datadive.net Report : Visit Site


  • Ranking Alexa Global: # 635,500,Alexa Ranking in United States is # 328,839

    Server:Apache...

    The main IP address: 185.169.68.27,Your server -,- ISP:-  TLD:net CountryCode:-

    The description :diving into data a blog on machine learning, data mining and visualization main menu skip to content about monotonicity constraints in machine learning september 16, 2018 in practical machine learning...

    This report updates in 13-Oct-2018

Created Date:2014-02-10
Changed Date:2018-01-18

Technical data of the datadive.net


Geo IP provides you such as latitude, longitude and ISP (Internet Service Provider) etc. informations. Our GeoIP service found where is host datadive.net. Currently, hosted in - and its service provider is - .

Latitude: 0
Longitude: 0
Country: - (-)
City: -
Region: -
ISP: -

HTTP Header Analysis


HTTP Header information is a part of HTTP protocol that a user's browser sends to called Apache containing the details of what the browser wants and will accept back from the web server.

Content-Length:6070
Upgrade:h2,h2c
Content-Encoding:gzip
Vary:Accept-Encoding
Keep-Alive:timeout=2, max=100
Server:Apache
Connection:Upgrade, Keep-Alive
Link:; rel="https://api.w.org/"
Date:Fri, 12 Oct 2018 17:47:20 GMT
Content-Type:text/html; charset=UTF-8

DNS

soa:ns1.virtuaal.com. tech.virtuaal.com. 2017121407 3600 7200 1209600 86400
txt:"v=spf1 a mx include:_spf.virtuaal.com ~all"
ns:ns2.virtuaal.com.
ns1.virtuaal.com.
ns3.virtuaal.com.
ipv4:IP:185.169.68.27
ASN:205930
OWNER:VIRTUAALCOM, EE
Country:EE
mx:MX preference = 0, mail exchanger = datadive.net.

HtmlToText

diving into data a blog on machine learning, data mining and visualization main menu skip to content about monotonicity constraints in machine learning september 16, 2018 in practical machine learning and data science tasks, an ml model is often used to quantify a global, semantically meaningful relationship between two or more values. for example, a hotel chain might want to use ml to optimize their pricing … continue reading → posted in data science , machine learning | replies: 9 random forest interpretation – conditional feature contributions october 24, 2016 in two of my previous blog posts, i explained how the black box of a random forest can be opened up by tracking decision paths along the trees and computing feature contributions. this way, any prediction can be decomposed into … continue reading → posted in random forest | replies: 15 histogram intersection for change detection february 28, 2016 the need for anomaly and change detection will pop up in almost any data driven system or quality monitoring application. typically, there a set of metrics that need to be monitored and an alert raised if the values deviate from … continue reading → posted in change detection , data science | replies: 8 who are the best mma fighters of all time. a bayesian study december 22, 2015 like with any sport, the question of who are the best competitors of all time in mixed martial arts (mma) is something that is hotly debated among mma fans. and unlike for tournament based sports such as tennis, or sports … continue reading → posted in bayesian analysis , data science , machine learning | replies: 17 first estonian machine learning meetup november 24, 2015 today, we had the first event of the estonian machine learning meetup series. i was quite baffled by the pretty massive turnout, with more than a hundred people attending, indicating that such an event series is long overdue. so props … continue reading → posted in machine learning , random forest | replies: 1 7 tools in every data scientist’s toolbox october 15, 2015 there is huge number of machine learning methods, statistical tools and data mining techniques available for a given data related task, from self organizing maps to q-learning, from streaming graph algorithms to gradient boosted trees. many of these methods, while … continue reading → posted in data science , machine learning | replies: 8 random forest interpretation with scikit-learn august 12, 2015 in one of my previous posts i discussed how random forests can be turned into a “white box”, such that each prediction is decomposed into a sum of contributions from each feature i.e. .i’ve a had quite a few requests … continue reading → posted in machine learning , random forest | replies: 36 prediction intervals for random forests june 2, 2015 an aspect that is important but often overlooked in applied machine learning is intervals for predictions, be it confidence or prediction intervals. for classification tasks, beginning practitioners quite often conflate probability with confidence: probability of 0.5 is taken to mean … continue reading → posted in confidence intervals , random forest | replies: 25 which topics get the upvote on hacker news? february 13, 2015 hacker news is a popular social news website, mostly covering technology and startup topics. it relies on user submissions and moderation, where each submitted story can be upvoted and commented by users, which in term determines whether the story reaches … continue reading → posted in nlp , topic modelling | replies: 3 selecting good features – part iv: stability selection, rfe and everything side by side december 20, 2014 in my previous posts, i looked at univariate methods,linear models and regularization and random forests for feature selection.in this post, i’ll look at two other methods: stability selection and recursive feature elimination (rfe), which can both considered wrapper methods. they … continue reading → posted in feature selection , machine learning | replies: 37 post navigation ← older posts recent posts monotonicity constraints in machine learning random forest interpretation – conditional feature contributions histogram intersection for change detection who are the best mma fighters of all time. a bayesian study first estonian machine learning meetup archives september 2018 october 2016 february 2016 december 2015 november 2015 october 2015 august 2015 june 2015 february 2015 december 2014 november 2014 october 2014 meta log in entries rss comments rss wordpress.org

