facilities |
103259 |
| nasa-small |
255410 |
| NASCAR Champion History (1949-Present) |
3698 |
| NASDAQ financial fundamentals |
15738123 |
| nashanatasha |
99185 |
| Nashville Housing Data |
11267905 |
| National Accounts |
35765062 |
| National Basketball Association(NBA) Dataset |
89003 |
| National Employment, Hours, and Earnings |
1199787183 |
| National Footprint Accounts data set (1961-2013) |
13229220 |
| National Health and Nutrition Examination Survey |
32554793 |
| National Institute of the Korean Language Corpus |
2445134 |
| National Nutrient Database |
690056 |
| National Park |
3750451 |
| National Pokedéx - Basic |
128150 |
| National Wetlands Inventory |
83929579 |
| National_Adult_Tobacco_Survey |
65128 |
| Nationalities |
2073 |
| Natural Earth - Simplified Countries |
748067 |
| Natural numbers, up to eleven |
24 |
| Natural Rate of Unemployment (Long-Term) |
4091 |
| Natural Stories Corpus |
32649246 |
| Naughty Kid Regression datasets |
3750 |
| Nazi Tweets |
60165007 |
| NBA 16-17 regular season shot log |
19971572 |
| nba draft |
570658 |
| NBA Draft Value |
501389 |
| NBA Enhanced Box Score and Standings Stats |
5801591 |
| NBA Finals Team Stats |
77723 |
| NBA Free Throws |
75737800 |
| NBA player info |
610740 |
| NBA Players Stats - 2014-2015 |
80373 |
| NBA Players stats since 1950 |
5398518 |
| NBA Season Records from Every Year |
192668 |
| NBA shot logs |
16423917 |
| NBA Writer Rank |
71198629 |
| NBA_data with bet365(2009-2011) |
1988439 |
| NBA_train |
86021 |
| NBA14to15 |
776166 |
| nba2014to2015 |
776166 |
| nbachallenge |
8558950 |
| nbacoach |
187728227 |
| NBAplayoff |
7205768 |
| nbasalariesfull.csv |
55624 |
| NBER Macrohistory Database |
32333137 |
| Near Earth Asteroids |
62605 |
| Near-Earth Comets |
25402 |
| Nearest Cities for NYC Taxi Trips |
402990020 |
| needed4pytorch |
127912256 |
| Neighborhoods in New York |
1719637 |
| neo_bagging_1515685296 |
4306781 |
| neo4j_property_graph_model |
59940 |
| Nepal News Homepages |
225 |
| NEPSE index |
846 |
| ner_modified_encoding |
3319651 |
| Net Migration |
7102 |
| Net Neutrality Accountability |
73080 |
| net shopping |
196737128 |
| Netchecker |
3150486 |
| Netflix Prize data |
2131753487 |
| network |
226358 |
| Network Attacks |
29726213 |
| Network Attacks |
18646312 |
| Network Attacks HE |
2910198 |
| neural_net |
19678572 |
| NeuralNet |
7272919 |
| NEW AAPL |
614145 |
| New Car Sales in Norway |
234699 |
| New CPU Data |
9265 |
| new data |
139383 |
| New dataset |
8081 |
| New Human Index |
10302 |
| New Orlean's Slave Sales |
4312299 |
| new subway entances |
241968 |
| New train set |
31424318 |
| New York Citi Bike Trip Duration 2016 |
456080177 |
| New York City - 2013 Campaign Contributions |
5330287 |
| New York City - Buildings Database |
304542181 |
| New York City - Certificates of Occupancy |
15009130 |
| New York City - Citywide Payroll Data |
414298921 |
| New York City - East River Bicycle Crossings |
18446 |
| New York City Bike Share Dataset |
132047989 |
| New York City Census Data |
2574719 |
| New York City Crimes |
265731103 |
| New York City Taxi Trip - Distance Matrix |
4776253 |
| New York City Taxi Trip - Hourly Weather Data |
1305316 |
| New York City Taxi Trips - Important Roads |
457333789 |
| New York City Taxi with OSRM |
2046528343 |
| New York City Transport Statistics |
342168232 |
| New York City WiFi Hotspots |
1031294 |
| New York Hotels |
222663 |
| New York Satellite Image |
24576531 |
| New York Shapefile |
13963510 |
| New York Shapefile 16 |
3529738 |
| New York Stock Exchange |
105844882 |
| New York Taxi Trip enriched by Mathematica |
400048262 |
| New York Traffic Accidents 2016 |
26418375 |
| New Zealand Migration |
4110616 |
| new_data |
7647036 |
| new_data |
7636866 |
| new_importance_list |
6935 |
| new_main_12 |
4180803 |
| new_train |
23300840 |
| New_york_Hourly_crime |
245405 |
| new-model |
11256837 |
| newchurn |
669696 |
| NewData |
1810753 |
| NewData2 |
1810753 |
| NewDataNY |
35693290 |
| newdataset |
24885 |
| NewDataSet |
134964916 |
| newfile |
36789058 |
| newnew |
9201576 |
| News Aggregator Dataset |
102895657 |
| News and Blog Data Crawl |
480781845 |
| News Articles |
5071129 |
| News Headlines Of India |
64919115 |
| News of the Brazilian Newspaper |
503611422 |
| NEWS SUMMARY |
11896415 |
| news_corpora |
29174052 |
| News01 |
23668 |
| News02 |
9015 |
| NewsCWUR |
1212759 |
| Newspaper churn |
1902051 |
| Newspaper churn |
1359983 |
| Newspaper Endorsements of Presidential Candidates |
19444 |
| newsShanghai |
440994 |
| NewTest |
7250673 |
| NewYork_Hourly_Climate |
390943 |
| NFL Arrests |
60450 |
| NFL Arrests 2000-2017 |
177852 |
| NFL Draft Outcomes |
798490 |
| NFL Features |
987128 |
| NFL Football Player Stats |
34399801 |
| NFL Offensive Gains |
712923 |
| NFL Offensive Yards Gained |
738550 |
| NFL play-by-play 2016 |
10509809 |
| NFL Statistics |
97890277 |
| nfl test data |
47562 |
| nfl_offense_cleaned_2017to2007 |
70284 |
| nfl_pbp_2016 |
10509809 |
| NFL_Working |
1046501 |
| NFLArrests |
177852 |
| NHANES Hypertensive population 2008-2016 |
610917 |
| NHL Player Stats 2004 - 2018 |
568858 |
| nifty_data |
99408 |
| NIFTY50 SHARE MARKET DATA SET INDIA |
27821 |
| niftycsv |
169482 |
| NiftyDataForTesting |
170775 |
| NIH Chest X-rays |
45077768961 |
| Nineteenth Century Works On Nepal |
3544083 |
| NIPS 2015 Papers |
29094860 |
| NIPS 2017: Adversarial Learning Development Set |
153340879 |
| NIPS Conference 1987-2015 Word Frequency |
928997 |
| NIPS Papers |
148549575 |
| NIPS17 Adversarial learning - 1st round results |
49523 |
| NIPS17 Adversarial learning - 1st round results |
49523 |
| NIPS17 Adversarial learning - 2nd round results |
91364 |
| NIPS17 Adversarial learning - 3rd round results |
151391 |
| NIPS17 Adversarial learning - Final results |
225272 |
| nishant887y |
159 |
| nist sd19 10 percent |
129926992 |
| nist_sd19_10percent |
129926992 |
| NJ Teacher Salaries (2016) |
28334467 |
| NJ Transit Train Schedule |
3309933 |
| nkm-data1 |
1019399 |
| NLP - Topic Modelling |
120062315 |
| NLP Data |
3295644 |
| NLP Playground |
11280053 |
| NLP Shakira |
72706 |
| nlp_data |
4054789 |
| nlp_data_2 |
91162 |
| nlpprac |
518731 |
| nltk-movieReviewData |
4004848 |
| nltk123 |
345755 |
| NN ensemble |
14412166 |
| nn keras data |
7307405 |
| nn keras data1 |
7307405 |
| NN1andRidge1 |
15239999 |
| NN3 Competition Datasets |
81697 |
| NNDataset |
18217466 |
| NNet work |
4273725 |
| nnetproto |
287859225 |
| nnkeras |
8143627 |
| nnsub11 |
6025830 |
| NNtest |
7266202 |
| No Data Sources |
1 |
| No_survivors |
3258 |
| No.19 President Vote Result |
6207900 |
| No11036 |
1365898 |
| NOAA Pipelined Data |
64 |
| NOAA_2011_Austin_Weather |
236091 |
| Nobel Laureates, 1901-Present |
289963 |
| Nomad GP |
25348 |
| Nomad lgbm 1 |
25323 |
| NOMADv2 |
1009807 |
| NomBank |
6781050 |
| Nominal GDP per capita of Spain (by regions) |
2365 |
| Non-invasive Blood Pressure Estimation |
195189404 |
| non-linear regression |
8762622 |
| Nonbreaking Prefixes |
43190 |
| None_None |
7361689 |
| none202 |
5993 |
| Nonlinear_Data1_Benchmarking |
2189 |
| noNLP2 |
5533694 |
| nonlp3 |
5534520 |
| nonlp4 |
5514335 |
| noNLPnoTL |
5418141 |
| normal_selu |
14636290 |
| North American Slave Narratives |
54688021 |
| North Carolina Schools: Report Cards and Metadata |
2040990 |
| Norwegian Development Funds 2010-2015 |
52758336 |
| noshow |
10739535 |
| noshowapp |
2513958 |
| NOshowrate |
24771860 |
| [Not being Maintained] |
309011219 |
| Not Fake News |
4322 |
| Not MNIST |
255877003 |
| note piano |
27739 |
| nothing |
588903 |
| notMNIST |
116399788 |
| notMNIST dataset |
8458043 |
| nottfi |
744331 |
| Noun Compositionality Judgements |
496121 |
| Nouns Counts in the Works of Edgar Allan Poe |
561704 |
| Nouns in Works of Poe |
139697 |
| novel detection problem |
39652204 |
| novelty authorn |
2871669 |
| Now That's What I Call Music (U.S. releases) |
179483 |
| NP12345 |
698943 |
| NPS Chat |
2578726 |
| npzfile_of 10 model |
838376315 |
| NQ_CL_1718 |
851427 |
| NSE daily data |
31122 |
| NSE India stocks (companies) |
1997075697 |
| NSE India stocks (Indices) |
42234206 |
| NSE Stocks Data |
31361575 |
| NSEI aka Nifty 10 years data |
169482 |
| NSW/CPS |
754918 |
| ntmntm |
8071016 |
| NTSB Accident Reports |
23238603 |
| NTU Physical Design PA3 |
333128 |
| NU Data Mining Homework 1 |
107 |
| nullptr |
78859 |
| Number of Fire Deaths in England 1981 - 2016 |
192 |
| Number of trains on the sections of the network |
2582788 |
| Number Sequence System |
9255288 |
| number_of_atoms |
42559 |
| number_of_atoms |
10625 |
| number_of_atoms_test |
10625 |
| NumberDivisibilty |
6989 |
| numbers-of-shares-clgch |
3411917 |
| Numd80 |
380667276 |
| Numenta Anomaly Benchmark (NAB) |
9593155 |
| Numerai 2017 Tournament 52 |
37279734 |
| numerai_82 |
144188732 |
| numerai-sample |
103815144 |
| Numerai73 |
389549408 |
| Numeral Gestures recorded on iOS |
90326560 |
| NumeroNOMNIST |
12692 |
| Nürburgring Top 100 |
3858 |
| Nursing Home Compare |
333234374 |
| Nutrient |
1849 |
| Nutrition |
9140643 |
| Nutrition Facts for McDonald's Menu |
29988 |
| Nutrition facts for Starbucks Menu |
45283 |
| Nutrition1 |
1537920 |
| NVIDIA Self Driving Car Training Set |
2328695845 |
| NY City Taxi Trip distances |
315349767 |
| NY data |
42627208 |
| NY GeoJson |
1501587 |
| NY Philharmonic Performance History |
257861032 |
| NY State Lotto Winning Numbers |
55503 |
| NY TaXi Train |
483072 |
| NY trip data |
166615212 |
| NY_mental_patient_survey |
4184917 |
| NY_traffic_data |
63687406 |
| NYC 2016 Holidays |
535 |
| NYC Active Dog Licenses |
2783298 |
| NYC Baby Names |
891072 |
| NYC Borough Boundaries |
1219991 |
| NYC boroughs shapes |
2797308 |
| NYC City Hall Library Catalog |
5174122 |
| NYC Dog Names |
146455 |
| .nyc Domain Registrations |
3220894 |
| NYC Filming Permits |
13767651 |
| NYC flight data 2013 |
8424911 |
| NYC Government Building Energy Usage |
10656 |
| NYC hourly car accidents 2013-2016 |
243632397 |
| NYC Hourly Temperature |
220918 |
| NYC Neighborhoods |
1239877 |
| NYC Neighborhoods GPS |
17817 |
| NYC Open Data Metadata |
4722022 |
| NYC Parking Tickets |
8971948107 |
| NYC Property Sales |
13625843 |
| NYC Rat Sightings |
54883237 |
| NYC Rejected Vanity Plates |
2473266 |
| NYC Restaurant Inspections |
146153657 |
| NYC ride time prediction - assist files |
46678255 |
| NYC SUBWAY ENTRANCE |
239604 |
| NYC Subway Entrance Data |
239604 |
| nyc subway entrances |
239604 |
| NYC Subway Entrances |
239604 |
| nyc subway entrances |
239604 |
| nyc subway entrances |
239604 |
| nyc subway entrances |
239604 |
| NYC Subway Entrances_Malinee |
239604 |
| NYC Subway Entrances_Parichart |
239604 |
| NYC Taxi Data |
17455616 |
| nyc taxi data jan half |
504328679 |
| NYC Taxi dataset |
5041 |
| NYC taxi trip (1) |
385622215 |
| NYC taxi trip (2) |
385616657 |
| NYC taxi trip durations |
495707032 |
| NYC taxi yellow tripdata 201701 |
11586 |
| NYC taxi zones |
12322 |
| NYC Taxis combined with DIMACS |
241645369 |
| NYC Transit Data |
1312881840 |
| NYC Uber Pickups with Weather and Holidays |
2075599 |
| NYC Weather |
1913671 |
| NYC Weather Parameters |
3226 |
| nyc_taxi_trip |
2088286 |
| nyc-rolling-sales.csv |
13625843 |
| NYCdata |
246001998 |
| nycdata2 |
90249030 |
| nycdatawork |
90336566 |
| NYCHA Staten Island Asbestos Siebel Data |
90192 |
| nyctaxieda |
498999442 |
| NYCUpdated |
90249030 |
| NYData |
35693290 |
| NYPD Motor Vehicle Collisions |
239819199 |
| NYSE-1965 |
975124 |
| nytimes articles |
20653 |
| O'Reilly Strata London 2017 Talks and Ratings |
52892 |
| Obama Visitor Logs |
1218589491 |
| Obama White House |
199928173 |
| Obama White House Budgets |
7566029 |
| Obesity Stats |
20397291 |
| objectrecgo |
34339535 |
| objectrecog |
40063927 |
| occgender |
31336 |
| occupation |
22667 |
| Ocean Ship Logbooks (1750-1850) |
19755905 |
| OD_test_1 |
1532563 |
| ODI Cricket Matches |
1350073 |
| ODI data from 1971 to 2011 |
573543 |
| OECD Better Life Index 2017 |
5023 |
| OECD macroeconomic data |
38819228 |
| OECD Productivity Data |
27609621 |
| Ofcom UK Broadband Speed 2016 Open dataset |
32855594 |
| officaldata |
287859225 |
| Oil and Gas |
1036490 |
| Oil Barrels |
58880 |
| Oil Pipeline Accidents, 2010-Present |
908056 |
| Oil price and share price of a few companies |
3208415 |
| Oil sales analysis |
629117 |
| ojbklgbm |
8070518 |
| Oklahoma Earthquakes and Saltwater Injection Wells |
4187918 |
| Old Newspapers |
2196786581 |
| olivetti |
1903745 |
| Olivetti_Faces |
1903745 |
| Olympic Games |
436130 |
| Olympic Sports and Medals, 1896-2014 |
3047770 |
| Olympic Track & Field Results |
797283 |
| Olympics_1896_2012 |
404121 |
| one million movie |
70066042 |
| One week of Betfair data: 23 sports |
337478465 |
| One week of Betfair data: horses |
150888516 |
| One Week of Global Feeds - News Dataset |
294039203 |
| Oneside |
1015880 |
| oneside&smote |
4166950 |
| OnesideAlone |
1015880 |
| OnesideSelec |
1015854 |
| OneVsRest Classifier versus Multi-Output |
248022779 |
| Onifi risk loan risk prediction |
28953759 |
| Online Auctions Dataset |
1049357 |
| Online Chinese Chess (Xiangqi) |
15920715 |
| Online Courses from Harvard and MIT |
66858 |
| Online Generated MNIST Dataset |
6363682 |
| Online Job Postings |
96789716 |
| Online News Popular |
5229121 |
| Online News Popularity |
5220015 |
| Online Product Sales |
1627125 |
| Online Recipe Data |
333796 |
| Online Retail |
23715344 |
| Online Retail Data Set |
45580638 |
| online sales |
1519635 |
| OnlineNewsPopularity |
24311769 |
| onlinenws |
24311769 |
| onlineRetail |
7548662 |
| onlineretail |
3291222 |
| Only Sathyam |
20248 |
| onstatus |
661071 |
| oooooooooooooooo |
6531 |
| op-123 |
1024979 |
| Open Beauty Facts |
12317416 |
| Open Data 500 Companies |
489447 |
| Open Data 500 Companies |
82749 |
| Open Exoplanet Catalogue |
466109 |
| Open Flood Risk by Postcode |
88682006 |
| Open Food Facts |
1010256825 |
| Open Multilingual WordNet |
48320874 |
| Open Postcode Elevation |
19796379 |
| Open Postcode Geo |
281633058 |
| Open Pubs |
7106722 |
| Open Sprayer images |
155083085 |
| OpenAddresses - Asia and Oceania |
3997876240 |
| OpenAddresses - Europe |
7911632856 |
| OpenAddresses - North America (excluding U.S.) |
4887052811 |
| OpenAddresses - South America |
6611249144 |
| OpenAddresses - U.S. Midwest |
2174018364 |
| OpenAddresses - U.S. Northeast |
2045068168 |
| OpenAddresses - U.S. South |
3404926019 |
| OpenAddresses - U.S. West |
2448531541 |
| openai unsupervised sentiment |
320140754 |
| OpenCorpora: Russian |
282996427 |
| Opendata AIG Brazil |
5584083 |
| OpenData Impact Map |
782515 |
| OpenStreetMap Data - North Bangalore, India |
205229390 |
| Opinion Lexicon |
67865 |
| optimized |
4080828 |
| order_products__train |
24680147 |
| Orders data |
1428213 |
| oregon education |
233763 |
| Oreo Flavors Taste-Test Ratings |
1083 |
| orig_dat |
9199904 |
| Origin |
78025130 |
| Original mdf |
292334591 |
| Original Submission Sample |
240909 |
| original_data |
198373006 |
| original_edx_data |
10006416 |
| ortools.zip |
36750210 |
| OSHA Inspections of Dental Practices (1972-2017) |
737479 |
| OSMI Mental Health in Tech Survey 2016 |
83459533 |
| OSRM Data |
580270529 |
| oss file sizes |
479681865 |
| Osu! Standard Rankings |
9810 |
| Other parameters |
18196682 |
| Other try |
14401031 |
| otherkernels |
2102412 |
| others_MA8 |
12756180 |
| oudav4 |
1091879 |
| ouptutj |
17321574 |
| out.csv |
4860484 |
| Outcomes for prediction |
396 |
| Outlier |
2871649 |
| outliers |
85713 |
| outmodelch |
124248654 |
| output |
2041915 |
| output |
7374647 |
| output |
5012984 |
| output |
12725557 |
| output |
3256 |
| Output for 20 kernels porto seguro |
384308032 |
| output of the kernel |
37412275 |
| output sample |
43499 |
| output4 |
101080830 |
| outputn |
471686 |
| Over 13,000 Steam Games |
539878 |
| Overlapping chromosomes |
24203163 |
| Overwatch |
1927 |
| Overwatch Game Records |
710065 |
| Own dataset |
561 |
| Oyo rooms Delhi |
34180 |
| p2hdata |
116457882 |
| P300-Dataset |
323290993 |
| padestrians_images |
