AIRLINE TICKET PRICE PREDICTION : AIRLINE TICKET

20111007


AIRLINE TICKET PRICE PREDICTION : EWR FLIGHT DEPARTURES



Airline Ticket Price Prediction





airline ticket price prediction






    airline ticket
  • An airline ticket is a document, created by an airline or a travel agency, to confirm that an individual has purchased a seat on a flight on an aircraft. This document is then used to obtain a boarding pass, at the airport.





    prediction
  • (predict) bode: indicate by signs; "These signs bode bad news"

  • (predict) make a prediction about; tell in advance; "Call the outcome of an election"

  • A thing predicted; a forecast

  • The action of predicting something

  • the act of predicting (as by reasoning about the future)





    price
  • Decide the amount required as payment for (something offered for sale)

  • monetary value: the property of having material worth (often indicated by the amount of money something would bring if sold); "the fluctuating monetary value of gold and silver"; "he puts a high price on his services"; "he couldn't calculate the cost of the collection"

  • determine the price of; "The grocer priced his wares high"

  • the amount of money needed to purchase something; "the price of gasoline"; "he got his new car on excellent terms"; "how much is the damage?"











airline ticket price prediction - The Future




The Future of Everything: The Science of Prediction


The Future of Everything: The Science of Prediction



For centuries, scientists have strived to predict the future. But to what extent have they succeeded? Can past events-Hurricane Katrina, the Internet stock bubble, the SARS outbreak-help us understand what will happen next? Will scientists ever really be able to forecast catastrophes, or will we always be at the mercy of Mother Nature, waiting for the next storm, epidemic, or economic crash to thunder through our lives? In The Future of Everything, David Orrell looks back at the history of forecasting, from the time of the oracle at Delphi to the rise of astrology to the advent of the TV weather report, showing us how scientists (and some charlatans) predicted the future. How can today’s scientists claim to anticipate future weather events when even thee-day forecasts prove a serious challenge? How can we predict and control epidemics? Can we accurately foresee our financial future? Or will we only find out about tomorrow when tomorrow arrives?










79% (17)





Prediction No1 & Prediction No2




Prediction No1 & Prediction No2





Location: Chingford, London, England

Prediction (prophesy) No1
(easy to predict)
The trade unions in west EU, are very demanding.
The workers are very demanding
Everybody is very demanding, compare to the east-central Europe and Asia. Almost half population on a benefits, therefore the taxation is very high. Tomorrow many business from the west EU, will go to east-central Europe and Asia. The taxation there is very low, because nobody is on a benefit there, except pensioners and invalids. The unemployment will grow in west EU. The France will suffer most, because the unemployment there already is 8% (in nazi Germany was 5%)
The war can be started in west Europe - as a result of unemployment.

Prediction No2
(not easy to predict)
The dictator (Antichrist) will emerge in Europe
The microchip (mark of the beast) will be implanted into the right hand of each individual.
Those will die, who will get that microchip implanted.












Predictions




Predictions





Montage expo Predictions, d'Isabelle Pralong et Aurelie William Levaux









airline ticket price prediction








airline ticket price prediction




The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)






During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.










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