URL analysis for datadive.net


http://www.datadive.net/#content

Whois Information


Whois is a protocol that is access to registering information. You can reach when the website was registered, when it will be expire, what is contact details of the site with the following informations. In a nutshell, it includes these informations;

Domain Name: DATADIVE.NET
Registry Domain ID: 1846075229_DOMAIN_NET-VRSN
Registrar WHOIS Server: whois.ascio.com
Registrar URL: http://www.ascio.com
Updated Date: 2018-01-18T14:10:34Z
Creation Date: 2014-02-10T15:20:24Z
Registry Expiry Date: 2019-02-10T15:20:24Z
Registrar: Ascio Technologies, Inc. Danmark - Filial af Ascio technologies, Inc. USA
Registrar IANA ID: 106
Registrar Abuse Contact Email: [email protected]
Registrar Abuse Contact Phone: +442070159370
Domain Status: clientTransferProhibited https://icann.org/epp#clientTransferProhibited
Name Server: NS1.VIRTUAAL.COM
Name Server: NS2.VIRTUAAL.COM
Name Server: NS3.VIRTUAAL.COM
DNSSEC: unsigned
URL of the ICANN Whois Inaccuracy Complaint Form: https://www.icann.org/wicf/
>>> Last update of whois database: 2018-08-07T22:46:20Z <<<

For more information on Whois status codes, please visit https://icann.org/epp

NOTICE: The expiration date displayed in this record is the date the
registrar's sponsorship of the domain name registration in the registry is
currently set to expire. This date does not necessarily reflect the expiration
date of the domain name registrant's agreement with the sponsoring
registrar. Users may consult the sponsoring registrar's Whois database to
view the registrar's reported date of expiration for this registration.

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database through the use of electronic processes that are high-volume and
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reserves the right to modify these terms at any time.

The Registry database contains ONLY .COM, .NET, .EDU domains and
Registrars.

  REGISTRAR Ascio Technologies, Inc. Danmark - Filial af Ascio technologies, Inc. USA

SERVERS

  SERVER net.whois-servers.net

  ARGS domain =datadive.net

  PORT 43

  TYPE domain

DOMAIN

  NAME datadive.net

  CHANGED 2018-01-18

  CREATED 2014-02-10

STATUS
clientTransferProhibited https://icann.org/epp#clientTransferProhibited

NSERVER

  NS1.VIRTUAAL.COM 185.169.70.231

  NS2.VIRTUAAL.COM 185.169.71.71

  NS3.VIRTUAAL.COM 144.76.180.188

  REGISTERED yes

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