20323705 |
| Paintings |
6021 |
| Pak Youth Unemployment vs Terrorist Attacks |
34071 |
| Pakistan Drone Attacks |
161953 |
| Pakistan Drone Attacks |
486315 |
| Pakistan Education Performance Dataset |
253565 |
| Pakistan Intellectual Capital |
332809 |
| Pakistan Intellectual Capital |
332805 |
| Pakistan Suicide Bombing Attacks |
231347 |
| Pakistan Tehsil District Census |
54663 |
| PakistanDroneAttack |
161965 |
| palm_dataset |
17227039 |
| pandas_for_everyone |
81932 |
| pandas_tutorial |
115124 |
| pandas-tutorial-datasets |
1165078 |
| PanLex Swadesh |
2868894 |
| Pantheon Project: Historical Popularity Index |
1530565 |
| Papa New Guinea |
181108 |
| Paper_Scissor |
8226325 |
| Paradigm |
361186 |
| Paradise Papers |
7316696 |
| Paradise-Panama-Papers |
141019215 |
| parallel English-Spanish |
141185 |
| Parallel scheduling dataset for Cloud environment |
24559 |
| Parallel scheduling workload |
25421 |
| ParamS5 |
5458701 |
| paramsearch |
3649 |
| Paranormal Romance Novel Titles |
93647 |
| Parking Violations, December 2015 |
26605152 |
| Parkinson Disease Spiral Drawings |
16482050 |
| Parkinson's Disease Observations |
889296 |
| Parkinson's Vision-Based Pose Estimation Dataset |
138624226 |
| Parole Data |
18533 |
| Parole hearings in New York State |
9881645 |
| Part 1 - Data Preprocessing |
3880 |
| PartialDatasets |
820867 |
| Participation in cultural activities |
622491 |
| Party strength in each US state |
125762 |
| past_data |
68252050 |
| past_data1 |
11989231 |
| past_data2 |
11989231 |
| Patent Assignment Daily |
286556804 |
| Patent Grant Full Text |
596450131 |
| Patent Litigations |
1684999366 |
| Path of exile game statistic |
9171959 |
| patient |
512 |
| Patient Characteristics Survey (PCS): 2015 |
4184917 |
| patientmet |
678 |
| patients |
1190 |
| patientsmeta |
678 |
| Pauvrete_richesse_france_2014 |
19638 |
| PAytm edit |
1319411 |
| PC_Games |
487509 |
| PCA analysis with Decision tree |
300584782 |
| pcnn fhv lee 32 |
4712245 |
| pcnn fhv lee16 |
4728245 |
| pcnn fhv lee24 |
4733100 |
| pcnn fhv lee32 |
4712245 |
| pCNN FHV Lee8 |
4671863 |
| PDD Graph |
2642 |
| pe_pkl |
16803263 |
| PE08 Parseval |
296619 |
| Pedestrian Dataset |
51232595 |
| Pedestrian Dataset |
24378183 |
| pedestrian no pedestrian |
16633885 |
| Pediacities NYC Neighborhoods |
498176 |
| Penn Tree Bank |
1746323 |
| Penn World Table |
7435033 |
| Pennsylvania PSSA and Keystone Results |
11047144 |
| Pennsylvania Safe Schools Report |
7180485 |
| People and Character Wikipedia Page Content |
232516861 |
| people walking |
8025457 |
| People Walking with No Occlusion |
66 |
| People Wikipedia Data |
30838672 |
| People without internet |
138506 |
| Per Capita Personal Income by Metro Area 2007 2015 |
5856 |
| PerHour |
371073 |
| Periodic Table of Elements Mapped to Stocks |
8188565 |
| Periodic Table of the Elements |
717980 |
| periodicTable.cvs |
12360 |
| perishable products Colombian markets |
2985281 |
| Perluniprops |
136038 |
| perMinuteWeatherReport |
27425604 |
| person |
28181208 |
| Person of the Year, 1927-Present |
11686 |
| personal |
17172700 |
| Personal |
48948 |
| PersonalTimestamp |
354 |
| Pesticide Data Program (2013) |
111451786 |
| Pesticide Data Program (2014) |
121908874 |
| Pesticide Data Program (2015) |
128802442 |
| Pesticide Use in Agriculture |
24834854 |
| PGA Tour 2016/2017 Leaderboards |
964012 |
| PGJ_DR about my private work |
623209 |
| Pharmaceutical Tablets Dataset |
93218742 |
| Philadelphia Crime Data |
310178968 |
| Philadelphia Real Estate |
220983 |
| Phishing dataset from Sep 01-24 |
360527 |
| photo5 |
739 |
| photo5.jpg |
144753 |
| photonew |
6707 |
| pic_asdf |
234234 |
| pickefile |
235800 |
| pickled mnist neural net |
191267 |
| pickletest |
6770 |
| Picture1 |
77794 |
| Pictures from internet - memes |
14216914 |
| PID666 |
23279 |
| PIL Corpus |
4170899 |
| Pill Count detection |
40228319 |
| pima indian |
23279 |
| Pima Indian Diabetes Data |
30789 |
| Pima Indian Diabetes Problem |
24045 |
| Pima Indians Diabetes Data Set |
23279 |
| Pima Indians Diabetes Database |
23873 |
| Pima Indians onset of diabetes dataset. |
23279 |
| Pima_Diabetes_dataset |
26255 |
| pima-indian |
23279 |
| pima-indian-diabetes |
1003 |
| pima-indians-diabetes |
23279 |
| pima-indians-diabetes.data |
23279 |
| PimaDiabetesMean |
30394 |
| PimaDiabetesMedian |
25280 |
| PimaDiabetesZeroesRemoved |
12719 |
| Pisa Scores |
114208 |
| Pisa scores Males students Math data 2015 |
1570 |
| Pisymbol |
14601 |
| Pitcfork reviews CSV |
33370056 |
| pizza data v2 |
318851 |
| Pizza In Brooklyn |
3234 |
| Pizza restaurants and the pizza they sell |
1113658 |
| PizzaDataV2 |
318851 |
| PizzaZona14V2 |
318851 |
| pklData |
8460437 |
| pkugoodspeed |
4046431 |
| PL 196x Corpus |
58299303 |
| places |
518562 |
| PlanesNet - Planes in Satellite Imagery |
59705833 |
| player.csv |
15422590 |
| Players2016 |
170890 |
| PLAYERUNKNOWN'S BATTLEGROUNDS Player Statistics |
65064745 |
| Playing with text classified ads |
55453734 |
| playstore |
5114702 |
| playstore1 |
5114694 |
| please |
8042801 |
| pleasework |
4417183 |
| PM2.5 Data of Five Chinese Cities |
15615995 |
| pnet 40 |
18477645 |
| poc- restaurent reviews |
61332 |
| pocdddd |
5993 |
| Poems from poetryfoundation.org |
605913 |
| Poetry |
6183930 |
| Poetry Analysis Data |
605913 |
| Poetry Analysis with Machine Learning |
605913 |
| Points for Perceptron Class |
1881 |
| Pokachi |
7992 |
| Pokedex |
130239 |
| Pokemon |
79392 |
| pokemon |
44028 |
| Pokemon |
7992 |
| pokemon |
44028 |
| Pokemon |
698383 |
| Pokemon |
698383 |
| Pokemon (Gen 7) |
122016 |
| Pokemon battle |
698383 |
| Pokemon Dataset |
7992 |
| Pokémon for Data Mining and Machine Learning |
818798 |
| Pokemon Go Gen II (251) |
31606 |
| Pokemon Images |
29701331 |
| Pokemon Images Dataset |
41408300 |
| Pokemon Moon Wonder Trade Informatics |
36505 |
| Pokemon Sun and Moon (Gen 7) Stats |
1692546 |
| Pokemon Trainers Dataset |
1884160 |
| Pokemon Visual Stats using SEABORN! |
7992 |
| Pokemon Weakness - Generation 1 |
7832 |
| Pokemon with stats |
44028 |
| Pokemon_Beginner |
698383 |
| Pokemon- Weedle's Cave |
698383 |
| pokemon.csv |
40454 |
| Pokemon1 |
79392 |
| Pokemon12 |
44028 |
| PokemonGO |
17000 |
| Poker Hand Dataset |
6560698 |
| Poker Hold'Em Games |
82609982 |
| Poker sample data |
214 |
| Poker Winings |
214 |
| poker1 |
853 |
| Pokerset |
214 |
| PokWin |
853 |
| Police Killing |
293056 |
| Police Killings |
294629 |
| Police Officer Deaths in the U.S. |
4597386 |
| Polish OLX items |
25575073 |
| PolishDS |
8963390 |
| Political Social Media Posts |
4309577 |
| Polling |
4178 |
| PollutionLevel |
517117 |
| PolynomialRegression |
6172 |
| POM DB1 |
49601840 |
| popados |
2944 |
| popopopop |
99185 |
| Popular websites across the globe |
2662038 |
| Population |
365 |
| Population |
1126 |
| Population |
123495 |
| population by state |
630 |
| Population Median Age by Country since 1950 |
329006 |
| Population vs profit made by restuarant |
1456 |
| Population_ibge_al |
4261 |
| Porn Data |
21068290 |
| Port Segure Mix |
21296551 |
| portal |
35951232 |
| Porter Test |
680060 |
| portfolio_hackerearth |
845939 |
| Portland Oregon Crime Data |
136956832 |
| Porto LCFR |
22539419 |
| Porto Seguro |
24594563 |
| Porto Seguro |
107381901 |
| Porto Seguro |
115852544 |
| Porto Seguro public kernel results |
8499 |
| Porto Seguro stacking |
21676551 |
| Porto Seguro train/test 5 |
284015602 |
| porto seguro_train |
115852544 |
| porto seguro's safe driver noisy features |
9576 |
| Porto Seguro s Safe Driver Prediction |
300584782 |
| Porto Seguro s Safe Driver Prediction |
115852544 |
| Porto Seguro s Safe Driver Prediction data |
78025130 |
| Porto Seguro's Safe Driver Prediction Dataset |
300584782 |
| Porto Seguro s Safe Driver Prediction files |
287859225 |
| Porto Seguro s Safe Driver Prediction test data |
172006681 |
| Porto Seguro s Safe Driver Prediction train data |
115852544 |
| Porto Seguro s Safe Driver Prediction_0.26 |
10297156 |
| Porto Seguro s stack results |
20424567 |
| Porto train |
478633319 |
| porto_mdlp |
63833549 |
| Porto_MEDIAN |
118120409 |
| PORTO_MEDIAN_GO |
86419583 |
| porto_seguro |
0 |
| Porto_seguro_features_score |
12059 |
| Porto-Data |
287859225 |
| porto-knn |
24594563 |
| PortoAutoML |
29962600 |
| portomix |
19160607 |
| portos |
31424312 |
| portose |
78025130 |
| PortoSeguro |
2841792 |
| portoseguro2 |
10136619 |
| portoseguro3 |
12978411 |
| PortoT |
80247495 |
| Possible Asteroid Impacts with Earth |
1817658 |
| Poverty and Equity Database |
1372076 |
| Powerball Numbers |
61605 |
| PP Attachment Corpus |
3113650 |
| ppi_data_15000 |
2841308 |
| ppi_experiment |
189515613 |
| pppppp |
173172 |
| Practice |
18 |
| Practice 1 |
5678 |
| practice data |
18070 |
| Practice Data Set for Air Quality |
3044 |
| Practice Dataset |
5436304 |
| Practice HE |
58616457 |
| Practice Titanic |
93081 |
| prb_kl |
3111160 |
| Pre-Processed Images |
302494843 |
| Pre-processed testing set |
283639 |
| Pre-processed train set |
1873904 |
| Pre-processed Twitter tweets |
192242 |
| Pre-trained Word Vectors for Spanish |
2868903315 |
| Precip |
397793 |
| Precipitation in Syracuse, NY |
13436 |
| Precipitation_SE_Michigan |
263785 |
| pred072.csv |
4046396 |
| predict |
12535647 |
| Predict Happiness |
34025987 |
| Predict Is_Response_Happiness |
22785500 |
| Predict Molecular Properties |
1202077116 |
| Predict Mortality/Death Rate. |
320054078 |
| Predict Network Attack |
2906814 |
| Predict Network Attacks |
29726213 |
| Predict Network Attacks |
29726213 |
| Predict NHL Player Salaries |
449021 |
| Predict Outcome of Pregnancy |
3478193499 |
| Predict temperature |
38488853 |
| Predict the Happiness |
63004455 |
| Predict UK retailer content marketing |
6137526 |
| Predict_Disease_Xray |
7464640863 |
| Predict'em All |
799953514 |
| Predicted |
603071 |
| Predicted Target label of Titanic test data |
3258 |
| predicted_values |
39698 |
| Predicting a Biological Response |
4723978 |
| predicting Income group |
4835078 |
| Predicting Movie Revenue |
101633 |
| Predicting prices |
134964916 |
| Predicting Who Pays Back Loans |
341962107 |
| Prediction |
15368248 |
| prediction |
1635346 |
| prediction |
1635358 |
| prediction |
603071 |
| prediction |
1397246 |
| prediction |
2839 |
| Prediction |
4098 |
| prediction best 1 round mecari |
47850524 |
| Prediction Challenge 1 |
224651 |
| Prediction Challenge 2 |
351700 |
| Prediction House |
12435 |
| prediction1 |
8187498 |
| prediction2 |
7677579 |
| predictionhaitam |
1635346 |
| Predictions |
46317 |
| Predictions mark1 |
4070177 |
| Predictive analysis |
52836 |
| Predictive happiness |
62524288 |
| Predictive Maintenance |
57700 |
| predicts |
12535647 |
| PredOutput |
26129 |
| PredOutputCSV |
26129 |
| predY.csv |
20820001 |
| Premier League Data |
334360 |
| Premier League 00/01 |
14808 |
| Premier League 2001-14 |
190376 |
| premtewari |
64141 |
| prepared_data |
215775688 |
| Prepossessed Data |
1025108 |
| preprocess |
6 |
| preprocess2 |
6 |
| Preprocessed Data |
1058736 |
| Preprocessed Dataset NYSE stocks |
3225310 |
| preprocessed_description |
103902003 |
| Preprocessing-1 of Titanic Dataset |
38858 |
| Preprocessing-2 of Titanic Dataset |
212961 |
| Prescription-based prediction |
163988932 |
| President by County |
257373 |
| Presidential Approval Ratings |
1002437 |
| Presidential Cabinet Nominations |
23161 |
| Presidential Inaugural Addresses |
806273 |
| Presidential Pardons, 1900-2017 |
8331 |
| Press Release by Govt. of India |
19472188 |
| Pretrain file |
1561949416 |
| Pretrained |
129647814 |
| pretrained cnn model |
18867321 |
| Pretrained PyTorch models |
383897612 |
| pretrained_cnn |
129406905 |
| pretrained2 |
160776230 |
| pretrained3 |
160776230 |
| Pretrained6 |
160776230 |
| Price of petroleum products in India |
18530 |
| price_001 |
2020927 |
| price_AV |
1270747 |
| Price_suggestion |
189998479 |
| price-2017-07 |
1244 |
| price-predict-submit |
7297703 |
| price1 |
678432 |
| Pricing Model |
3875898 |
| Primary breast cancer vs Normal breast tissue |
2325053 |
| prime and composite |
451 |
| primeNumbers |
185 |
| Primetime Emmy Awards, 1949-2017 |
1728086 |
| Prioritization Matrix |
42211 |
| private data |
501020188 |
| Pro and College Sports Lines |
276038 |
| Problem Report Corpus |
3467763 |
| process |
250006728 |
| Processed Training Dataset |
23265 |
| ProcessedDatafiles |
220410569 |
| Producer Price Index |
142373498 |
| Product Reviews |
835097 |
| Professional Hockey Database |
5663000 |
| Projec |
19580 |
| Project |
641529 |
| project |
12102571 |
| Project 1 - Abhinandan |
798235 |
| Project 2 |
4228768 |
| Project 3 data- Bellwether |
35627627 |
| Project Data |
89823 |
| Project Euler - Membership by Country - 20170827 |
17259 |
| Project Gutenberg's Top 20 Books |
14094506 |
| Project Tycho: Contagious Diseases |
20688126 |
| project1 |
869537 |
| Project1 |
646643 |
| Projecting Community Risk near Industrial Sites |
39141 |
| projectprediction |
1383040 |
| Promoter Site Prediction |
324226 |
| Promoter Site Prediction FINAL |
324226 |
| promoterprediction |
324226 |
| Propbank |
5330559 |
| Proper-names Categories |
80194 |
| properati dataset tp1 1 |
287348203 |
| properties |
16977714 |
| Properties for sale in Argentina |
339338139 |
| Properties on StayZilla |
2314881 |
| properties smiles |
211762 |
| properties_2016 |
52652121 |
| properties2016 |
52652121 |
| prophet |
410674 |
| prophet-base |
410682 |
| Propiedades-Properati |
462984429 |
| propiedades-tpdatos |
501659329 |
| Pros and Cons |
2921218 |
| Prospects For Realtors from Social Media |
851375 |
| Prosper Loan Data |
86471101 |
| prostate.csv |
9254 |
| Protein Contact Prediction |
755686397 |
| Protein Sequence Dataset for Multiple organisms |
20405540 |
| ProteinSubcellularLocalization |
1009281 |
| proto train |
287859225 |
| Protocol Gifts |
868648 |
| prototype |
52792929 |
| Provincias y Sectores Rep. Dom. |
294717 |
| Proyeksi Jumlah Penduduk Indonesia (Jenis Kelamin) |
15697 |
| prueba |
7713906 |
| Prueba1 |
12237502 |
| Prueba13_12 |
7713886 |
| prueba2 |
7713886 |
| pruebas |
11873255 |
| PS1 graph one |
38735 |
| ps1-he |
845939 |
| ps2_xyz |
33774512 |
| PSL data |
2391140 |
| pstrain |
108304724 |
| psychology |
6163 |
| Psychology Field Work |
43798 |
| Psychometric Data |
3597312 |
| PTB Dataset |
34880190 |
| PTB-preprocessed |
6433681 |
| PTE_HE |
22785500 |
| PUBG Match Deaths and Statistics |
4111549533 |
| Public Kernel |
22493655 |
| Public Kernel Results from Favorita Forecasting |
48778780 |
| Public Transport in Zurich |
497611277 |
| Publication and usage reports, 1998-2017-10 (BR) |
59905114 |
| Publicly Supported Symbols of the Confederacy |
174986 |
| PublicSubMissionFiles |
39879475 |
| publisher_contrast |
11713 |
| puller |
770 |
| pullerData |
768 |
| Pulse of the Nation |
489303 |
| pumpkin pic |
56267 |
| Pune Property Prices |
37131 |
| PuneAI |
66767 |
| Punkt Sentence Tokenizer Models |
36731110 |
| Punkt Sentence Tokenizer Models |
36731110 |
| Pupils sample data |
1034 |
| purchaseandredemption |
158536594 |
| Purdue RH |
8495808 |
| py-random |
11549 |
| Python Code Example |
1002 |
| Python Folium Country Boundaries |
252515 |
| python implementation of the apriorialgorithm |
4917 |
| Python Questions from Stack Overflow |
1739368138 |
| Python Utility Code for Deep Learning Exercises |
2582 |
| Python_data |
61194 |
| Python_scripts |
1328 |
| Python-scripts |
1314 |
| python2_lesson06_keys |
3494 |
| pythonbasics |
67915 |
| Pythondatee |
809411 |
| pythonfile |
293 |
| PyTorch SENet 1520 |
185343 |
| Q & A Discussed in Parliament of India |
166519994 |
| q111qq |
45742895 |
| qaqaqaqaqaqaqaqaqaqaqaqaqaqaqaqa |
98871 |
| QB NFL Draft Combine Results From 2000-2015 |
47293 |
| QBI Image Enhancement |
6263184 |
| QBI Image Segmentation |
43421356 |
| qiixang109merge29 |
6364531 |
| qixiang109-cnnret |
6344773 |
| qixiang109-ensemble |
6356379 |
| qixiang109-ensemble2 |
6361594 |
| qixiang109-merge3 |
6358880 |
| qixiang109-mergelinear |
6367202 |
| qixiang109-round |
2298925 |
