This document presents a machine learning model to predict aircraft ticket prices. It discusses previous works on airfare price prediction and describes a proposed system using random forest regression. The methodology includes data collection from online sources, preprocessing like one-hot encoding of categorical variables, and generating a random forest regression model. The model achieves good accuracy, with a mean absolute error of 1172.61, mean squared error of 4044048.9764, root mean squared error of 2010.982, and R-squared value of 81.0258. Future work may include exploring additional impactful features and expanding the model using larger datasets.
ADS Team 3 Final Presentation.
Discusses different models to perform delay prediction, flight cancellation, flight recommendation and flight fare prediction.
Profit Maximization is addressing Multi-stop operating model of Airlines, it shows how to max. profit in terms of CASK and RASK analysis, delivering the best seniario to select the aircraft then the best result to operate the right segment
The objective of the project is to predict whether a flight will be delayed or not by studying the various features of the flight and calculating the probability for delay using Bayesian Classification.
ADS Team 3 Final Presentation.
Discusses different models to perform delay prediction, flight cancellation, flight recommendation and flight fare prediction.
Profit Maximization is addressing Multi-stop operating model of Airlines, it shows how to max. profit in terms of CASK and RASK analysis, delivering the best seniario to select the aircraft then the best result to operate the right segment
The objective of the project is to predict whether a flight will be delayed or not by studying the various features of the flight and calculating the probability for delay using Bayesian Classification.
What make airlines gain profits while the others fall in losses !!!
How LCC creates profits in a recession time âŠ.
Is Airline Industry a profitable Industry !!!
What are various strategies in such casesâŠ
And how to survive in this miss !!!!!!!
Improving the efficiency of aircraft turnaroundAppear
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Implementing innovative app toolkit for airport workers that improve aircraft turnaround. Airport IQ will develop a mobile information sharing system connecting back-end A-CDM systems with mobile devices (smartphones, tablets and other wearable devices) carried by ground staff. The system will provide the right information at the right place and time to the right people, making it easier for planners to make optimal use of resources.
We are looking for potential partners to participate in the project whether they are Airports, Ground Handlers, Airlines, System providers and other commercial entities. If you would like to know more, then get in touch
Exploratory Data Analysis for 2015 Flight Delays and Cancellations dataset from Kaggle.
You can find the script here:
https://github.com/SubhiH/Flight-Delays-and-Cancellations-EDA
The Presentation is about how airline industry has evolved and how systems in airline industry have evolved. The presentation further provided a roadmap on system evolution for airlines
This document provides a structural outline of the EASA Airworthiness Regulations. It is a Publication of Wing Engineering Limited's Key Points Resource Library.
Revenue management first appeared in the airline industry in the early 1980s. It arose from the need for accurate demand estimates and profit-generating resource allocations in a newly deregulated environment. We begin this program and this module with a look back at the main causes and consequences of airline deregulation in North America. We describe how the deregulated North American airline industry has encouraged a trend toward deregulation, or at least liberalization, worldwide. We then move on to introduce the basic concept involved in airline revenue management.
Route Development - it is task of airlines that done by airports, as most of airports in world try to convince airlines to operate and used their airports, the issue, is not the huge facility and or service, but it is a business, if airline does not get a business, then it is no need to operate to that destination.
This virtual simulation program was developed to help airline management teams understand competitive market dynamics and improve problem solving and decision-making skills.
Find out more at: http://www.iata.org/airline-business-simulation
MACHINE LEARNING TECHNIQUES FOR ANALYSIS OF EGYPTIAN FLIGHT DELAYIJDKP
Â
Flight delay has been the fiendish problem to the world's aviation industry, so there is very important
significance to research for computer system predicting flight delay propagation. Extraction of hidden
information from large datasets of raw data could be one of the ways for building predictive model. This
paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt
Airlineâs Flight dataset. In this work, four decision tree classifiers were evaluated and results show that the
REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules based
classifiers were compared and results show that PART provides best accuracy among studied rule-based
classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work
concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also, the
current work is extended to apply of Apriori association technique to extract some important information
about flight delay. Association rules are presented and association technique is evaluated.
Benchmarking data mining approaches for traveler segmentation IJECEIAES
Â
The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.
What make airlines gain profits while the others fall in losses !!!
How LCC creates profits in a recession time âŠ.
Is Airline Industry a profitable Industry !!!
What are various strategies in such casesâŠ
And how to survive in this miss !!!!!!!