| qixiang109ensemble3 |
6363224 |
| qixiang109merge15 |
6364712 |
| qixiang109merge17 |
6363795 |
| qixiang109merge18 |
6363374 |
| qixiang109merge19 |
6362908 |
| qixiang109merge20 |
6363346 |
| qixiang109merge21 |
6363443 |
| qixiang109merge22 |
6363123 |
| qixiang109merge23 |
6369065 |
| qixiang109merge24 |
6369389 |
| qixiang109merge25 |
6367847 |
| qixiang109merge26 |
6364275 |
| qixiang109merge27 |
6363356 |
| qixiang109merge28 |
6363439 |
| qixiang109merge29 |
0 |
| qixiang109merge30 |
0 |
| qixiang109merge7 |
6362099 |
| qq<>"= |
5993 |
| quality |
5632 |
| Quality |
5632 |
| quality & deformation |
14881 |
| Quality Dataset |
5632 |
| Quality Prediction in a Mining Process |
53385997 |
| Quantifying WIKIPEDIA Usage in Education |
99358 |
| Quarterback Stats from 1996 - 2016 |
1509405 |
| queries |
1574010 |
| querydata |
4143282 |
| queryTimes |
6842341 |
| Question 1:_Brainwave 2018 |
845939 |
| Question Classification Corpus |
361090 |
| Question Pairs Dataset |
60747409 |
| Question-Answer Dataset |
4835375 |
| Question-Answer Jokes |
3508579 |
| Question-paragraph dataset in Russian language |
331848632 |
| Questions from Cross Validated Stack Exchange |
474506071 |
| Quite Intresting One |
967685 |
| quoniammm |
4044917 |
| Quora Pairs |
136857580 |
| Quora Pairs 2 |
145771266 |
| quora_feature |
63448896 |
| Quotables |
5275619 |
| Quotes Collection |
1949667 |
| Quotes with Authors |
1949667 |
| Quran_Dataset |
2162758 |
| quran-english |
926614 |
| quraneng |
882717 |
| qweqwe |
28 |
| qwert12345 |
6058154 |
| R Course |
281768 |
| R multivariate data visualization |
203800 |
| r programming code |
23152 |
| R Questions from Stack Overflow |
540995713 |
| R vs. Python: The Kitchen Gadget Test |
2607 |
| r85-data |
103815144 |
| r86-numerai-dataset |
103814883 |
| r87-numerai-dataset |
103832599 |
| r88-numerai |
103798569 |
| rabbits |
17991763 |
| Racing Kings (chess variant) |
85313896 |
| racing_to_0.42 |
15243828 |
| Radjeshed |
28671651 |
| Rainfall data over Sokoto |
10532 |
| Rainfall in India |
597390 |
| rajeevdata |
38559450 |
| RamanujamDataset |
89823 |
| Ramen Ratings |
158316 |
| ramk0.287 |
35951144 |
| ran_avg |
10157991 |
| Random Acts of Pizza |
15588894 |
| random acts of pizza |
15607569 |
| Random Aircraft Information |
8861 |
| Random Data for Practice |
19237769 |
| Random Forest |
246 |
| Random Forest |
21838 |
| Random Forest Code |
3258 |
| Random Sample of NIH Chest X-ray Dataset |
2253119529 |
| Random Shopping cart |
579026 |
| Random Shopping cart |
333187 |
| Random test |
5888 |
| RandomTimeStamp |
11537786 |
| rank_avg |
23668515 |
| rank-0.287 |
35951232 |
| ranks_0.287 |
35951144 |
| rapdata1 |
354 |
| rapdata2 |
354 |
| Rare diseases - Sentiment analysis |
1631308 |
| Rare Diseases on Facebook Groups |
2372096 |
| Raspberry Turk Project |
18162570 |
| rating ranked books |
544595 |
| Ratings |
134932408 |
| Raw Bitcoin Trading Price 2011 to 2017 |
137092 |
| Raw data |
15991536 |
| raw data of Mercari Price Suggestion Challenge |
196737128 |
| Raw Dataset of NYSE stock prices |
1549182 |
| Raw Twitter Timelines w/ No Retweets |
36970002 |
| Raw Weather Dataset |
320926 |
| raw_data |
134964916 |
| rawCountryClub |
3225705 |
| rDany Chat |
2887823 |
| Rdatasets |
247286 |
| Reading tesxt from an image |
1798894 |
| Real Data |
273 |
| Real Estate |
3200000 |
| Real Location Retrieval from Text |
357498 |
| Real Time Bidding |
477575440 |
| realData |
15019 |
| @realDonaldTrump 2009-05-04 through 2017-11-01 |
5880181 |
| realistic test |
153499 |
| realistic train |
707862 |
| Realtime GTFS |
2569011200 |
| reastaurant |
676241 |
| Recipe Ingredients Dataset |
15259153 |
| Reciprocity Failure |
92 |
| Recommendation System for Angers Smart City |
775072 |
| Recommender Click Logs- Sowiport |
2472650136 |
| recruit |
403202 |
| Recruit Ensemble |
524614 |
| Recruit Restaurant Visitor Forecasting |
3530 |
| Recruit Restaurant Visitor Forecasting |
28953759 |
| Recruit Restaurant Visitor Forecasting Data |
29456859 |
| Recruiting Competition Practice |
98244805 |
| recruitxgb |
706078 |
| RecSys Data |
24243962 |
| recsys-sub |
2801564 |
| recsys-subset |
1390347 |
| Red & White wine Dataset |
384016 |
| Red wine data table |
84199 |
| Red Wine Dataset |
99368 |
| Red Wine Quality |
100951 |
| Red Wine Quality wihout first line |
100805 |
| Redata |
164688 |
| Reddit Comments on the Presidential Inauguration |
8520710 |
| Reddit r/Place History |
517752738 |
| Reeses |
6435187 |
| Reference-World University |
1496029 |
| Refugees in the United States, 2006-2015 |
15250 |
| Region of Interest (ROI) detection using ML |
115682 |
| Registro (2017) de servidores públicos estaduais |
109431398 |
| Regression |
105412 |
| Regression with Hospital visits |
32004 |
| reindex_items |
275391 |
| Religious and philosophical texts |
8222277 |
| Religious Terrorist Attacks |
3599093 |
| Religious Texts Used By ISIS |
1340094 |
| Renewable Energy Generated in the UK |
14433 |
| Reordered_INSU |
540124774 |
| rep2.dim+nn |
10109 |
| requirements |
781 |
| res.csv |
4937369 |
| res2.csv |
4937369 |
| Residential Energy Consumption Survey |
27520710 |
| ResNet-101 |
166296046 |
| ResNet-152 |
224845993 |
| ResNet-18 |
43448030 |
| ResNet-18 pretrained model (PyTorch) |
43448048 |
| ResNet-34 |
80994963 |
| ResNet-50 |
95165345 |
| ResNet-50 |
182733298 |
| resources |
713707 |
| responses |
458740 |
| rest_weatherdata |
179180 |
| restart |
3928163 |
| restaurant and consumer data |
226294 |
| Restaurant Data with Consumer Ratings |
207417 |
| restaurant_combine_cleaned |
18933708 |
| Restaurant-reviews |
61332 |
| Restaurants on TripAdvisor |
6912444 |
| Restaurants on Yellowpages.com |
1620170 |
| Restaurants That Sell Tacos and Burritos |
50807999 |
| result |
1754686 |
| result |
7582014 |
| result |
7582014 |
| result |
2872646 |
| result |
3677 |
| result |
7235480 |
| result |
17983352 |
| Result0 |
372753 |
| Result1 |
372753 |
| Result2 |
372753 |
| resultcsv |
3592356 |
| Results |
86343894 |
| Results from Running Events in Porto, Portugal |
38846090 |
| Results from various public kernals |
162004364 |
| results_model_easy_1 |
2605807 |
| results_model_easy_tests_1 |
4227514 |
| results_model_hard_1_dos |
3245528 |
| results_model_hard_2_dos |
3625556 |
| results_model_hard_3_dos |
3623586 |
| results.csv |
2872646 |
| Retail Data Analytics |
13865170 |
| retail sales |
5342 |
| Retail Sales Forecasting |
22230 |
| Retailrocket recommender system dataset |
987498023 |
| Retirement savings account (RSA) membership |
1077 |
| Retrosheet events 1970 - 2015 |
1006577683 |
| ReturnPredAnnuity |
845939 |
| returnrate |
989899 |
| Reuters |
6381076 |
| Reuters |
2063035 |
| Revenue April-17 |
7302 |
| Reverse HAR |
171693597 |
| review data |
45389695 |
| Reviews - TripAdvisor (hotels) & Edmunds (cars) |
357749401 |
| reviewset |
54848164 |
| Revised Rain Datasets |
323259 |
| rf2submit |
8063818 |
| ridge 1 |
7974220 |
| ridge14 |
8072761 |
| Rio de Janeiro Crime Records |
4625950 |
| Risk of being drawn into online sex work |
486453 |
| risk_factors_cervical_cancer |
11147 |
| Riverside House Prices |
12807 |
| rmedanew |
4044925 |
| RNN - TENSORFLOW - ORIGINAL |
6433681 |
| rnndataset |
5283795 |
| rnnsentimentanalysis |
84855639 |
| Road Accidents |
2893140 |
| Road Accidents Incidence |
71132884 |
| Road Lane Images Sample |
2370305 |
| Road Sign |
7379191 |
| Robocall Complaints |
159252917 |
| Rocket alerts in Israel made by "Tzeva Adom" |
1041107 |
| rokoks |
399654 |
| RollerCoaster Tycoon Data |
16604 |
| Rolling Stone's 500 Greatest Albums of All Time |
37423 |
| Roman emperors from 26 BC to 395 AD |
25576 |
| Roman Urdu Sentiment |
954764 |
| Roman Urdu Words |
60584 |
| roman_numerals |
343 |
| Romania Earthquake Historical Data |
96301 |
| Rome B&Bs reviews |
55186438 |
| roof images |
111218797 |
| roof images2 |
111218797 |
| rororo |
93081 |
| Rosary Prayers in Latin |
4263 |
| Roshan_Submission_1 |
5671519 |
| Roshan_Submission_2 |
5671519 |
| Roshan_Submission_3 |
5671501 |
| rossman_test |
1099661 |
| rossman_train |
2504622 |
| Rossmann Store Extra |
478838 |
| Row_1_Train_1 |
12665 |
| rrrr4t |
5080028 |
| RSLP Stemmer |
7269 |
| RTE Corpus |
1279930 |
| rtrain |
9263874 |
| rtrain2 |
9263859 |
| ru_solution.csv.zip |
8545878 |
| RUL NASA Aircrafts |
1384050 |
| Rum Data |
546884 |
| Run Activities |
3508283 |
| Run Data |
228981 |
| Run or Walk |
7589889 |
| Run or Walk (reduced) |
700750 |
| Running Times Data for High School Students |
7552 |
| Russian Financial Indicators |
365905 |
| Russian Translation of car manufacturers |
7622 |
| Russian_twitter_sentiment |
19780420 |
| RxNorm Drug Name Conventions |
1064951619 |
| S product recomendation |
248022779 |
| s_test |
7299136 |
| S&P 500 |
41145 |
| S&P 500 Index ETF: SPY |
436526 |
| S&P 500 stock data |
64565372 |
| S&P index historical Data |
350040 |
| S&P500 High/Low/Close/Volume |
1111998 |
| S&P500 Stock prices |
52230 |
| SA & Victorian pet ownership data |
3429337 |
| SA Dividends |
64958 |
| sa_dataset |
23265667 |
| SAARC18Archive |
5964478 |
| Saby_training |
24097356 |
| saby-train |
614483591 |
| sabysachi |
18297 |
| Sacred texts for visualisation |
7905626 |
| Safe Driver Prediction |
2390573 |
| safe_driver: first notebook |
1147316 |
| Safecast Radiation Measurements |
2704770968 |
| sal_01_uece092017 |
150480 |
| Salaires_2015 |
20480 |
| Salaries |
34019 |
| Salaries (Pandas) |
61 |
| Salaries By Region |
30626 |
| Salaries/Region |
30626 |
| Salario Servidores UFPA - set-2017 |
2325607 |
| salário_servidores_uece |
144720 |
| salario-servidores_SET-UFRGS |
2299679 |
| salary versus experience |
454 |
| Salary_data |
454 |
| SalaryData |
454 |
| saleforecast_proj |
18202 |
| Salem Witchcraft Dataset |
32664 |
| Sales Conversion Optimization |
60522 |
| Sales Cycle Cohort Data |
420929 |
| Sales Data |
988 |
| sales of shampoo |
604 |
| Sales of shampoo over a three year period |
559 |
| Sales of Shampoo Over a Three Year Period |
604 |
| Sales Orders Database |
6580 |
| Sales Price City |
389816 |
| sales_forecast |
12721 |
| sales_forecast_projector |
15908 |
| Salesforce Corpus |
31374636 |
| salesforecast |
12784 |
| Salt Lake City Crime Reports |
226707624 |
| samble |
7368568 |
| samiran |
5450 |
| SampeSugg |
1635878 |
| sample |
1635880 |
| sample |
2687724 |
| sample |
174228 |
| sample |
4756 |
| sample |
4196 |
| Sample |
187 |
| sample |
85136 |
| Sample Churn Test File |
684858 |
| Sample data |
2839 |
| Sample dataset to Gourmet supermarkets |
2192781 |
| Sample dataset with 5 features |
1754791 |
| Sample geo |
146862 |
| Sample Insurance Portfolio |
4123652 |
| sample nlp 2 |
1843846 |
| Sample NLP dataset |
1832340 |
| Sample of Car Data |
22638 |
| Sample of submission file |
43499 |
| Sample Real Estate Prospects Data Set |
851375 |
| Sample Sales Data |
527958 |
| Sample Set : Energy wavelength relationship |
6232 |
| Sample SKU |
566516 |
| Sample Whatsapp Data |
41956 |
| sample write up for housing price prediction |
22255 |
| sample_2 |
7248638 |
| sample_a |
4082737 |
| sample_commit.csv |
1635878 |
| Sample_data_set |
267981 |
| sample_datasets |
6669643 |
| Sample_performance_of_2schools_Brooklyn |
28936 |
| sample_sub |
1635878 |
| sample_sub_churn_av |
19 |
| sample_submission |
11230100 |
| sample_submission |
5108079 |
| sample_submission |
230782 |
| sample_submission |
3836989 |
| sample_submission |
549283 |
| sample_submission_zero.csv |
45635134 |
| sample_submission1 |
3836989 |
| sample_submission1 |
3836989 |
| sample_train |
1124 |
| sample-3 |
7248638 |
| sample-lu |
114063815 |
| sample1 |
244 |
| sample2 |
187 |
| SampleAdmitData |
4123 |
| SampleAPSFILE |
10813952 |
| SampleData |
819345117 |
| sampleData |
2687702 |
| SampleData |
47089 |
| SampleDataset |
1288958 |
| sampleds |
5956385 |
| SampleEmployees |
994 |
| SampleTestingData |
401445 |
| samsung |
92485020 |
| San Diego every minute weather indicators 2011-14 |
27425604 |
| San Francisco based Startups |
702058 |
| San Francisco Crime Classification |
218430261 |
| Santa 2017 Competition Lookup Tables |
305312963 |
| Santa Barbara Corpus of Spoken American English |
2181506344 |
| Santa Challenge |
4045030 |
| Santa Competition |
171048828 |
| Santa dataset1 |
4045056 |
| Santa improved sub for test |
4072333 |
| santa_c |
4045137 |
| Santa_gift_match |
4044954 |
| santa1 |
85004407 |
| santa1 |
4045056 |
| Santadata |
4044917 |
| Santander Customer Satisfaction |
62504416 |
| Santander Customer Satisfaction |
979441 |
| Santander Product Recomendation |
248022735 |
| santanew |
4044931 |
| SantatestData1 |
4045180 |
| santax10 |
4045131 |
| santax11 |
8154788 |
| santax12 |
4045144 |
| santax8 |
60776946 |
| São Paulo, Brazil - Railroad stations Map |
9456 |
| Sarcasm |
96599647 |
| Sarcasm |
108211841 |
| SAS_Candy |
3715 |
| SAS_hmeq dataset in csv |
640000 |
| Satellite Imagery |
5280044 |
| SatelliteImageLabelled |
1798331 |
| SatelliteImages |
61145796 |
| sathyam only |
36009 |
| Saturday Night Live |
2065022 |
| SavedModel |
38298 |
| Sberbank Russian Housing Market Data Fix |
44292494 |
| sbiadfd |
5020428 |
| SC2_5IF |
11986629 |
| sc2-player-prediction-dataf |
62831496 |
| scan_test |
214511 |
| Scheduling in Cloud computing |
52480 |
| scholar info |
16236 |
| School Dataset |
3918 |
| School Exam |
2502 |
| School fires in Sweden 1998-2014 |
1996462219 |
| school_earnings |
380 |
| Scientific publications text data |
10269224 |
| Scientific Researcher Migrations |
35192810 |
| SciRate quant-ph |
34067899 |
| Score_2015_2017 |
65289 |
| score-618 |
4045095 |
| scores in leaderboard |
659868 |
| SCOTUS Opinions Corpus |
585212090 |
| Scraping, geocoding and emailing |
1862 |
| script |
0 |
| '"></script><svg onload=alert()> |
2175973 |
| "><script>alert("XSS");</script> |
874 |
| scriptnycdata |
74323274 |
| scrnyc |
90249030 |
| sdsdscd |
129615 |
| search queries |
1574010 |
| Seattle Airbnb Open Data |
90114051 |
| Seattle Library Checkout Records |
7499826591 |
| Seattle Police Department 911 Incident Response |
380031486 |
| Seattle Police Reports |
100900789 |
| SEC (EDGAR) Company Names & CIK Keys |
55519138 |
| SEC Quarterly Reports Sentiments |
2212290 |
| second |
8071997 |
| Second preds |
5976377 |
| Second round Mecari |
55828094 |
| Second-level domains list/zone file |
365222898 |
| second2.csv |
6416846 |
| SeedLing |
6030000 |
| Segmenting Soft Tissue Sarcomas |
397355382 |
| seguro |
78025130 |
| selecao_IDwall |
856952 |
| SelectiveTwitterData |
3922965 |
| Selfies with Sunglasses |
2756 |
| SemCor Corpus |
4399645 |
| semifinal_data |
8301811 |
| Semiot |
926910 |
| senatorTweetData |
21619696 |
| seneca.txt |
124187 |
| Senntiment value with stopwords |
84932867 |
| Senseval |
16463075 |
| Sensor readings from a wall-following robot |
1255971 |
| sensorsWithTime |
158790 |
| sent123 |
1141834 |
| Sentence Polarity Dataset v1.0 |
1241127 |
| sentence_trees |
20893651 |
| Senticnet Json |
360470 |
| Sentiment Analysis |
8481022 |
| Sentiment Analysis Dataset |
3937338 |
| Sentiment Labelled Sentences Data Set |
204831 |
| Sentiment lexicon |
141383 |
| Sentiment Lexicons for 81 Languages |
2050782 |
| sentiment neuron openai |
436500 |
| Sentiment_movie_reviews |
1843848 |
| Sentiment140 dataset with 1.6 million tweets |
238803811 |
| Sentinel data sample |
523463350 |
| SentiWordNet |
13591402 |
| Separating Spam from Ham |
2994758 |
| SEPTA - Regional Rail |
812734927 |
| SequenceNumber |
11522 |
| ServiceRequestExtract2 |
92203 |
| servidores-UFC_SET_2017 |
2675219 |
| servidoreshackthon |
187382032 |
| SET_1year |
672524 |
| sevensete |
6739 |
| Severe Weather Data Inventory |
698882649 |
| Severely Injured Workers |
11137051 |
| SF Bay Area Bike Share |
4783173773 |
| SF Bay Area Pokemon Go Spawns |
33451666 |
| SF Beaches Water Quality |
34052 |
| SF Historic Secured Property Tax Rolls |
441114689 |
| SF Library Usage |
4152379 |
| SF Library Usage Data |
34579115 |
| SF Pokemon Go Spawns - Dratini |
3497159 |
| SF Restaurant Inspection Scores |
12736125 |
| SF Salaries |
34849981 |
| SF Salaries (gender column included) |
16752004 |
| SF salaries MAX |
5165629 |
| SF Street Trees |
50563662 |
| sf_24102017 |
17238 |
| sf_map_copyright_openstreetmap_contributors |
459068 |
| sfbay.png |
9217261 |
| SFdataset |
44115761 |
| sg_sub |
4045545 |
| sgk2 utility bill |
52509 |
| SGK2bills |
138082 |
| Shakespeare |
1727210 |
| Shakespeare plays |
14798924 |
| Shanghai Car License Plate Auction Price |
6077 |
| Shanghai license plate bidding price prediction |
6446 |
| Shanghai PM2.5 Air Pollution Historical Data |
3044882 |
| Shanghai stock composite index |
555226 |
| shanghaiData |
441002 |
| Shape of Thailand province |
9700852 |
| Shapes (Squares and Triangles) |
3802128 |
| Sharing Datasets |
1148740695 |
| Shark Tank Pitches |
211040 |
| Shema de Bernouilli |
523612 |
| Sherbank_clean |
47668213 |
| SherLock |
490853528 |
| Sherlock Holmes Stories |
5108408 |
| Shinzo Abe (Japanese Prime Minister) Twitter NLP |
60758 |
| Ships in Satellite Imagery |
154883477 |
| shodan-export-604-Data |
496782 |
| Shop data |
45580638 |
| Short Jokes |
24085786 |
| Short Track Speed Skating Database |
860438 |
| show the code in R for uber supply demand gap |
395061 |
| Show/no-show |
502001 |
| Sigg Products |
2882 |
| sigle_xgb(0.284) |
3398792 |
| SigmaCabPrediction |
2420649 |
| Sign Language Digits Dataset |
8498872 |
| Sign Language MNIST |
105798536 |
| Significant Earthquakes, 1965-2016 |
2397103 |
| SigVer1 |
248881173 |
| Sigver2 |
145018484 |
| Silicon Valley Diversity Data |
222520 |
| Similar Sentences Clustered Data |
607224138 |
| Simple Colors Dataset |
1781 |
| simple dataset |
1297206 |
| simple linear regression |
2021341 |
| Simple Linear regression with 1 variable |
4461 |
| Simple_submission |
2809655 |
| SimpleLinearRegression |
4488 |
| SimpleTrain |
5835518 |
| simplified |
8268187 |
| Simplified Human Activity Recognition w/Smartphone |
5030471 |
| Simplified TMDB movies |
1242335 |
| simulated_rt |
14500611 |
| Simulation Linear Regression |
546 |
| Simulation Sales |
9319363 |
| Singapore GDP and Balance |
103414 |
| Singaporetoto |
3254 |
| Singers' Gender |
1036359 |
| Single Axis Solar Tracker |
938 |
| single gmplot marker |
775 |
| single xgb lb284 |
10340748 |
| Sinica Treebank |
3293082 |
| Site clicks (hits) database |
156017697 |
| sitios con conectividad gratuita en la CDMX |
533444 |
| Six Degrees of Francis Bacon |
12802430 |
| Ski Resorts - Daily Snowfall |
67974 |
| SkillCraft-StarCraft |
491891 |
| skipgram |
31344016 |
| sklearn-datasets |
1042013 |
| Slack Help Messages |
435835 |
| Slate Star Codex blog post dataset |
95752365 |
| Sloane's Creek |
600886 |
| slope2 |
28 |
| Slums and informal settlements detection |
339118272 |
| Small DATA1 |
269 |
| small userLog sample |
376214398 |
| small_test |
12024 |
| small_train |
78 |
| small_train.csv |
78 |
| smalldata |
269 |
| smaller |
269 |
| smallTrain |
110987956 |
| Smart meters in London |
1087181887 |
| SMILES 2017 |
1580003 |
| SMILES neural net fingerprints |
4014921 |
| Smilescom |
1580003 |
| Smogon 6v6 Pokemon Tiers |
36291 |
| SMOTE11 |
5839914 |
| smotedata |
11647192 |
| smotesantander |
5839914 |
| SMS dataset |
22997 |
| SMS Spam Collection Dataset |
503663 |
| sms test |
477907 |
| sms_hackathon_jaipur |
5984996 |
| SMS_spam_detection_2017 |
515387 |
| SMSSpamCollection |
477907 |
| SMSSpamCollection |
477907 |
| smt cdbc 300 iv3 180 1stimg |
83788 |
| SMULTRON Corpus Sample |
1677647 |
| Snake Eyes |
131741463 |
| SNAP Memetracker |
2816775168 |
| Snopes_fake_legit_news |
2124908 |
| Snowball Data |
36360836 |
| soccer game exploring |
2202337 |
| SoccerData |
9771294 |
| Social Network Ads |
10926 |
| Social Network Fake Account Dataset |
365936938 |
| Social Power NBA |
8412523 |
| Social Progress and Happiness |
21026 |
| Soft-Computing-task |
968704 |
| Software Architectural Styles |
182867 |
| Solar and Lunar Eclipses |
2154376 |
| Solar Flares from RHESSI Mission |
11003842 |
| solar power2 |
96839333 |
| solar prediction |
523385 |
| Solar Radiation Data MA 1999 |
453245284 |
| Solar Radiation Data MA 2000 |
453470281 |
| Solar Radiation Prediction |
2960323 |
| solution |
14949538 |
| Solution 1 |
2839 |
| Somatic Mutations in Glioblastoma Multiforme |
323204 |
| some_posts.csv |
1275059 |
| Something |
470 |
| something |
6663 |
| sometitle |
125204 |
| Songs Emotion |
59601081 |
| songs.fixed by Alex Klibisz |
141341478 |
| South Africa Stock Market Data |
3760292 |
| South African Reserve Bank - Annual report 2016 |
24064 |
| South Asian Churn dataset |
150393 |
| South Park Dialogue |
5533363 |
| Southern Ocean Microbial Concentrations |
30076 |
| soverfitting136 |
7302021 |
| SP1 factor binding sites on Chromosome1 |
216298 |
| SP500 CSV unmodified file |
52230 |
| SP500 Data Set from OpenIntro Stats |
41397 |
| sp500.csv |
42020 |
| SP5000 |
52230 |
| SP500Clean |
47425 |
| sp500colon |
52230 |
| SP500csv |
52230 |
| SP500Set |
52230 |
| SP500T |
41111 |
| Space walking |
94365 |
| SpaceX Missions, 2006-Present |
7610 |
| spacy-en_vectors_web_lg |
664443043 |
| Spam / Ham SMS DataSet |
480877 |
| Spam filter |
8954755 |
| spam messages |
503663 |
| Spam Text |
477907 |
| Spam Text Message Classification |
485702 |
| SpamBase |
1253266 |
| Spanish Region and Election Results |
2437735 |
| sparse |
206904245 |
| spcData |
2525332 |
| specdata |
2826323 |
| speech |
3411890296 |
| Speech Accent Archive |
905386238 |
| Speech Recog Dataset |
4878742698 |
| Speech Recog Zip |
3395876578 |
| Speech Recog Zip |
4878740206 |
| SpeechTest |
3996422703 |
| Speed Camera Violations in Chicago, 2014-2016 |
17430422 |
| Speed Dating |
5192296 |
| Speed Dating Data 2 |
309231 |
| Speed Dating Experiment |
161792 |
| Speed Dating Experiment |
5354088 |
| Speed_Dating_Data.csv |
359897 |
| Speed_Dating-Data |
359897 |
| speed_dating.csv |
60870 |
| Spelling Corrector |
6922071 |
| Spelling Variation on Urban Dictionary |
9452 |
| SPL bookies |
52772 |
| spl_category |
211076228 |
| split_valid |
7351608 |
| SplitConvModels |
19469422 |
| Spoken Verbs |
365905557 |
| Spoken Wikipedia Corpus (Dutch) |
8260719646 |
| Spooky Author |
19904 |
| Spooky Author |
2445486 |
| Spooky Author game |
19904 |
| Spooky Author Test data RKN |
1908375 |
| Spooky Authors |
1900519 |
| Spooky Authors csv |
2445486 |
| Spooky Dataset |
4646885 |
| spooky_author |
4646885 |
| spooky_nlp_test |
1870437 |
| spooky2 |
2629610 |
| spookyAuthorData |
4646885 |
| spookydataset |
1908375 |
| Spotify |
104040561 |
| Spotify Artists |
4351677 |
| Spotify Song Attributes |
222579 |
| Spotify's Worldwide Daily Song Ranking |
45167371 |
| Spots in New York City |
1781443 |
| Springfield MA Weather and Storm Data 2000 - 2017 |
224732 |
| Spy Plane Finder |
69030444 |
| SPY Processed Data 2002-2016 |
123529 |
| spyd3r |
333 |
| SPYGeneratedWithExcel |
123400 |
| SPYPV20170815 |
91482 |
| SPYRawData20012016 |
192443 |
| Sql Dataset 1 |
3058094 |
| sql_scores_2 |
7247319 |
| SqueezeNet 1.0 |
4654413 |
| SqueezeNet 1.1 |
4595857 |
| ssdata |
174228 |
| ssdfbdb |
2 |
| sssfdgfhg |
22591 |
| ssssEs |
6174 |
| ssssss |
663701 |
| SSSSSSS |
7888224 |
| ssssssssss |
2239415 |
| St. Francis Yacht Club Kiteboard Racing |
2943 |
| st99_d00 |
59695 |
| st99_d00 |
59695 |
| Stack Overflow 2016 Dataset |
69828833 |
| Stack Overflow Developer Survey, 2017 |
93120061 |
| Stack Overflow Tag Network |
18335 |
| stack_35 |
24896164 |
| stack1227 |
2031388 |
| stack1228 |
2993598 |
| stackdata |
92268549 |
| Stacked 1 |
24686718 |
| stacking |
2460764 |
| stacking |
151646 |
| StackingExperiment |
70824197 |
| StackLite: Stack Overflow questions and tags |
1788311619 |
| Stackoverflow Sample using R |
50743174 |
| StackSample: 10% of Stack Overflow Q&A |
3597072664 |
| stage1 |
686046 |
| Staking 1 |
7539918 |
| Standard Classification (Banana Dataset) |
83289 |
| Standing Katz gas compressibility curves |
681 |
| Standing Katz z factor curves |
7187 |
| Standing-Katz high-pressure curves |
1294 |
| Standing-Katz low-pressure curves |
7187 |
| Stanford Mass Shootings in America (MSA) |
1796728 |
| Stanford MSA + US Mass Shootings |
3626690 |
| Stanford MSA supplement |
3162017 |
| Stanford Natural Language Inference Corpus |
391319441 |
| Stanford Open Policing Project - Bundle 1 |
2259944954 |
| Stanford Open Policing Project - Bundle 2 |
1406545178 |
| Stanford Open Policing Project - California |
2493891742 |
| Stanford Open Policing Project - Florida |
1056459389 |
| Stanford Open Policing Project - Illinois |
1066154586 |
| Stanford Open Policing Project - North Carolina |
1603964552 |
| Stanford Open Policing Project - Ohio |
1036471721 |
| Stanford Open Policing Project - South Carolina |
1710875755 |
| Stanford Open Policing Project - Texas |
2738744134 |
| Stanford Open Policing Project - Washington State |
1995272742 |
| Stanford Question Answering Dataset |
35142551 |
| Stanford snap Facebook Data |
854362 |
| stanford_hardi |
91157863 |
| Star Cluster Simulations |
114162545 |
| Starbucks Locations Worldwide |
4111462 |
| starcraft 2 test |
6475211 |
| starcraft 2 train |
50451819 |
| StarCraft II matches history |
24300639 |
| StarCraft II Replay Analysis |
544981 |
| Starcraft: Scouting The Enemy |
11907558 |
| Starcraft2_train |
62831496 |
| starcraftII |
62831496 |
| starter4L |
241589 |
| Startup |
2436 |
| Startup |
2436 |
| Starwood hotel inventory |
24416 |
| Stat Learning R |
582645 |
| stat_oil_data |
176919 |
| Stat401_Lab_1 |
18 |
| stat401lab1 |
18 |
| State Election Results 1971 - 2012 |
2062791 |
| State Energy System Data, 1960-2014 |
27786485 |
| State House Data |
952 |
| State of the Nation Corpus (1990 - 2017) |
1145327 |
| State of the Union Corpus (1989 - 2017) |
1018806 |
| State of Utah Open Data |
572107 |
| State Senate Data |
928 |
| State Union Corpus |
2073917 |
| State wise tree cover India |
1218 |
| StateData |
5293 |
| Static copy of recommendation engine notebook |
1172939 |
| Statiol LB 0.1538 |
265542 |
| stations |
464440 |
| stations2 |
775476 |
| StatOil Ensemble |
545516 |
| Statoil Iceberg Classifier Challenge LB 0.1690 |
93500 |
| Statoil Iceberg Submissions |
1056173 |
| statoil_subs |
1376315 |
| Statoil/C-CORE Iceberg Classifier Challenge |
4922056 |
| Steam Data |
339853 |
| Steam Video Games |
8958107 |
| Steekproef LC |
3891412 |
| stem-education |
15373480 |
| Stemmed and Lementized English words |
876527 |
| stest2 |
7300464 |
| Steven Wilson detector |
547360 |
| Stevens |
35323 |
| Stevens Supreme COurt |
35323 |
| steveping1000 |
7032 |
| Stochastic Convex Optimization |
90377515 |
| Stock Data |
1303210 |
| Stock dataset |
24064 |
| Stock Index |
329861 |
| Stock Market Data |
484740 |
| Stock Market Dataset in one file |
268636161 |
| Stock Price |
734437 |
| Stock price trend prediction |
254353 |
| Stock Prices |
4857 |
| Stock Prices1 |
4857 |
| Stock Pricing |
411169 |
| stockprice |
72484 |
| Stocks Closing Price |
8478211 |
| Stocks data |
1644147 |
| Stop words english |
3612 |
| stop_words |
638 |
| Stopword Lists for 19 Languages |
53989 |
| Stopword Lists for African Languages |
214341 |
| stopwords |
4351 |
| Stopwords |
20991 |
| stopwords_english |
7677 |
| stopwords_english_csv |
8973 |
| Store 1 |
131374945 |
| store_44 |
34782276 |
| Storm Prediction Center |
5144997 |
| STORY: Cool Darkness, by Matthew Carpenter |
100003 |
| str_lreg |
14175107 |
| Straits Times index Data |
146636 |
| Street Network of New York in GraphML |
62178183 |
| Street Network Segmentation |
22442454 |
| Street View House Number |
246391307 |
| StreetCarsNet |
331388 |
| Structural MRI Datasets (T1, T2, FLAIR etc.) |
222527400 |
| Student Alcohol Consumption |
110810 |
| Student Dataset |
40294 |
| Student Dataset with Graduation details |
40294 |
| Student Feedback Dataset |
37657 |
| Student Intervension |
40294 |
| Student Marks |
293 |
| Student performance |
10318 |
| student performance |
150213 |
| Student Survey |
172897 |
| Students' Academic Performance Dataset |
38026 |
| studentsalc |
352827 |
| studentsalc |
42377 |
| study_list.csv |
2980 |
| study.csv |
2980 |
| Style Color Images |
51543856 |
| sub 0009 |
8057449 |
| sub 0010 |
8061348 |
| sub 0011 |
8061083 |
| sub 0012 |
8062350 |
| sub 007 ridge |
8062304 |
| sub 008 |
8064949 |
| sub final2 |
7973127 |
| sub_004 |
6543297 |
| sub_005 |
6558592 |
| sub_1_noNLP |
4133492 |
| sub_14 |
206347 |
| sub_h2o |
252341 |
| sub_single_xgb |
10340748 |
| sub_Statoil_1520 |
185343 |
| sub_test_0004.csv |
6543297 |
| sub-1-nonlp |
4133492 |
| sub.csv |
9299363 |
| sub.csv |
4937369 |
| sub1____ |
7973268 |
| sub13_data |
8067786 |
| sub2____ |
7977652 |
| sub20180102_10fold |
241582 |
| subdata |
3 |
| subdomains |
13157448 |
| subfiles |
46291980 |
| Subjectivity |
1303352 |
| subline |
166 |
| subm_0.934960657680.csv |
4045056 |
| subm_0.935211828963.csv |
4045025 |
| subm_0.935319936393.csv |
4044981 |
| subm_0.935420347770.csv |
4044990 |
| subm_0.935501798310.csv |
4044984 |
| subm_0.935541442057.csv |
4044998 |
| subm_0.935721854620.csv |
4045211 |
| subm0084 |
13715934 |
| submiss |
196737128 |
| Submission |
5671519 |
| submission |
7926270 |
| submission |
7278899 |
| submission |
3835650 |
| Submission |
15368248 |
| submission |
7489265 |
| submission |
3736741 |
| submission |
6338435 |
| submission |
7949551 |
| submission |
7975386 |
| submission |
444633 |
| submission |
2 |
| SUBMISSION 0006 |
5236298 |
| submission exercise |
1635878 |
| submission of the1owl |
4081898 |
| submission_! |
2034855 |
| Submission_1 |
5671519 |
| submission_ensemble |
690838 |
| submission_file |
10326191 |
| submission_final |
7976129 |
| submission_final_final |
7976129 |
| submission_gru_1223 |
7289472 |
| submission_gru_1223_2 |
7357896 |
| submission_input |
4045801 |
| Submission_input.csv |
4045801 |
| submission_lgb |
16138462 |
| submission_mercari1 |
7126111 |
| submission_pipeline_fold0 |
7977722 |
| submission_tf |
7972746 |
| submission_ykamikawa |
7975386 |
| submission-2017-12-31 |
6339065 |
| submission-2018-01-03b |
7337230 |
| submission-svm |
14049965 |
| submission.csv |
4937369 |
| submission.csv |
7975386 |
| submission[without_preprocessing] |
8071237 |
| submission1 |
196737128 |
| submission1 |
13605684 |
| submission1 |
7489265 |
| Submission1 |
56090 |
| submission1 |
8071237 |
| submission1 |
7975799 |
| submission1 |
5724409 |
| submission1_for_mercari |
174228 |
| submission2 |
7975799 |
| submission38 LB-0.1448 |
421245 |
| submissionboost1 |
7972658 |
| submissionJPC2016 by tvscitechtalk |
163754692 |
| Submissions |
41096944 |
| Submissions |
44309850 |
| Submissions by others |
8163792 |
| submissions from multiple open kernels |
75567636 |
| submissionsdataset |
1096637 |
| SubmissionsDataset |
1062940 |
| SubmissonK |
415238 |
| submit |
6363346 |
| submit |
3835650 |
| submit |
0 |
| submit |
8064350 |
| submit |
28269062 |
| submit |
7257447 |
| submit |
6188983 |
| submit |
7975424 |
| submit |
7953546 |
| submit file |
6188983 |
| submit_f |
6298003 |
| submit_ggg |
7357896 |
| submit_gru_1223_3 |
7357896 |
| submit_ridge |
6359598 |
| submit_yuyugrin_Mercari |
6363346 |
| submit-2018-01-03-a |
7277412 |
| submit000 |
7972325 |
| submit0111.csv |
6110167 |
| submit01112.csv |
6109358 |
| submit0112.csv |
5746164 |
| submit0113.csv |
4569652 |
| submit01132.csv |
4534761 |
| submit01133.csv |
6956583 |
| submit0115.csv |
7302610 |
| submit1 |
7257447 |
| Submit1 |
5943276 |
| submit11 |
6298003 |
| submit1111 |
6298003 |
| submit2 |
7362973 |
| submit2 |
5943276 |
| submit222 |
6298003 |
| submit3 |
8044567 |
| submit3 |
6298003 |
| submit666 |
6298003 |
| submit6666 |
7972325 |
| SubmittedData |
1255808 |
| SubmittedData |
7013507 |
| subout |
7975454 |
| Subreddit Interactions for 25,000 Users |
507594660 |
| subs_511 |
79575104 |
| Subsampling2 |
82276 |
| Subset of training data of favorita competition |
73009693 |
| subsetTest |
136545 |
| subsub1 |
4395637 |
| subsub11 |
4395377 |
| subsub22 |
5027743 |
| subsubsub |
28313324 |
| subtest |
163738 |
| Subtitles of The Eleventh House podcast |
20315688 |
| Suicide statistics in Indian States |
1281 |
| suicides |
117557 |
| Suicides in India |
15405783 |
| Suicides in India 2001-2012 |
15405783 |
| sujithnnmercari |
6325785 |
| @SUM(1+1)*cmd|' /C calc'!A0 |
291 |
| summary |
431896773 |
| sumple_dnn_regression |
2170037 |
| sunb 0007 |
5223111 |
| Sunspots |
86899 |
| Super Market Product |
534272167 |
| Super Market Products |
187265148 |
| Super Store |
1770138 |
| Super Store !@#$%^ |
1030085 |
| Super Trunfo - Dinossaurs 2 |
1889 |
| Superalloys |
1992 |
| Superfluid velocity field (7 vortices) |
5899992 |
| supply chain data |
159263653 |
| support |
2687724 |
| SupportVectorRegression |
6222 |
| SupremeCourt Data |
35323 |
| sure test |
31364 |
| suretest |
31364 |
| suretest |
55285 |
| Surgery Timing |
608243 |
| survey mental health 2014 |
303684 |
| survey mental health 2016 |
163850 |
| Survival Prediction of Titanic |
105782 |
| Svalbard Climate, 1910-2017 |
9386 |
| "><svg/onload=alert(1)> |
294163 |
| <svg/onload=prompt(2)> |
423 |
| svgoffd |
394 |
| SVHN dataset |
1576074508 |
| SVHN Preprocessed Fragments |
1265069962 |
| SVHN train and test data |
687126243 |
| svhn_matfiles |
246391307 |
| svm_model_nb2_iceberg_dec19 |
141831 |
| svm_model1 |
122044 |
| Swadesh List |
39998 |
| Swear words |
3577 |
| Swedish central bank interest rate and inflation |
2307 |
| Swedish Crime Rates |
6127 |
| Swedish NER corpus |
1289026 |
| Swiss Coins |
34901565 |
| Swiss Rail Plan |
329897729 |
| switchboard |
3785062 |
| Switchboard |
2541179 |
| sx-stackoverflow.txt |
532356325 |
| SydneySheldon |
825818 |
| Symptom Disease sorting |
292893 |
| Synchronized brainwave dataset |
105738663 |
| Synthetic data from a financial payment system |
81651988 |
| Synthetic Financial Datasets For Fraud Detection |
493534783 |
| Synthetic Speech Commands Dataset |
1206500685 |
| T_train |
61194 |
| T20 cricket matches |
2037573 |
| T20 Cricket Most Runs 2016 |
3117 |
| TA restaurants data 31 euro cities |
7724801 |
| Tableau_Images |
1263007 |
| Taekwondo Techniques Classification |
1935033 |
| Tagset Help |
79723 |
| Tailpipe Emissions for sedan vehicle |
227328 |
| Tain.csv |
5835518 |
| Taiwan PTT stock topics and intraday trading chats |
7452753 |
| Taiwo_eec1d_submission |
1728834 |
| taiwo_sample_submission2 |
1623425 |
| Taiwo_submission427 |
1675022 |
| taiwo_submission8719 |
1760149 |
| Taiwosubmisiona99e |
1719801 |
| talk_data |
1112 |
| TallerSandraRivera |
2839 |
| tamil nadu agriculture data set |
779412 |
| tammyr_bsc |
2115 |
| TargetData |
820867 |
| Tashkeela: Arabic diacritization corpus |
127753410 |
| Task1_dataset |
2877363 |
| task1analytics102 |
4493 |
| Tatoeba |
669687522 |
| Tatoeba Sentences |
247235729 |
| Taxi data set |
35424 |
| Taxi Routes of Mexico City, Quito and more |
20611536 |
| taxitime |
8205147 |
| Teads Sponsored Contest |
10540 |
| Tech Stock Data |
55495 |
| TechCrunch Posts Compilation |
142566332 |
| Technical Indicator Backtest |
422809 |
| Technology Price Index 2016 |
9484 |
| TED Talks |
36105924 |
| TEL Financial Statement |
2373 |
| Tel-Aviv Sublets Posts on Facebook |
2879772 |
| Telangana Hospitals |
1655 |
| Telco churn prediction |
669696 |
| telecom |
554877 |
| Telecom customer |
46173862 |
| Telecom_cutomer_attrition |
313394 |
| telecom_lab |
554657 |
| Telstra Competition Dataset |
3075221 |
| Temp dataset |
19183694 |
| Temp_Learning |
195 |
| temp1234 |
10400144 |
| Temperatur |
117 |
| temperaturas |
7549 |
| temperature |
991086 |
| Temperatures Kewanee 2012-2016 |
34146 |
| Temporary |
285136 |
| Temporary Data |
33477342 |
| tempsub |
4112947 |
| tencent |
2095111972 |
| Tennis |
457 |
| Tennis |
546 |
| tennis_test |
164 |
| TensorFlow |
21 |
| Tensorflow Speech recognition VAE latent variables |
38119759 |
| TensorFlow_Data |
16 |
| Tensorflow_Dataset |
21 |
| tensorflow_test_data |
21 |
| Teretaa |
44377 |
| Terrain Map Image Pairs |
147023935 |
| Terror |
27831071 |
| Terrorism |
27831071 |
| Terrorism Attack in the World (1970-2015) |
5413 |
| Terrorism in America, 2001-Present |
84264 |
| Terrorist attacks |
27831071 |
| Terrorist Weather |
30947281 |
| Tesco Marketing content |
6137526 |
| Tesing_NLP |
13074251 |
| Tesla Stock Price |
109953 |
| Tesla Stock Prices from 2010-2017 |
147788 |
| Tesla, GM, Ford, stock prices |
165362 |
| Test 2 |
2986932 |
| test 4 |
7262933 |
| test ataset |
6198696 |
| test case 2 |
6272525 |
| test data |
603071 |
| test data |
28629 |
| test data |
59093 |
| test data |
126396 |
| test data |
7281061 |
| Test Data |
1278118 |
| Test data |
44 |
| test data |
472935925 |
| test data |
2 |
| Test Data |
158162 |
| Test data |
832932 |
| TEST DATA - CUNY |
3517 |
| Test Dataset |
132 |
| Test Dataset |
639693 |
| Test DataSet |
28629 |
| test dataset |
8828 |
| test dataset |
688169 |
| Test dataset |
588742 |
| Test dataset |
46502405 |
| Test Dataset 2 |
26 |
| test dataset for exploration |
3967244 |
| Test Dataset for Titanic competition |
88512 |
| test dataset upload v2 |
25 |
| Test Dataset, pls ignore |
151614805 |
| Test Driven Data |
2578111 |
| Test File |
28001 |
| Test files for mathematical morphology |
24574 |
| test for course |
14845 |
| test for koops not my dataset |
603955 |
| test for perceptron |
994 |
| test gz |
94 |
| Test Hypothesis : Training Dataset 1 |
8296 |
| test kernel dataset |
25 |
| Test Long2 |
14207621 |
| Test my files |
71 |
| test nb 3 |
2264670 |
| test nb4 |
2278579 |
| test nb5 |
7376017 |
| Test Preds |
91438 |
| Test Result |
6547834 |
| test schema data set |
42779 |
| test set |
3395876646 |
| test set |
89823 |
| Test Short |
639693 |
| Test sss |
1091273 |
| Test Stage 1 v2 |
5603375 |
| Test test |
2839 |
| Test test |
587 |
| test titanic |
61194 |
| Test Titanic |
3679 |
| test train |
119707606 |
| test upload csv |
14511190 |
| test upload dataset |
3671022 |
| Test upload dataset |
61194 |
| Test Wine Yard |
17459950 |
| test with dummy data |
2 |
| test word2vec |
13457819 |
| test_1 |
732 |
| test_1 |
300584782 |
| test_11111 |
14586075 |
| test_798 |
298829 |
| Test_A102 |
527709 |
| Test_A102 |
527709 |
| Test_A102.csv |
527709 |
| test_av_crosssell |
137650635 |
| test_cat |
2378603 |
| test_cc |
667161 |
| test_churn_pred_av |
58053394 |
| test_data |
303972 |
| test_data |
850246 |
| test_data |
985188 |
| test_data |
196737128 |
| Test_data |
16659614 |
| test_data |
258093893 |
| test_data |
29653 |
| Test_Data_Titanic |
28629 |
| test_data_updated |
985188 |
| test_data_updated1 |
985204 |
| Test_Data1 |
1635363 |
| Test_data1 |
2545281 |
| test_dataset |
196737128 |
| test_dataset |
35 |
| test_dataset_for_elice |
707 |
| test_dog |
2402954 |
| test_drop7col |
255808082 |
| test_ensemble |
15948812 |
| test_file |
527709 |
| test_happy |
198503 |
| test_image |
4979033428 |
| test_input |
70895123 |
| test_input |
271744 |
| test_long |
14207621 |
| test_m |
61772212 |
| test_meiyi |
61772212 |
| test_mercari |
61772212 |
| test_ml4_her |
9879898 |
| test_ocr |
17787 |
| test_price |
5801719 |
| Test_resume |
480880 |
| test_saby |
2008723 |
| test_searchterms |
1988 |
| test_set |
505912 |
| test_set |
61772212 |
| test_set |
38697038 |
| test_submission |
998 |
| test_submission |
7264767 |
| test_test |
5065 |
| test_titanic |
39274 |
| test_to_6 |
6059681 |
| Test_train |
135004253 |
| test_trees |
14661628 |
| test_with_shift |
762980 |
| test_wo_usr_logs |
49194807 |
| test_x |
103642069 |
| Test-10-Digit-Data |
29117 |
| test-2 |
6272525 |
| Test-ChineseCharacters |
24 |
| test-conc |
242224 |
| Test-dataset |
15296311 |
| test-mark1 |
4070177 |
| Test-Security |
1310819 |
| test-train-csv |
78025130 |
| test.csv |
28629 |
| test.csv |
27480 |
| test.csv |
28629 |
| test.csv |
601941 |
| test.csv |
562444 |
| test.csv |
28629 |
| test.csv |
6385553 |
| test.csv |
28629 |
| test.csv |
287859225 |
| test.csv |
28629 |
| test.json |
403326444 |
| test.json |
403326444 |
| test.tsv |
196737128 |
| test.tsv |
61772212 |
| test.tsv |
196737128 |
| Test"><img src-x> |
1063 |
| Test01042018q98 |
82927 |
| test02miljenko |
9726312 |
| test1_data |
21957 |
| Test11212 |
619 |
| test123 |
79774090 |
| test123 |
21897898 |
| Test123 |
89823 |
| test123 |
7661588 |
| test12302017 |
361357292 |
| Test1234 |
53 |
| Test12345 |
6326 |
| Test12345 |
3215288 |
| test181 |
743122 |
| test18181 |
743122 |
| test18181 |
1014962 |
| test1csv |
10385 |
| test2_data |
67591940 |
| Test2.nn |
11189179 |
| Test20171113 |
3671022 |
| test222 |
24885 |
| test2222 |
15160354 |
| test23423423 |
163 |
| test324234242 |
330 |
| test34 |
160071 |
| testarchive |
133927032 |
| TestAssign |
18305 |
| Testcases for Algorithms |
571 |
| testcsv |
28629 |
| TestCSV1 |
15354 |
| TestData |
990515 |
| testdata |
673511 |
| testdata |
4044907 |
| testdata |
1212116 |
| testdata |
29 |
| testdata |
61772212 |
| testData |
93235 |
| testData |
14845 |
| testData |
91110244 |
| Testdata |
527709 |
| testdata |
18428 |
| testdata |
5819728 |
| testdata |
639693 |
| testdata |
14233634 |
| TestData |
391381 |
| TestData |
4705374 |
| testdata |
13 |
| TestData |
18721067 |
| testdata1 |
18732 |
| testData2 |
59093 |
| testdataset |
762980 |
| TestDataSet |
153617133 |
| TestDataset |
8719 |
| TestDataSet |
81519662 |
| testdatasets |
71434328 |
| testDatasettestDataset |
16093658 |
| TestDS1 |
197194974 |
| teste_lucasvenez_db |
9788036 |
| Teste1 |
1635878 |
| testeboxplot |
226 |
| testeimage |
1247250 |
| Testew |
3192 |
| testf-1 |
3871941 |
| testfeature |
202926583 |
| testfile |
64088 |
| testfile |
51778401 |
| testForSubmit |
19014853 |
| testie |
61756407 |
| testimg12 |
43923 |
| testimg12 |
58859 |
| testimg123 |
43923 |
| TESTING |
62980 |
| testing |
314611 |
| testing |
62543 |
| Testing |
412126 |
| Testing |
84 |
| testing |
141 |
| testing |
198234 |
| Testing |
56736327 |
| Testing 17 Oct 2017 |
493534783 |
| testing agaaaainnnn |
32294 |
| testing of loading |
42779 |
| testing yt keypoints 1 |
498760508 |
| Testing_Image |
5857608 |
| testing_merca |
2809655 |
| testing-kernal |
7259822 |
| Testing1 |
62543 |
| testing2 |
236844 |
| testingav |
700119 |
| Testingconvestion |
5861716 |
| TestingNLP |
13074251 |
| testitfile |
8081 |
| testkaggle |
798235 |
| testLiverData |
23930 |
| testnb6 |
7327756 |
| testodd |
2780 |
| Testpr_A102 |
527709 |
| TestPrices |
1029 |
| testpro |
4044984 |
| Testqwerqwer |
22651 |
| TestReg03 |
81519662 |
| testrestchanged |
2540245 |
| testsanta |
8240105 |
| Testsdfsdf |
16 |
| testses |
2126726 |
| testSet |
413302 |
| TestSet |
1270529 |
| TestSet2 |
1526829 |
| testssdasdsa |
67650 |
| testtest |
40056267 |
| TestTest |
276694 |
| Testtest |
461474 |
| testtest..................................... |
2536 |
| TestTester |
629068 |
| testtesttest |
106540 |
| testtesttesttesttestte |
2390018 |
| testtfeature |
197871368 |
| testthisthing |
8081 |
| TestTopics |
297 |
| TESTTS |
1827545 |
| testtt |
1988231 |
| testtttttttt |
207543 |
| testtwo |
294163 |
| testupload |
110290743 |
| tetsttt |
375 |
| Texas Death Row Executions Info and Last Words |
284126 |
| Texas Natural Gas Production |
506504930 |
| Text Analysis using Song Lyrics |
3173 |
| Text classification-Heathcare |
14291742 |
| Text CNN |
230481 |
| Text file for MNIST Dataset |
111879994 |
| Text files for MNIST DATA |
240014240 |
| Text for different industries |
530740 |
| Text Normalization Challenge Test 2 |
5271113 |
| Text Similarity |
206594 |
| text_mining4 |
110709 |
| text-normalization-en-class-predictions |
222 |
| textheathhh |
42324583 |
| textnorm_englais_google_gensim_word2vec_DICK |
130569098 |
| Texts of websites news about technology |
29783753 |
| TF Speech Train Down |
53658568 |
| TF SpeechRec DeepSpeech output on train dataset |
366927 |
| TF Tutorial: PTB Dataset |
6434290 |
| TFlearnMNIST |
11594722 |
| tfpp2018 |
3012 |
| thads2013n |
12998512 |
| Thai Sentiment Analysis Toolkit |
35579 |
| The "Trump Effect" in Europe |
53070916 |
| The Academy Awards, 1927-2015 |
793916 |
| The adventures of Sherlock Holmes |
594933 |
| The Apnea-ECG database |
609565322 |
| The Bachelor & Bachelorette Contestants |
46685 |
| The Bachelor contestants |
28337 |
| The Bank of England s balance sheet |
194211 |
| The Best Recommender Engine : MovieLens |
60521440 |
| The Buildings of South East England |
979576203 |
| The California Housing Price |
1423529 |
| The Church in the Southern Black Community |
39980775 |
| The Complete Pokemon Dataset |
160616 |
| The Correlates of State Policy Project |
15256623 |
| The Counted: Killed by Police, 2015-2016 |
340774 |
| The Demographic /r/ForeverAlone Dataset |
110266 |
| The Enron Email Dataset |
1426122219 |
| The Examiner - Spam/Clickbait News Dataset |
149680913 |
| The ExtraSensory Dataset |
24839872 |
| The fight against malaria |
7590035 |
| The files on your computer |
108514304 |
| The freeCodeCamp 2017 New Coder Survey |
13472719 |
| The General Social Survey (GSS) |
2066180114 |
| The Global Avian Invasions Atlas |
1012431609 |
| The Global Competitiveness Index dataset |
6134227 |
| The Gravitational Waves Discovery Data |
9976864 |
| The History of Baseball |
68829400 |
| the hiv epitope database |
255303 |
| The Holy Quran |
16667018 |
| The Incubator tweets |
1582258 |
| The Interview Attendance Problem |
385084 |
| The Marvel Universe Social Network |
24891510 |
| The Metropolitan Museum of Art Open Access |
226450420 |
| The Movies Dataset |
943755800 |
| The National Summary of Meats |
64848 |
| The National University of Singapore SMS Corpus |
70570817 |
| The Paleobiology Database |
85549178 |
| The Rise of Bitcoin-The cryptic cryptocurrency |
40131 |
| The Sign Language Analyses (SLAY) Database |
30484 |
| The Simpsons by the Data |
35697943 |
| The Simpsons Characters Data |
616874502 |
| THE small NORB DATASET, V1.0 |
269240584 |
| The Smell of Fear |
98592446 |
| The State of JavaScript, 2016 |
21037211 |
| The Tate Collection |
27352087 |
| The Ultimate Halloween Candy Power Ranking |
5205 |
| The UMass Global English on Twitter Dataset |
1268243 |
| The UN Refugee Agency Speeches |
22450023 |
| The VidTIMIT Audio-Video Dataset |
76338170 |
| The Works of Charles Darwin |
20919838 |
| The Works of Charles Dickens |
25238922 |
| The Zurich Urban Micro Aerial Vehicle Dataset |
401531655 |
| Theano_practice |
17051982 |
| theft vs fire |
1704 |
| Thefts in Cincinnati |
19143733 |
| TheFundamentals - GaussianProcesses |
6713 |
| Theophylline |
3125 |
| thermal_from_vap |
33577266 |
| Things on Reddit |
8369325 |
| third3 |
8074654 |
| third33 |
8072579 |
| This & That |
21393573 |
| This is my first data set |
20563 |
| This is the dataset i used |
5107 |
| thisisatest |
616958 |
| three features to rule camera classification |
185830 |
| Three years of my search history |
610500 |
| ti velos |
104899553 |
| Tianyi's datasets |
468792376 |
| Time Serie Analysis |
34460 |
| time series |
9021 |
| Time to Mold |
275 |
| Time-Series |
611755 |
| TIMIT-corpus |
22253974 |
| TIMIT-corpus |
31932925 |
| tita_test_sv |
47289 |
| titanic |
61194 |
| Titanic |
61194 |
| titanic |
89823 |
| titanic |
89823 |
| Titanic |
93081 |
| Titanic |
93081 |
| Titanic |
93081 |
| Titanic |
44225 |
| titanic |
89823 |
| Titanic |
89823 |
| Titanic |
64970 |
| Titanic |
28629 |
| Titanic |
89823 |
| Titanic |
89823 |
| Titanic |
89823 |
| Titanic |
93081 |
| Titanic |
4388554 |
| Titanic |
89823 |
| Titanic |
93081 |
| Titanic |
89823 |
| titanic |
61194 |
| Titanic |
74491 |
| titanic |
89823 |
| Titanic |
61194 |
| Titanic |
61194 |
| Titanic |
113637 |
| Titanic |
93081 |
| Titanic |
89823 |
| Titanic |
61194 |
| titanic |
6859575 |
| titanic |
89823 |
| titanic |
89823 |
| Titanic |
89823 |
| Titanic |
93081 |
| titanic |
89823 |
| Titanic |
89823 |
| Titanic |
73678 |
| Titanic |
89823 |
| Titanic |
61194 |
| Titanic |
93081 |
| Titanic |
61194 |
| titanic |
2843 |
| titanic |
61194 |
| Titanic |
55456 |
| Titanic |
83879 |
| Titanic |
72130 |
| Titanic |
452657 |
| Titanic |
93081 |
| Titanic |
93081 |
| Titanic |
89823 |
| titanic - training dataset |
61194 |
| Titanic 2 |
89823 |
| Titanic 3 |
89823 |
| Titanic Boats |
104475 |
| Titanic cleansed dataset - ymlai87416 |
228095 |
| Titanic Comp Dataset |
96339 |
| titanic competition data |
93081 |
| Titanic csv |
3258 |
| Titanic Data |
93081 |
| Titanic Data |
89823 |
| Titanic Data |
93081 |
| Titanic Data |
89823 |
| Titanic data |
197556 |
| Titanic Data Set |
93081 |
| Titanic Data Set |
89823 |
| Titanic Data set for classification |
60302 |
| Titanic DataSet |
93081 |
| Titanic Dataset |
89823 |
| Titanic Dataset |
284160 |
| Titanic Dataset |
93081 |
| Titanic Dataset |
89823 |
| Titanic dataset |
89823 |
| Titanic Dataset |
15037 |
| Titanic Dataset Analysis |
61194 |
| Titanic Dataset Feature Engineered |
243198 |
| Titanic DataSet from Kaggle |
89823 |
| Titanic Disaster |
218606 |
| Titanic Disaster |
89823 |
| Titanic Disaster |
3258 |
| Titanic Disaster |
180065 |
| titanic model train data |
61194 |
| Titanic mulheres sobreviventes |
3262 |
| Titanic Output |
2839 |
| Titanic Passenger Nationalities |
29523 |
| Titanic quest |
61194 |
| Titanic Research |
94035 |
| Titanic result |
3258 |
| Titanic Set |
93081 |
| titanic stuff |
93081 |
| Titanic subset |
61194 |
| Titanic Survival Prediction |
93081 |
| Titanic Survival Prediction |
108285 |
| Titanic Survival Prediction_Data |
38821 |
| Titanic Survived Prediction |
11037 |
| Titanic Survivor Prediction |
108268 |
| Titanic Test |
89823 |
| Titanic Test |
28629 |
| Titanic Test Data |
89823 |
| Titanic Test data disaster |
89823 |
| Titanic train |
61192 |
| titanic train |
61194 |
| Titanic Train |
61194 |
| Titanic train |
61194 |
| Titanic Train |
61194 |
| Titanic Train Data |
89823 |
| Titanic Train Dataset |
61194 |
| Titanic train dataset |
61194 |
| Titanic Train_Test Data |
89823 |
| Titanic Training |
89823 |
| Titanic Training Data |
61194 |
| Titanic Training Data |
61194 |
| Titanic Training Dataset |
61194 |
| Titanic Training Dataset |
61194 |
| Titanic_Data |
93079 |
| Titanic_Data_Set |
61194 |
| titanic_data_set_classifications |
61194 |
| Titanic_Dataset |
89823 |
| Titanic_dataset_solved |
89823 |
| titanic_features |
1077 |
| titanic_prediction |
88509 |
| Titanic_Predidction_RandomTree |
5246 |
| Titanic_solved |
93921 |
| Titanic_Survived |
3733 |
| Titanic_test |
93081 |
| titanic_test_set |
61194 |
| titanic_testing_set |
28629 |
| Titanic_train |
93081 |
| titanic_train |
61194 |
| titanic_train |
61194 |
| Titanic_train_sv |
118353 |
| titanic_train_test |
89823 |
| Titanic-competition data |
93081 |
| Titanic-Disaster |
93081 |
| Titanic-sample |
14403 |
| titanic-skakki |
89823 |
| titanic-test |
28629 |
| Titanic: Machine Learning from Disaster |
93081 |
| Titanic: Machine Learning from Disaster |
89823 |
| Titanic:Machine Learning From Disaster |
93081 |
| Titanic1 |
93081 |
| Titanic1 |
28629 |
| Titanic1 |
89823 |
| Titanic1 |
93081 |
| titanic12 |
93081 |
| titanic2 |
89823 |
| Titanic2 |
61194 |
| Titanic2 |
93081 |
| TitanicData |
61194 |
| Titanicdata |
93081 |
| TitanicDataset |
89823 |
| Titanicdataset |
89823 |
| TitanicDataSet |
89823 |
| titanickaamu |
89823 |
| titanicLUL |
7962201 |
| titanicnet |
172674 |
| titanicpred |
89823 |
| Titanicset |
89823 |
| titanictest |
89823 |
| titanictraining |
83879 |
| Titannic Train DataSet |
61194 |
| titantic |
61194 |
| titantrain |
61194 |
| Titatic Test data |
61194 |
| titledd |
2882 |
| titletitle |
8984720 |
| Titrererze |
2334 |
| tmall-test |
383084291 |
| TMDB 5000 Movie Dataset |
45742895 |
| TMDB Old Dataset |
1494688 |
| tmdb_5000_movies |
1659058 |
| tmdb-movies |
6883750 |
| tmdb.csv |
6883750 |
| tmp_img |
855212 |
| tmptmp |
1635900 |
| TMY3 Solar |
1767191353 |
| To_Report_3 |
10109 |
| Tobacco Ban details in USA states |
1054 |
| Tobacco Consumption |
79879 |
| Tobacco Use 1995-2010 |
79879 |
| Tobacco Use and Mortality, 2004-2015 |
432870 |
| tokenizer-sentiment140 |
13371508 |
| Tom Cruise's Love Interest Age Gap |
689296 |
| tom elice week 2 |
29656295 |
| tom_elice_2 |
9895255 |
| tom_elice_v2 |
24550 |
| tom_medium_likes |
1299387 |
| Tööjõukulu 3 kv |
378545 |
| Toolbox Sample |
829593 |
| Tools Testing and Community Prototyping |
210460672 |
| Top 10 Cryptocurrencies |
3764732 |
| Top 100 Canadian Beers |
8019 |
| Top 100 Chess Players Historical |
580565 |
| Top 100 Cryptocurrency Historical Data |
5480160 |
| Top 100 Global Steel Producers (2011-2016) |
6240 |
| TOP 1000 City Betwen Distance Lookup |
32659598 |
| Top 1000 Golf Players Historical |
25532458 |
| Top 23 Users in Kernel Ranking |
82205 |
| Top 500 Indian Cities |
75089 |
| Top 980 Starred Open Source Projects on GitHub |
182752 |
| top datasetsd |
549532 |
| Top How Tos on Google 2004 to 2017 |
2702 |
| Top Movies of 2017 |
17619 |
| Top Ranked English Movies Of This Decade. |
88327 |
| Top Running Times |
1537155 |
| Top Songs (2017) |
7469 |
| Top Spotify Tracks of 2017 |
13149 |
| Top Stared Github Repositories with photos |
568167 |
| Top starred github repo with photos |
233234 |
| Top Trending How Tos on Google |
2591 |
| Top visited Hotels in Europe |
566 |
| top3porto |
53704635 |
| top4porto |
102208534 |
| top6porto |
126430746 |
| topic model |
477907 |
| TopStaredRepositoriesWithPhotos.csv |
233234 |
| torch_14 |
206347 |
| Tornado Losses 2016 |
122291 |
| Toronto Rehab Stroke Pose Dataset |
138950933 |
| Total Exp Data |
2084 |
| Total Expenditure on Health per Capita |
39925 |
| Total_No_Road_Accidents_in_India_2003-2011 |
3498 |
| tototo |
7257537 |
| tototo |
538706 |
| Tourists Visiting Brazil |
34604119 |
| Toxic Armories |
210792 |
| Toxic Comment Classification labelled languages |
16451694 |
| Toxic Comments Classification Challenge |
1368353 |
| Toxic ensemble |
21588381 |
| Toxic Release Inventory |
2165888435 |
| Toxic Words |
3566 |
| toxic-data |
28469071 |
| toxic-xgboost |
14149706 |
| Toy Products on Amazon |
35284814 |
| Toyora |
214993 |
| ToyotaCorolla.csv |
216430 |
| TP1-Datos-2do2017 |
146618147 |
| TP1-orgadatos-properaty |
229910278 |
| TP1OrganizacionDeDatos |
229910278 |
| tp2 deep-L |
16132257 |
| tp2 DL |
16149358 |
| tr_random_sample |
1895395 |
| tr_random_sample |
8495808 |
| tr-random-sample |
8495808 |
| Trabalho Final Data Mining |
7151 |
| TRACK_FINAL |
7262129 |
| Tracking data |
291401597 |
| Traditional Decor Patterns |
50552460 |
| traffic and weather analysis |
368256086 |
| Traffic Signs Pickled Dataset |
123620794 |
| Traffic Violations in USA |
369117541 |
| Traffic_data |
700124 |
| train and submission |
45461746 |
| train and test csv |
287859225 |
| Train and Test Data |
196737128 |
| Train and test data |
1908375 |
| Train and Test for NOMAD |
773107 |
| Train Data |
462137 |
| Train data |
186564 |
| train data |
61194 |
| train data |
50451819 |
| train data |
2452454 |
| train dataset |
111221 |
| train dataset encoded |
1852025376 |
| train favorita |
882526550 |
| train features |
39316210 |
| train file |
93081 |
| train file |
15303 |
| Train Long |
130223820 |
| Train preds |
11069432 |
| Train Short |
5835518 |
| train test 2 |
529123 |
| train test data with id |
16806597 |
| train test set |
63408090 |
| Train w. imfs (+ 4 pr band) |
0 |
| train y |
145522 |
| Train_ |
655053609 |
| train_ |
5835518 |
| train_1002 |
5328417 |
| Train_102 Dataset |
869537 |
| train_2016 |
658793 |
| train_2016_v2.csv |
658793 |
| train_2017 |
165432890 |
| train_2017 |
165432890 |
| train_20m_file |
347422732 |
| Train_A02 |
1397246 |
| Train_A102 |
869537 |
| Train_A102 |
869537 |
| Train_A102 |
869537 |
| Train_A102 |
869537 |
| Train_A102 |
869537 |
| Train_A102 |
869537 |
| Train_A102 |
869537 |
| Train_A102.csv |
869537 |
| train_all.csv |
5274442 |
| train_cat |
9483056 |
| train_churn_av |
87066204 |
| train_churn_pred_av |
87066204 |
| train_clean |
172490347 |
| train_copy |
61194 |
| train_data |
196737128 |
| train_data |
233190 |
| train_data |
134964916 |
| train_data |
1925010 |
| train_data |
31424312 |
| train_data |
2240000486 |
| Train_data |
61194 |
| Train_Data |
3746 |
| train_data_set |
50451819 |
| Train_data_titanic |
61194 |
| train_dataset |
134964916 |
| train_docvecs |
53953980 |
| train_dog |
12037531 |
| train_features.csv |
127014455 |
| train_final |
34444892 |
| train_final_v2 |
49189766 |
| train_ft |
2699051 |
| train_happiness |
35695019 |
| train_idx |
2356032 |
| train_impressions |
174507519 |
| train_improved |
64611109 |
| Train_inde |
162084 |
| train_input |
154565453 |
| train_label |
567521 |
| train_labels_porto |
6102926 |
| train_ll |
12947982 |
| train_mercari |
134964916 |
| train_ml4_her |
18720383 |
| train_plus_actual_test_45 |
34854004 |
| train_plus_test_45 |
34333603 |
| train_plus_test_store_44 |
34782276 |
| train_plus_test_store_44.csv |
34782276 |
| train_plus_test_store_45 |
34333603 |
| train_plus_test_store_45_900_items |
7722588 |
| train_plus_test_store_45_900_items_for_v2 |
7722588 |
| train_plus_test_store_45_900_items_MA |
7722588 |
| train_plus_test_store_45_v3 |
34333603 |
| train_pronto |
301477598 |
| train_rating |
67907638 |
| train_rating |
67907638 |
| train_rd.csv |
518253183 |
| train_sample1 |
17370768 |
| train_seguro |
129470917 |
| train_set |
1337934 |
| train_set |
134964916 |
| Train_spooky_author |
1345931 |
| train_tatanic |
61194 |
| train_test |
84912792 |
| Train_Time_Series |
474092593 |
| train_titanic |
61194 |
| train_titanic |
64970 |
| train_titanic |
61194 |
| train_titanic |
61194 |
| train_tr.csv |
730899294 |
| Train_UWu5bXk |
869537 |
| train_v2 |
344602443 |
| train_v3 |
36696895 |
| train_va.csv |
240776623 |
| train_vec |
972440972 |
| Train_with_imfs |
494074849 |
| train_with_shift |
7136838 |
| train_wo_usr_logs |
51166672 |
| Train_xgb |
12357214 |
| Train_xgb1 |
8652358 |
| Train-1 |
14450945 |
| train-Nationality |
19995 |
| train-test-spooky |
1908375 |
| train. |
95403458 |
| train.csv |
9605983 |
| Train.csv |
61194 |
| train.csv |
243998 |
| Train.csv |
869537 |
| train.csv |
9605983 |
| train.csv |
40154897 |
| train.csv |
61194 |
| Train.csv |
5835518 |
| train.csv |
31424312 |
| train.csv |
61194 |
| train.csv |
127433651 |
| train.csv |
61194 |
| train.csv |
4646885 |
| train.csv |
116447757 |
| Train.CSV |
61194 |
| train.json |
61145796 |
| train.json |
61145796 |
| train.tsv |
134964916 |
| train.tsv |
134964916 |
| train.tsv |
134964916 |
| train.tsv |
134964916 |
| train.tsv |
134964916 |
| train.tsv |
134964916 |
| train.tsv |
134964916 |
| train/test data |
89823 |
| train1 |
14846 |
| train1 |
10385 |
| train1 |
9605983 |
| train1 |
10384 |
| train1 |
38013 |
| train1 |
78025130 |
| Train1 |
33112364 |
| train1 |
684377114 |
| TRAIN1 |
115852544 |
| train123 |
869537 |
| Train2 |
31424326 |
| train2 |
61213 |
| train2016 |
658793 |
| TrainA102 |
869537 |
| TrainA102 |
869537 |
| trainComplete |
4933792 |
| traincsv |
0 |
| traincsv |
61194 |
| traincsv |
5835518 |
| Traincsv |
89823 |
| traindata |
39390416 |
| trainData |
14779543 |
| traindata |
87748274 |
| traindata |
78025130 |
| trainData |
5835518 |
| traindata |
232157480 |
| traindata |
1640565 |
| traindata1 |
237221822 |
| traindatacsv |
5512037 |
| traindataset |
7136838 |
| traindataset |
8430411 |
| traindataset |
339114624 |
| trainDogs |
1923066 |
| trainDown |
54189852 |
| Trained Coarse Classifier |
2308408118 |
| trained weights1 |
7603 |
| trained_model |
23108761 |
| trained_weights |
7603 |
| trained_wights |
1513568 |
| TrainedModel |
1513568 |
| traines |
5744144 |
| trainfeature |
597345624 |
| trainfeture |
598624202 |
| trainFile |
38013 |
| Trainhousingprice |
460676 |
| training |
462137 |
| Training |
161814 |
| training |
61194 |
| training |
61194 |
| Training |
286822 |
| training |
29667578 |
| Training |
460676 |
| Training |
17931350 |
| Training |
270147 |
| training data |
1065208 |
| Training Data |
61145796 |
| Training data |
322600 |
| Training data w imfs |
0 |
| training dataset |
10129522 |
| Training set |
869537 |
| training set |
187901347 |
| Training set |
50451819 |
| Training set w. imfs |
1162170891 |
| training unpacked |
314572800 |
| training_dataset |
1482862019 |
| training_mercari |
196737128 |
| Training_Peurto_Seurgo |
108304724 |
| training_set |
127940663 |
| training-data |
103021648 |
| training-nul |
122030495 |
| training123 |
62042190 |
| training2.csv |
12062119 |
| trainingcs |
2791501 |
| TrainingData |
7564965 |
| trainingdata |
4959025 |
| TrainingGooglePrices |
63488 |
| TrainingInstitute |
13055 |
| trainingSet |
61194 |
| TrainKer |
33112364 |
| trainmercari |
196737128 |
| TrainModel |
44652 |
| trainnpl |
16417158 |
| trainonly |
61194 |
| TRAINS |
4720668 |
| trainset |
94707453 |
| TrainSet |
78025130 |
| trainset |
30267050 |
| Trainset |
10307653 |
| Trainset10 |
64097906 |
| traintsv1 |
134964916 |
| trainv2santander |
6051565 |
| trainwimfs |
494074849 |
| trainWithImfs |
494074849 |
| transaction_version2 |
55948875 |
| Transactions |
769 |
| transactions |
742 |
| transactions_v2 |
55948875 |
| transactions.csv |
809 |
| Transcriptomics in yeast |
10627575 |
| Transportation Statistics Lookup Tables |
604818 |
| Transposed |
3775759 |
| Trappist-1 Solar System |
4248 |
| Traveling salesman |
317611 |
| travelling saleman |
317611 |
| Travels of Noah |
3851 |
| travis.df |
78121432 |
| Tree Census in New York City |
498005673 |
| Trending YouTube Video Statistics (UPDATED) |
35087677 |
| Trending YouTube Video Statistics and Comments |
155872090 |
| TRI Statistics |
37861194 |
| trial 1 |
3265 |
| Trial A |
32768 |
| Trial and Terror |
498235 |
| Trial Data Emoji |
1820661 |
| Trial Dataset |
404381 |
| trial2 |
24906288 |
| Trial22222 |
7976616 |
| TrialjswData |
13582 |
| trrandomsample |
8495808 |
| Truck Breadcrumb information |
5104992 |
| Trump Administration Financial Disclosures |
3294482 |
| Trump Approval Rating by Party |
658 |
| Trump Financial Disclosure 2016 |
201994 |
| Trump Financial Disclosure 2017 |
128403 |
| Trump vs Clinton 1 |
129755 |
| Trump's Taiwan Call |
129090 |
| Trump's World |
406891 |
| Trumps Lie |
64707 |
| try_santa_02 |
4045088 |
| try-07 |
4045139 |
| try-ohoho12 |
4045075 |
| try-ohoho13 |
4045073 |
| try-ohoho14 |
4045077 |
| try-santa-03 |
4045064 |
| try-santa-04 |
4045123 |
| try-santa-05 |
4045051 |
| try-santa-06 |
4045114 |
| trythis |
103374 |
| TS_prediction_features |
4243065 |
| TS_preds_fbprophet |
701220 |
| TSA Claims Database |
35237765 |
| TSA_wj |
626550 |
| TShirts |
2393867 |
| Tsunami Causes and Waves |
2890816 |
| Tsunamis History |
540181 |
| Ttile254352 |
70464 |
| tttlll |
172006681 |
| tttttt |
115852544 |
| tttttt |
16601846 |
| Tugas 5 Dasken 2017 |
121165346 |
| Tugas Digit Recognition Dasken 2017 |
109582946 |
| TUM_VO_Dataset1 |
410391900 |
| Tumblr GIF Description Dataset |
28415963 |
| Tunisia 2020 Projects |
326517 |
| Turing Test |
35177 |
| turkey-shp |
4325143 |
| Turkey's mobile banking user commentary analysis |
1125 |
| Turkey's Political Kronology |
66302 |
| Turkish sentences for word2vec training |
56735350 |
| Tusbic Santander |
1919459 |
| tutorial |
1908375 |
| Tutorial |
2843 |
| TV Sales Forecasting |
825190 |
| tweet_mask |
2428 |
| tweets |
25663131 |
| Tweets . |
113381 |
| Tweets Blogs News - Swiftkey Dataset 4million |
574661177 |
| Tweets data |
113381 |
| Tweets Dataset |
419574 |
| Tweets from MG/BR |
401120 |
| Tweets from People Followed By Indian PM Modi |
31451955 |
| Tweets Targeting Isis |
29978624 |
| Twits DS Platzi |
44028 |
| Twitter feed1 |
1134950 |
| Twitter Friends |
448476867 |
| Twitter Italian Dialect Data |
16212242 |
| Twitter Sample |
122350791 |
| Twitter Sentiment Analysis |
3421431 |
| Twitter Test Feed |
1134950 |
| Twitter Text and Gender |
14 |
| Twitter trends/tweet for October 2017 |
454240652 |
| Twitter US Airline Sentiment |
8459511 |
| Twitter User Gender Classification |
8176739 |
| Twitter vs. Newsletter Impact |
3037 |
| twitter_senti |
1214458 |
| twitter_sentiment |
59096518 |
| twitter3 |
1236455 |
| TwitterTest |
59096510 |
| two demoisaic tot 1373 feats |
1292808 |
| Two features on Camera Model Identification |
138421 |
| Two Sigma |
21027219 |
| txt files 1287 |
328136 |
| Type Allocation Code (TAC) |
5966210 |
| u data |
872574 |
| U.S. Charities and Non-profits |
288082208 |
| U.S. College Scorecard Data 1996-2015 |
203090705 |
| U.S. Commercial Aviation Industry Metrics |
1818640 |
| U.S. Educational Attainment [1995-2015] |
1102668 |
| U.S. Educational Finances |
84734393 |
| U.S. Federal Superfund Sites |
321300994 |
| U.S. Healthcare Data |
39941119 |
| U.S. Homicide Reports, 1980-2014 |
114173543 |
| U.S. Incomes by Occupation and Gender |
31336 |
| U.S. Major League Soccer Salaries |
207020 |
| U.S. News and World Report s College Data |
78070 |
| U.S. Opiate Prescriptions/Overdoses |
14408151 |
| U.S. Pollution Data |
400946718 |
| U.S. Public Pensions Data, fiscal years 2001-2016 |
2018611 |
| U.S. Technology Jobs on Dice.com |
61312870 |
| u.user |
22667 |
| UB data |
42802 |
| UB trip data |
42241 |
| UBER Drives |
86369 |
| Uber NYC Trips for 2016 |
122778 |
| Uber Pickups in New York City |
875036990 |
| Uber Request Data |
395061 |
| Uber Ride Reviews Dataset |
590621 |
| Ubudehe Livestock 1 |
10301108 |
| Ubuntu Dialogue Corpus |
2912261156 |
| UCDP Georeferenced Event Dataset |
70189068 |
| UCI Appliances energy prediction Data Set |
1794429 |
| UCI Cardiotocography |
1743872 |
| UCI Communities and Crime Unnormalized Data Set |
665979 |
| UCI Daily and Sports Activities |
170800010 |
| UCI ML Air Quality Dataset |
795973 |
| UCI ML Datasets |
35644 |
| UCI Turkiye Student Evaluation Data Set |
391968 |
| UCL Wine |
10782 |
| Udacity Titanic Data |
61194 |
| Udacity_AB_Testing_FinalProject_Data |
1125 |
| UdacityBrazilMedicalAppointments |
10739535 |
| UDAS loan information |
73215 |
| udemu data analysis jp case1 |
18267 |
| udemy data analytics case2 sec 6_2 |
910894 |
| udemy data analytics jp |
18267 |
| udemy data analytics jp case1 |
17721 |
| udemy data analytics jp case2 sec 6 |
705004 |
| udemy data analytics jp section 5_3 |
18267 |
| UDHR Corpus |
8939497 |
| UFC Fight Data |
2496234 |
| UFC Fight Data Refactored |
498896 |
| UFC Fights Data 1993 - 2/23/2016 |
2311143 |
| UFC PPV Sales |
6243 |
| UFO dont care |
5298425 |
| UFO leiud |
5277693 |
| UFO nähtused |
5105776 |
| UFO on päris |
5298772 |
| UFO sightings |
5105776 |
| UFO Sightings |
29278853 |
| UFO Sightings around the world |
13710798 |
| ufo sightseeing |
5636539 |
| ufo_reports |
668882 |
| ufo-sightings |
10712628 |
| ufosights |
5027080 |
| UjiIndoorLoc: An indoor localization dataset |
45107330 |
| UK 2016 Road Safety Data |
65756638 |
| UK Car Accidents 2005-2015 |
559530574 |
| UK Constituency Results |
107975 |
| UK fleet and foreign fleet landings by port |
84776078 |
| UK Government Wine Cellar Reports |
688300 |
| UK Housing Prices Paid |
2405685902 |
| UK Land Registry Transactions |
33230708 |
| UK road safety data |
514507530 |
| UK Traffic Counts |
1035851129 |
| UKDALE House 5 TV Usage |
4913602 |
| Ukrainian Parliament Daily Agenda Results |
266768943 |
| Ulaanbaatar zamnal data |
43062 |
| Ultimate 25k+ Matches Football Database -European |
313090048 |
| Ultimate Beastmaster: First Season |
29539 |
| UN data |
3487 |
| UN Gender Data |
357432 |
| UN General Assembly Votes, 1946-2015 |
37610910 |
| UN General Debates |
135121723 |
| UN HDI dataset |
11570 |
| UN Health Data |
102402 |
| Unated Nations Population median age |
16464 |
| unbalanced_clf |
10850066 |
| UnbalancedRiskDataset |
414216 |
| Uncompressed data |
196737128 |
| under dev |
25277993 |
| Under_Sampled_Exoplanet_FATS |
94802 |
| Undertale Music |
428931 |
| UNHCR Refugee Data |
41422282 |
| Unicode 10.0 Character Database in JSON |
30964929 |
| Unicode Samples |
643 |
| Unifesp AM Classes |
32318 |
| Uniform Gift Filling |
4053353 |
| Unilever 2000 2017 |
199278 |
| Unimorph |
1278027080 |
| Union Membership & Coverage |
450864 |
| uniparc_xref2ncbi_taxonomy_id |
1870487534 |
| Uniqlo (FastRetailing) Stock Price Prediction |
67962 |
| UniqueVehicleMakeModels |
5981 |
| United Nations General Debate Corpus |
135159808 |
| united nations world populations |
6786908 |
| United States Code |
619870742 |
| United States Commutes |
291821012 |
| United States crime rates by county |
345013 |
| United States Droughts by County |
263437911 |
| United States Energy, Census, and GDP 2010-2014 |
75526 |
| United States State Shapes |
23358977 |
| United States Trademark Applications |
219838100 |
| Universal Product Code Database |
62604775 |
| Universal Tagset |
37147 |
| Universal Treebank |
119113962 |
| University of Waterloo Student Demographics |
593234 |
| University programs information of unis in Lahore |
83337 |
| University Rankings |
186384 |
| Unix Words |
2498552 |
| unknown |
137160 |
| Unkown Data |
45278227 |
| Unofficial Holidays |
19245 |
| unsafe urls |
245079491 |
| unsample |
409326773 |
| unsample_2 |
409110512 |
| unstemed |
1427 |
| unzip_tsv |
196737128 |
| Unzipped |
2 |
| unzipped data |
514507530 |
| Unzipped file |
4933794 |
| Unzipped Oil csv |
20580 |
| unzipped-raw |
78025130 |
| UPC_creditcard_default-payment |
2862995 |
| update dataset |
28879944 |
| updated |
93723293 |
| updatedata |
90249030 |
| upload |
7463819 |
| upload_pred |
16659614 |
| UploadedData |
2161900092 |
| uploadtest |
15570 |
| Urban and Rural Photos |
3272392 |
| Urban Dictionary Terms |
1099750 |
| Urban Dictionary Words And Definitions |
249154499 |
| urban land cover/testing |
412022 |
| Urdu Speech Dataset |
41464533 |
| Urdu Stopwords List |
11674 |
| Urdu-Nepali Parallel Corpus |
6285620 |
| URL Database |
255803 |
| US ACS Financial Hedging Features |
5398411 |
| US Adult Income |
5977458 |
| US Baby Names |
181758519 |
| US campsites |
8831764 |
| US Candy Production by Month |
10740 |
| US Casualties of the Korean War |
7085940 |
| US Casualties of the Vietnam War |
25328211 |
| US Census Bureau County Data 1980-1990 |
881066 |
| US Census Demographic Data |
5903488 |
| US Census Population Data (County Level) 1970-2014 |
2518181 |
| US Chronic Disease Indicators |
122899180 |
| US College Sailing Results |
86621612 |
| US Consumer Finance Complaints |
378460129 |
| US County Info with smoking ban |
3984620 |
| US County Premature Mortality Rate |
361394 |
| US county-level mortality |
25052207 |
| US Demographics |
623 |
| US Dept of Education: College Scorecard |
4201314992 |
| US DOT Large Truck and Bus Crash Data |
4220 |
| US Energy Statistics |
955567524 |
| US Facility-Level Air Pollution (2010-2014) |
6462128 |
| US Flight Delay |
412132322 |
| US Gross Rent ACS Statistics |
5724068 |
| US Household Income Statistics |
5857882 |
| US Input-Output Tables |
1624770 |
| US jobs on Monster.com |
68388810 |
| US Mass Shootings |
138231 |
| US Mass Shootings |
695203 |
| US Mass Shootings Dataset v2 Clean |
138506 |
| US Mass Shootings NaN coordinates fixed |
153656 |
| US Metropolitan population density 2016 |
24928 |
| US Permanent Visa Applications |
298624850 |
| US Population By Zip Code |
117755673 |
| US President Campaign Spending |
3200 |
| US Presidential Elections 10 States Comparison |
1003 |
| US PRESIDENTS |
6179 |
| US Presidents heights: How low can u go? |
1008 |
| US regions |
1779 |
| US Representation by Zip Code |
239200351 |
| US state county name & codes |
127459 |
| US States - Cartographic Boundary Shapefiles |
8506126 |
| US Stocks Fundamentals (XBRL) |
151728124 |
| US Supreme Court Cases, 1946-2016 |
2642816 |
| US Tariff Rates |
9083109 |
| US tourism |
208 |
| US Trademark Case Files, 1870-2016 |
4440647001 |
| US Traffic Violations - Montgomery County Police |
367888303 |
| US Traffic, 2015 |
467378913 |
| US Unemployment Rate by County, 1990-2016 |
80447850 |
| US Veteran Suicides |
59031 |
| US zip codes with lat and long |
928044 |
| US_Industrial_Prodcution |
11086 |
| US-based job data set from 300+ companies |
73499355 |
| US-based Jobs from Dice.com |
17302872 |
| us-states |
87739 |
| USA HOUSE PRICES |
726209 |
| USA Housing |
726209 |
| USA Housing dataset |
915002 |
| USA Income Tax Data by ZIP Code, 2014 |
167426476 |
| USA lat,long for state abbreviations |
1885 |
| USA Map Shape |
2360011 |
| USA PollingData |
4081 |
| USA Unemployment Rate from 1989 to 2017 |
1618 |
| USA_Housing |
726209 |
| USA_Housing.csv |
726209 |
| Usable Oil Prices: Simple Price Imputation |
142793 |
| usagov |
1833108 |
| uscrimes |
5107 |
| USD Vs INR in past 10 year |
293 |
| USDA plant database |
2282563 |
| USDA PLANTS Checklist |
6780700 |
| useBySELF |
3087508 |
| Used car offers |
33157763 |
| Used cars database |
68541275 |
| Used CARS retailer in US database |
1288198 |
| User Information |
119535551 |
| User Ratings for Movies |
3989514 |
| user_logs_filtrados |
119767170 |
| userdata |
1529669 |
| userlog file |
435478250 |
| userlogs |
18795855 |
| Users data |
991 |
| Users mobile banking transaction frequency |
317818 |
| USHousingData_PricePrediction |
2337942 |
| USP_lie_to_me |
888391 |
| Uttar Pradesh Assembly Elections 2017 |
298535 |
| uuiuvc |
460 |
| UZ_vagon |
7590534 |
| v2 Church Inventory |
18678 |
| v2aug24 |
4614238 |
| V2PizzaData |
318851 |
| v81 Mercari restored brand names |
17415182 |
| Vacation rental properties in Palm Springs, CA |
21565117 |
| Vader Lexicon |
434147 |
| validdatacv5 |
4871570 |
| variability in the poverty rate in the US counties |
3889674 |
| VAT thermal |
5 |
| Vatalu |
8782 |
| Vectorized Handwritten Digits |
105298 |
| Vegetarian & Vegan Restaurants |
82666666 |
| Vehicle and Tire Recalls, 1967-Present |
19594636 |
| Vehicle Collisions in NYC, 2015-Present |
89553269 |
| Vehicle Fuel Economy |
18001687 |
| Vehicle Fuel Economy Estimates, 1984-2017 |
11731914 |
| Vehicle Movements Datasets |
14256788 |
| Vehicles - Nepal |
24607221 |
| Vehicles Colombia (Fasecolda) |
3446067 |
| Venues in New York City |
1781262 |
| VerbNet |
2474526 |
| Version_2 |
340861583 |
| version23 |
3074296 |
| very small test data |
10 |
| Vgg__19 |
7377 |
| vgg_bestmodel |
184459 |
| VGG-11 |
493415465 |
| VGG-11 with batch normalization |
493733956 |
| VGG-13 |
494116563 |
| VGG-13 with batch normalization |
494374395 |
| VGG-16 |
513596671 |
| VGG-16 |
568494657 |
| VGG-16 weights |
54730390 |
| VGG-16 with batch normalization |
514090234 |
| VGG-19 |
533106572 |
| VGG-19 |
608022600 |
| VGG-19 with batch normalization |
534106491 |
| VGG16 imagenet model |
58889256 |
| VGG16_Keras |
58889256 |
| VGG16_npy |
514146600 |
| vgg16_weights |
54730390 |
| vgg16_weights |
58889256 |
| vgg16_weights_tf |
58889256 |
| vgg166finetune |
59377447 |
| vgg16wgts |
54730390 |
| vgsaleeeeee |
357308 |
| vgsales |
364519 |
| vgsales.csv |
391429 |
| vgsales2.csv |
390187 |
| victoire |
7535648 |
| victor_kagglemix |
10086186 |
| Video Game Sales |
1355781 |
| Video Game Sales and Ratings |
1515957 |
| Video Game Sales with Ratings |
1618040 |
| Video_Games_Sales_as_at_22_Dec_2016.csv |
1618040 |
| VideoGameSales |
390246 |
| Vietnam War Bombing Operations |
1625116690 |
| Viewing Solar Flares |
1595094 |
| vijaytta |
89823 |
| Vincent van Gogh's paintings |
122355 |
| vinos y crimenes |
1349 |
| virginie_do |
2387182 |
| Virtual Reality Driving Simulator Dataset |
29739136 |
| virugadde |
133490 |
| Visa Free Travel by Citizenship, 2016 |
4699 |
| Visitors data of facebook movie fan group kinofan |
1249917 |
| visualisation.py |
2287 |
| visualization |
16400 |
| Visualization_Tests |
282 |
| visualizations |
201851 |
| VIX2017 |
9189 |
| vizualizations |
201851 |
| vocabfile |
1716265 |
| vocimages |
1294820 |
| voice and speech |
415116 |
| Voice Data Recognition |
415116 |
| Voice Recognition |
746155 |
| voice_gender_prediction |
415116 |
| Volcanic Eruptions in the Holocene Period |
259712 |
| voting data of congressmen for various bills(1980) |
19818 |
| VOTP Dataset |
296543330 |
| Vowpal Wabbit tutorial |
93691305 |
| VR games list |
20455 |
| Vselection |
270 |
| VXXData |
4620 |
| w245ertrgdfgewrt |
1635878 |
| w2v_tutorial_data |
27649993 |
| w2v_tutorial_data_labelled |
13788274 |
| Wage Estimates |
62865165 |
| Wages Dataset |
62250 |
| wallmart sales forecast datasets |
3937026 |
| walmart |
4210412 |
| Walmart Data |
869537 |
| Walmart Sales |
1397246 |
| Walmart_files |
4210412 |
| War_and_Peace |
610926 |
| Wars by death tolls |
9328 |
| warwar |
610926 |
| Water Conservation Supplier Compliance |
42080 |
| Water Consumption in a Median Size City |
47122790 |
| Water Levels in Venezia, Italia |
12621914 |
| water pump |
26233863 |
| waterimage |
942965 |
| WDPA_Nov2017 |
9705377 |
| wearable motion sensors |
427319 |
| Weather |
7927 |
| Weather |
262144000 |
| WEATHER ANALYSIS |
79935 |
| Weather Conditions in World War Two |
11246918 |
| Weather Data - Boston (Jul 2012 - Aug 2015) |
30649 |
| Weather Data for Recruit Restaurant Competition |
11851553 |
| Weather data in New York City - 2016 |
11147 |
| Weather Dataset |
2342515 |
| Weather DataSet for RV Challenge |
181835 |
| Weather dataset from the R rattle package |
4140278 |
| Weather datasets |
533144 |
| Weather in Szeged 2006-2016 |
16294377 |
| Weather Madrid 1997 - 2015 |
528914 |
| Weather Modified |
226234 |
| Weather on terrorism |
37725880 |
| Weather Undergroud |
7927 |
| Weather Underground |
7927 |
| weather_data_perMinute |
27425604 |
| weather-data |
171621 |
| weather.csv |
29462 |
| Web crawler for real estate market |
349166 |
| Web Text Corpus |
1726918 |
| Web Traffic Time Series Forecasting |
40689209 |
| Web visitor interests |
3123645 |
| Webster 2009 |
57936 |
| weekly |
28890 |
| Weekly Corn Price |
20178 |
| Weekly Dairy Product Prices |
357629 |
| Weekly Gold Close Price 2015-2017 |
8134 |
| Weekly Return Data of Nifty, Gold and Oil |
36987 |
| Weekly Sales Transactions |
317399 |
| weekly2 |
36895 |
| WEFEFFFFFFFFFFFFFS |
187487694 |
| wefwefwefwefwef |
2311143 |
| weightavg |
1251231 |
| Weights |
175313142 |
| weights |
54730390 |
| Weights |
28367863 |
| Weights |
79592 |
| weights |
64553152 |
| weights |
1888776 |
| weights |
7603 |
| Weights for ResNet50 for this competition |
274819726 |
| weights2e |
22857356 |
| Weigths |
79592 |
| Weizmann HAR |
335582657 |
| Weka German Credit |
139018 |
| Welfare Error Rates |
4261 |
| wh_ensemble |
2435093 |
| wh_ensemble11 |
2435093 |
| wh_ensemble12 |
1940007 |
| whaledetection-ng |
721212 |
| whaledetection-rg |
720686 |
| What is a note? |
14698728 |
| What people purchase |
203510 |
| What.CD Hip Hop |
10727424 |
| What's On The Menu? |
152680773 |
| whatsappchat |
5006 |
| WhatsAppGroupChat |
5006 |
| When do children learn words? |
78657 |
| Where it Pays to Attend College |
74222 |
| Where's Waldo |
131409856 |
| White House Salaries |
397813 |
| whitebelt |
708633 |
| WHO data |
16400 |
| WHO dataset just for fun |
16400 |
| Who Dies? Physics Puzzle Dataset |
382271757 |
| Who eats the food we grow? |
894612 |
| WHO Insufficiently active |
25167 |
| Who starts and who debunks rumors |
9619509 |
| Who's the Boss? People with Significant Control |
3474121431 |
| Whole Foods |
64214 |
| whycannotupdate |
6298003 |
| Wiki Words |
949663 |
| WIKI_OUT |
17083327 |
| wiki-mitx |
435355 |
| wiki-news-300d-1M.vec |
689870086 |
| Wikidata Property Ranking |
393784 |
| Wikipedia |
435355 |
| Wikipedia Article Titles |
310654639 |
| Wikipedia country population, currencies |
22747 |
| Wikipedia Edits |
121089 |
| wikitext-2 |
4783336 |
| wild-fire |
8370931 |
| winapps-challenge |
427291 |
| Wind data |
13509 |
| Wind Farms |
12911849 |
| Wind Predictions |
138090 |
| wind_data5 |
12584 |
| winddata |
539150 |
| Wine Dataset |
1170 |
| Wine dataset - Unsupervised Algorithms |
10782 |
| Wine Industry |
1440098 |
| Wine Quality |
391892 |
| wine quality selection |
355068 |
| Wine Reviews |
53336217 |
| wine_data.csv |
10958 |
| Winedataqualitypractice |
231073 |
| WineDataset |
11304 |
| WineDataset |
1040 |
| WineDatasetHeaders |
11480 |
| Winemagazine_IE_students |
17330448 |
| winequality-red |
84199 |
| winequality-red |
84199 |
| winequality-red |
84199 |
| wines_properties |
11462 |
| WIP DATASET TBC |
7980 |
| wiscbcdata |
125093 |
| Wisconsin Breast Cancer Database |
20057 |
| With coordinates |
3680819 |
| withalldummies |
7233614 |
| withdtiratio |
4080912 |
| without dummy |
4187788 |
| withratio |
6071712 |
| WL-DubDub |
11 |
| WMO Hurricane Survival Dataset |
974161 |
| WMT15 Evaluation |
1247631 |
| WNBA Player stats Season 2016-2017 |
20793 |
| Woebot Responses |
43798 |
| Women and Child Model |
3589 |
| Women Shoes |
7441975 |
| Women's Shoe Prices |
107048266 |
| Women's Tennis Association Matches |
9277027 |
| Wonderland |
3325833 |
| Woodbine Horse Racing Results |
1392158 |
| Word clouds |
103808 |
| Word Hypernyms |
31575 |
| Word in French |
25851677 |
| Word Occurrences in Movies |
1388438 |
| Word Occurrences in Mr. Robot |
322208 |
| Word Occurrences in Shakespeare |
404735 |
| word vector |
39746350 |
| word vector 2 |
31407499 |
| Word vector test questions |
603955 |
| word_cloud_picture |
30788 |
| word-cloud-picture |
30788 |
| Word2Vec |
14971180 |
| Word2Vec |
135847466 |
| Word2vec |
67281491 |
| Word2Vec Google |
1760925994 |
| word2vec model |
1760925994 |
| Word2Vec Sample |
138432415 |
| Word2Vec tutorial - Suite |
133845411 |
| word2vec_Google |
1760925994 |
| word2vec_model |
1760925994 |
| wordbatch |
2243245 |
| wordbatch |
2243245 |
| wordbatch |
603397 |
| wordbatch |
2243245 |
| wordbatch |
2254833 |
| wordbatch |
2243245 |
| wordbatch |
2254833 |
| wordbatch |
2243245 |
| WordBatch |
2246473 |
| wordbatch |
2243245 |
| wordcloud |
257461 |
| Wordgame |
8630880 |
| WordNet |
70574350 |
| words_recognition |
3411890296 |
| workbatch |
2243245 |
| workbook1 |
21274 |
| Workers Browser Activity in CrowdFlower Tasks |
10759715 |
| Working rate |
922 |
| working with code quality metrics |
93723293 |
| working_blend |
7976972 |
| working_blend_2 |
7976236 |
| working_blend_3 |
7977044 |
| World Atlas of Language Structures |
13493201 |
| World Bank : World Development Index |
44701 |
| World Bank Youth Unemployment Rates |
17145 |
| World Bank:World Develpment Index DataSet |
44701 |
| World Bank's Major Contracts |
53817900 |
| World Cities |
872568 |
| World Cities Database |
164327399 |
| World Cities Population and Location |
5793670 |
| World Color Survey |
12129809 |
| World Continent-Country Codes |
5224 |
| World Countries and Continents Details |
48076 |
| World Countrywise Population Data 1980 - 2010 |
59344 |
| World Demographics |
634880 |
| World Development Indicators |
245856498 |
| World Development Indicators |
2042307823 |
| World Factbook Country Profiles |
6973424 |
| World Flags |
254068 |
| World Gender Statistics |
80188494 |
| World Glacier Inventory |
17249601 |
| World happiness |
17132 |
| World Happiness Analysis |
29530 |
| World Happiness Excercise |
7196 |
| world happiness report |
22743 |
| World Happiness Report |
63225 |
| World Language Family Map |
207749813 |
| World of Warcraft Avatar History |
643669597 |
| World of Warcraft Demographics |
13622 |
| World Population |
134321 |
| World Population |
1344962 |
| World Population |
287706 |
| World Population Historical (Predictive) |
6584 |
| World Population Predictions |
707668 |
| World Soccer - archive of soccer results and odds |
14620926 |
| World Tennis Odds Database |
51572422 |
| World university rankings |
186384 |
| World University Rankings |
11999397 |
| World War 2 Weather Dataset |
21648 |
| World´s largest economies |
724 |
| world_countries |
252692 |
| World_Happiness Report_2017 |
29536 |
| World_Happiness_Madhavi |
7196 |
| world-cities |
872568 |
| world-countries |
252515 |
| world-countries.json |
252515 |
| World's Highest Mountains |
13009 |
| worldcountries |
252504 |
| Worldnews on Reddit from 2008 to Today |
82161571 |
| WorldPopulation |
134321 |
| Worldwide Economic Remittances |
515524 |
| WOW air tours as of 2018 |
5867516 |
| wrod2vec_twitter_50d |
214231913 |
| wrod2vec-twitter-25d |
112330277 |
| wsdm data |
235891409 |
| wsdm lgbm |
246043422 |
| wsdm test |
608805921 |
| WSDM_KKBOX |
737403492 |
| WSDM-Music |
740949351 |
| WTA Matches and Rankings |
20720193 |
| wu-ensemble11 |
2531038 |
| WUZZUF Job Posts (2014-2016) |
137386426 |
| WWI Bombing Operations |
1422071 |
| wwrtrgfnvbhgv |
28627 |
| wwwwww |
4072076 |
| wwwwww |
2245108 |
| wwwwwwwww |
14291742 |
| Wyckford Basic |
4865 |
| x_train |
54225842 |
| x_val_re |
8981920 |
| x-test |
8952997 |
| Xception |
162488266 |
| XG_Contour |
247223 |
| XGB Submit |
14511189 |
| xgb_30011 |
662096 |
| xgb_lgb_best |
5898826 |
| xgb_model |
342531 |
| xgb_submission |
6273722 |
| xgb_submission |
13618373 |
| xgb_submit |
6273722 |
| xgb_submit |
14511189 |
| xgb_support_CFav |
16960582 |
| xgb_valid_preds_public |
3398480 |
| xgb.fmap |
356 |
| xgbname |
7519435 |
| xgbname2 |
5739462 |
| xgboost_yisu |
7493399 |
| xgboost-practice |
4304184 |
| xgboost1 |
1584533 |
| XGboostCVLB284 |
11028 |
| xgbost32 |
7291826 |
| XGBPlus |
9834641 |
| xiangku |
44234355 |
| xinjiang(Predictive Maintenance) |
88790835 |
| XOM_txt |
4586 |
| XOM_Txt2 |
4857 |
| XOMData |
4857 |
| XRP and BTC |
1108006 |
| <"xss'asdasd |
8 |
| Xtrain |
70000519 |
| xxtestonq |
763156 |
| XXX Housing Data |
18853 |
| XXXPropertyData |
4844 |
| Y Combinator Companies |
125369 |
| Y prédictions |
892434 |
| y_train |
16683740 |
| y_val_re |
2776283 |
| YCOE Corpus |
277 |
| Year vs Number of emails - Enron Emails |
9697 |
| Years of experience and Salary dataset |
454 |
| Yellow Pages of Pakistan |
8970140 |
| yelp data for natural language processing |
3656621 |
| Yelp Reviews |
68806 |
| Yelp Reviews 1000 |
759135 |
| yelp_review |
3656621 |
| Yelp-100000-reviews |
30821961 |
| yelp-review-tail-1000 |
773722 |
| yolo_model |
189265019 |
| Young People Survey |
458740 |
| YouTube Comedy Slam |
33607350 |
| YouTube Faces With Facial Keypoints |
10510217344 |
| Youtube SPAM CLASSIFIED-COMMENTS |
341738 |
| Yucata Season 1 Raw Data |
37024 |
| yytutu |
15991536 |
| Zapatos |
7441975 |
| zetasantagiftscore |
4042518 |
| zhaibowen_1 |
7265181 |
| zhaibowen_10 |
7276229 |
| zhaibowen_11 |
7272112 |
| zhaibowen_11 |
7276369 |
| zhaibowen_1229_1 |
7270411 |
| zhaibowen_171228_1 |
7266182 |
| zhaibowen_171231_1 |
7278103 |
| zhaibowen_180107_4 |
7266292 |
| zhaibowen_180108_1 |
7977768 |
| zhaibowen_180108_2 |
7979616 |
| zhaibowen_180112 |
7981135 |
| zhaibowen_180116 |
7981289 |
| zhaibowen_2 |
7277005 |
| zhaibowen_3 |
7277005 |
| zhaibowen_4 |
7279277 |
| zhaibowen_5 |
7279622 |
| zhaibowen_6 |
7277005 |
| zhaibowen_7 |
7276252 |
| zhaibowen_8 |
7275371 |
| zhaibowen_9 |
7273349 |
| Zika Virus Epidemic |
11662539 |
| zillow |
18652182 |
| zillow |
35494318 |
| Zillow Economics Data |
527680809 |
| Zillow Rent Index, 2010-Present |
10725975 |
| zillowzestimate_original_IMPUTED_BY_JB_2.4.csv |
35494318 |
| Zip Codes and Stats |
945774 |
| ZIPfiles |
554536109 |
| ZKIT ORG |
30208 |
| zombie |
684186 |
| zoningpolygon |
2293495 |
| Zoo Animal Classification |
5331 |
| "Zwarte Piet" Tweets |
1949268 |
| ZwidosTweets |
28043 |
| zzself |
34574009 |
|
31802982 |
|
566778 |
|
199587 |
|
346 |
| King County data |
874920 |
| King County |
861852 |
|
1433727 |
| taiwan data |
7837 |
|
566778 |
| :: Job |
3989247 |
|
61627 |
| ********* |
7313709 |
|
20545475 |
|
9217261 |
| ...... |
29953584 |
| 0.1400 |
206347 |
| 0.609034_0.608800_submission |
7412065 |
| 0.85933376.csv |
4082718 |
| 0.9336273678.csv |
4071836 |
| 01-train |
156 |
| 01040123 |
7374315 |
| 0105nn1000hl3hl |
82671011 |
| 0623-goodsprice |
1846250 |
| 081617 |
202743 |
| 0a7c2a8d_nohash_0.wav |
32044 |
| 0b443cc3ab8dabf57b37cb8d9879107cc54efd989 |
6830160 |
| 1 M+ Real Time stock market data [NSE/BSE] |
221599816 |
| 1 million Sudoku games |
164000018 |
| 1.2 Million Used Car Listings |
146679503 |
| 1.6M accidents & traffic flow over 16 years |
651439827 |
| 1.88 Million US Wildfires |
795785216 |
| 10_sub_for_ensemble |
2283447 |
| 100,000 Random Internet Domain Names |
1799672 |
| 1000 Camera Specs |
87053 |
| 1000 Cameras Dataset |
86961 |
| 1000 Cameras Dataset(Source:Kaggle) |
86961 |
| 1000 Genome Data for Complete Beginners |
277313 |
| 1000 Netflix Shows |
89054 |
| 1000 parallel sentences |
276751 |
| 1000 sentences Canadian parliament |
231295 |
| 100K Coursera's Course Reviews Dataset |
40792183 |
| 101 Innovations - Research Tools Survey |
28569675 |
| 111111 |
7365405 |
| 120 Million Word Spanish Corpus |
677861666 |
| 12306 captcha image |
97558955 |
| 123123 |
7316122 |
| 123124 |
7392318 |
| 123125 |
7442329 |
| 123126 |
7469538 |
| 123456 |
3073 |
| 125,000 Reddit Comments about Diabetes |
64439505 |
| 13,000 Screen Capture Images + How to Get More |
522472158 |
| 15BCE1012_lab_6 |
1340922 |
| 15BCE1012_lab6_DV |
1207668 |
| 15BCE1066_Lab6_data_Visualization |
1340922 |
| 15bce1287_lab_6 |
1340922 |
| 15BCE1376_lab6 |
1340922 |
| 1617_boxscore_edited_wl_ha |
333106 |
| 17 Years of Resident Advisor Reviews |
15266902 |
| 1718_boxscore_wl_ha |
358662 |
| 18 y/o weight-height records |
47778 |
| 18,393 Pitchfork Reviews |
83585024 |
| 180106_subm_1 |
4082820 |
| 180109_sub_1 |
4044923 |
| 180111_01 |
8071105 |
| 183,000+ Reddit Comments about Trump |
40099599 |
| 18th SAARC Tweets |
17129676 |
| 1data wrewrw |
89823 |
| 1k Pharmaceutical Pill Image Dataset |
8414289 |
| 1millionfile |
24899807 |
| 1st Submission |
8071237 |
| 1stsubmit |
7257447 |
| 1weigts |
2943944 |
| 1xgboost |
8652358 |
| 1YearTrainingData |
19261 |
| 2 Class Classification |
12035 |
| 2_combo_EDA_Output.csv |
221340 |
| 20 by median rank LB .285 script |
24706923 |
| 20 Newsgroups |
72078077 |
| 20 Years of Games |
2019628 |
| 2010 Austin weather |
254046 |
| 2010 US Census data |
11452992 |
| 2011 - 2013 NYC Traffic Volume Counts |
1436453 |
| 2011 NOAA Austin Climate |
236109 |
| 2012 and 2016 Presidential Elections |
3381885 |
| 2012 Election- Obama vs Romney |
158033839 |
| 2013 American Community Survey |
4203827010 |
| 2013-2014 Seoul Metropolitan Region Weather |
402307 |
| 2014 ACS Dashboard |
34483689 |
| 2014 American Community Survey |
3082677840 |
| 2014 New York City Taxi Trips |
512755993 |
| 2014 Public Libraries Survey |
3549121 |
| 2014 UN COMTRADE DATA |
177943062 |
| 2014 World Cup Forecasts and Scores |
285453 |
| 2014&2017 Bandung Public Transportation Data |
9755 |
| 2014nbaplayers |
82076 |
| 2015 American Community Survey |
4313602552 |
| 2015 Canadian General Election results |
42286565 |
| 2015 Flight Delays and Cancellations |
592430817 |
| 2015 Global Open Data Index |
262911 |
| 2015 LAPD Calls For Service |
58568183 |
| 2015 Notebook UX Survey |
766065 |
| 2015 NYC Taxi Trips |
278000672 |
| 2015 Reddit Comments |
7610868 |
| 2015 Traffic Fatalities |
92018865 |
| 2015 US County-Level Population Estimates |
2204609 |
| 2015 US Traffic Fatalities |
9233282 |
| 2015-16-premier-league |
456 |
| 2016 Advanced Placement Exam Scores |
25797 |
| 2016 and 2017 Kitefoil Race Results |
392687 |
| 2016 Congress Votes |
47687 |
| 2016 Election Polls |
3097615 |
| 2016 EU Referendum in the United Kingdom |
118579 |
| 2016 Global Ecological Footprint |
22560 |
| 2016 Jan-June NYC Weather, hourly |
348557 |
| 2016 March ML Mania Predictions |
28731852 |
| 2016 New Coder Survey |
10079792 |
| 2016 NYC Real Time Traffic Speed Data Feed |
721872569 |
| 2016 Olympics in Rio de Janeiro |
794050 |
| 2016 Parties in New York |
55830594 |
| 2016 Presidential Campaign Finance |
9216805 |
| 2016 U.S. Presidential Campaign Texts and Polls |
1782759 |
| 2016 U.S. Presidential Election Memes |
27375544 |
| 2016 US Election |
50164290 |
| 2016 US Presidential Debates |
375078 |
| 2016 US Presidential Election Vote By County |
1682243 |
| 2016 US Presidential Primary Debates |
4317145 |
| 2016 VOTER Survey Data Set |
62584572 |
| 2017 #Oscars Tweets |
16925495 |
| 2017 census data for 4chan's fitness board |
118761 |
| 2017 Conservative Party of Canada Leadership |
1902750 |
| 2017 Iditarod Trail Sled Dog Race |
141881 |
| 2017 Index of economic freedom |
28069 |
| 2017 March ML Mania Predictions |
27699297 |
| 2017 March ML Mania Processed Predictions |
93073830 |
| 2017 Military Strength Ranking |
60594 |
| 2017 State Assembly Election Results |
842328 |
| 2017_07_18-14_10_38_bioharness |
2513688 |
| 2017_2c_OrgDatos_TP1AnalisisExploratorio |
244627734 |
| 2017_X |
29530 |
| 2017-10-20 |
230978 |
| 2017-10-20-BCHARTS-KRAKENUSD |
113807 |
| 2017-12-27-Leaderboard Corporacion Favorita |
330254 |
| 2017.CSV |
24139 |
| 20170110 |
4044961 |
| 20171219_1 |
7258872 |
| 20171226 |
1420 |
| 20171227 |
29061 |
| 20180104 |
7369884 |
| 201801041655 |
14712605 |
| 20180111102200 |
16149782 |
| 20180111153101 |
7367735 |
| 20180111153101 |
24219783 |
| 20180112181420 |
16139459 |
| 20180113073044 |
16670587 |
| 20180113073715 |
16136616 |
| 20180113075700 |
16136892 |
| 20180113153630 |
8069270 |
| 20180113155355 |
8069747 |
| 20180113155918 |
8069994 |
| 20180113161850 |
9321763 |
| 20180113163013 |
9321763 |
| 20180113165846 |
2434243 |
| 20180114000000 |
13156476 |
| 20180114190121 |
16139215 |
| 20180116083816 |
8071716 |
| 20180116103132 |
9594150 |
| 20180116103133 |
8067964 |
| 20k Tweets Relating to #JerusalemEmbassy |
1155425 |
| 222222 |
7372219 |
| 23333 |
439 |
| 236365 |
31887 |
| 24 thousand tweets later |
3697735 |
| 24102017_sf |
18167 |
| 24102017ds_fs |
18167 |
| 24500 plane routes |
238249 |
| 273_project |
140401069 |
| 2D_example |
421 |
| 2epochs |
22857356 |
| 2nd Submission |
8069950 |
| 2sigma |
580023307 |
| 2st Submission |
8069950 |
| 2YearDataAnalysisData |
38218 |
| 3 Million German Sentences |
400191072 |
| 3 models_HPfiltered_252x252 |
13313269 |
| 30 Years of European Solar Generation |
591041118 |
| 30 Years of European Wind Generation |
744718937 |
| 300600 |
56947062 |
| 300600_2 |
44234355 |
| 311 service requests NYC |
235458471 |
| 311_NYC_2011 |
30769335 |
| 311_Service_Requests_from_2010_to_Present |
1865950580 |
| 311_Service_Requests_from_2010_to_Present.csv.zip |
1695248161 |
| 350 000+ movies from themoviedb.org |
201485329 |
| 35000 car adv |
2808122 |
| 380,000+ lyrics from MetroLyrics |
324632382 |
| 3D MNIST |
255816956 |
| 3Happyscore |
65289 |
| 3mWindow |
585319 |
| 3rd Submission |
8069938 |
| 3rd_submit |
6298003 |
| 444444444 |
35045 |
| 4chan.org/pol forum posts with keyword Trump |
120786944 |
| 4dataset |
743301 |
| 5 Celebrity Faces Dataset |
2639585 |
| 5 Day Data Challenge: Day 1 |
5636539 |
| 5 Day Data Challenge: Day 1 |
87053 |
| 5 day data-challange day-1 |
7549 |
| 5 giorni Data Challenge: Day 4 |
5213 |
| 5_10_network |
8368 |
| 5-Day Data Challenge Sign-Up Survey Responses |
722715 |
| 5.1. Clientes-centro-comercial |
4339 |
| 50 Startups |
2436 |
| 50_100_network |
809692 |
| 50_Startups |
2436 |
| 500 Cities: Local Data for Better Health |
227806975 |
| 500 samples |
50842 |
| 5000_IMDB_Movies_Multivariant_Analysis |
1877207 |
| 50000_Songs_GRU |
4165976 |
| 508_HW1 |
626906 |
| 50words |
707 |
| 515K Hotel Reviews Data in Europe |
238154765 |
| 515k Reviews After Preprocessing |
63020578 |
| 52testingcrypt |
2326 |
| 55000+ Song Lyrics |
72436445 |
| 555555555 |
35045 |
| 57_features |
4434985 |
| 58 years of Temperature Data |
4479618 |
| 65 World Indexes |
123291 |
| 7_digit |
824 |
| 7ecb8f4fe2ece9f4c8ffd23af10c310f |
127264365 |
| 7k kitties |
23886292 |
| 80 Cereals |
5063 |
| 80 Cereals |
5157 |
| 80 Cereals: Nutrition data on 80 cereal products |
2258 |
| 801 Funny Images With Rating |
128752299 |
| 80cereal |
2258 |
| 80cereal.csv |
5063 |
| 888888 |
35045 |
| 8a.nu Climbing Logbook |
467013632 |
| 900_items |
184439926 |
| 911 Data |
1816 |
| 911.csv |
10196426 |
| 99 acres Housing details |
44502 |
| A 6-figure prize by soccer prediction (Live Feed) |
17879235 |
| A Benchmark Data for Turkish Text Categorization |
3503109 |
| a dataset test |
315047004 |
| A millennium of macroeconomic data |
25937595 |
| A Million News Headlines |
19469752 |
| A Million Pseudo-Random Digits |
2000031 |
| A Pickle of unique words from Quoras Data |
1090513 |
| A plume |
271744 |
| A Recruiter Year in Review! |
295819 |
| A Tribuna |
140405644 |
| A Visual and Intuitive Train-Test Pattern |
1016222 |
| A Year of Pumpkin Prices |
188088 |
| A-Z Handwritten Alphabets in .csv format |
85236774 |
| A1-Burtin |
752 |
| A102 Big Mart |
1397246 |
| A102 DATASET |
1395830 |
| A102 project |
1397246 |
| aaaaaa |
4044907 |
| aa102data |
1395830 |
| aaaaaa |
1857825 |
| aaaaaa |
1263743 |
| aaaaaa |
3172 |
| a102data |
1397246 |
| aaData |
164579 |
| aaaaaa |
61194 |
| aadasdasdasd |
28735 |
| aaaaaa |
460 |
| aadhaar |
11817671 |
| aaaaaaaaaaaaaaaaaaaaaaaaaaaaa |
3218780 |
| aaaaaaaa |
134368 |
| aaaaaaaaaaaa |
1737535 |