Improving the efficiency of aircraft turnaroundAppear
Â
Implementing innovative app toolkit for airport workers that improve aircraft turnaround. Airport IQ will develop a mobile information sharing system connecting back-end A-CDM systems with mobile devices (smartphones, tablets and other wearable devices) carried by ground staff. The system will provide the right information at the right place and time to the right people, making it easier for planners to make optimal use of resources.
We are looking for potential partners to participate in the project whether they are Airports, Ground Handlers, Airlines, System providers and other commercial entities. If you would like to know more, then get in touch
Exploratory Data Analysis for 2015 Flight Delays and Cancellations dataset from Kaggle.
You can find the script here:
https://github.com/SubhiH/Flight-Delays-and-Cancellations-EDA
The Presentation is about how airline industry has evolved and how systems in airline industry have evolved. The presentation further provided a roadmap on system evolution for airlines
This document provides a structural outline of the EASA Airworthiness Regulations. It is a Publication of Wing Engineering Limited's Key Points Resource Library.
Revenue management first appeared in the airline industry in the early 1980s. It arose from the need for accurate demand estimates and profit-generating resource allocations in a newly deregulated environment. We begin this program and this module with a look back at the main causes and consequences of airline deregulation in North America. We describe how the deregulated North American airline industry has encouraged a trend toward deregulation, or at least liberalization, worldwide. We then move on to introduce the basic concept involved in airline revenue management.
Route Development - it is task of airlines that done by airports, as most of airports in world try to convince airlines to operate and used their airports, the issue, is not the huge facility and or service, but it is a business, if airline does not get a business, then it is no need to operate to that destination.
This virtual simulation program was developed to help airline management teams understand competitive market dynamics and improve problem solving and decision-making skills.
Find out more at: http://www.iata.org/airline-business-simulation
MACHINE LEARNING TECHNIQUES FOR ANALYSIS OF EGYPTIAN FLIGHT DELAYIJDKP
Â
Flight delay has been the fiendish problem to the world's aviation industry, so there is very important
significance to research for computer system predicting flight delay propagation. Extraction of hidden
information from large datasets of raw data could be one of the ways for building predictive model. This
paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt
Airlineâs Flight dataset. In this work, four decision tree classifiers were evaluated and results show that the
REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules based
classifiers were compared and results show that PART provides best accuracy among studied rule-based
classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work
concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also, the
current work is extended to apply of Apriori association technique to extract some important information
about flight delay. Association rules are presented and association technique is evaluated.
Benchmarking data mining approaches for traveler segmentation IJECEIAES
Â
The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.
Decision Support System Using FUCOM-MARCOS for Airline Selection in IndonesiaGede Surya Mahendra
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Abstractâ Since deregulation in 1999, the development of the Indonesian aviation industry has continued to develop. However, many airlines still face various problems before and during flights. Problems in the plane, ranging from engine problems, technical problems, tire damage, cockpit problems to air pressure problems. Airlines customers have personal considerations and preferences when choosing an airline. The many choices and many considerations of airlines often confuse customers. To solve this problem, a decision support system (DSS) can be used to provide advice in selecting airlines based on customer preferences. This study uses the FUCOM-MARCOS method, using 8 criteria and 6 testing alternatives. When using FUCOM to calculate criterion weights, it appears that the factor price (C5) is the factor that counts most. Calculations using FUCOM-MARCOS show that Garuda Indonesia is the favorite airline in Indonesia with a preference value of 0.7390, followed by Citilink in second place, and Batik Air in third. Testing using consistency analysis shows that Garuda Indonesia remains stable and is the first choice by being ranked first 15 times out of 17 tests, with an average ranking distribution reaching 1.23466.
Intisariâ Sejak deregulasi pada 1999, perkembangan industri penerbangan Indonesia semakin berkembang. Namun, banyak maskapai penerbangan yang masih menghadapi berbagai masalah sebelum dan selama penerbangan. Terdapat masalah di pesawat, mulai dari masalah mesin, masalah teknis, kerusakan ban, masalah kokpit hingga masalah tekanan udara. Pelanggan maskapai penerbangan memiliki pertimbangan dan preferensi pribadi saat memilih maskapai. Banyaknya pilihan dan banyaknya pertimbangan maskapai seringkali membingungkan pelanggan. Untuk mengatasi masalah tersebut, dapat digunakan sistem pendukung keputusan (SPK) untuk memberikan saran dalam memilih maskapai penerbangan berdasarkan preferensi pelanggan. Penelitian ini menggunakan metode FUCOM-MARCOS, menggunakan 8 kriteria dan 6 alternatif untuk dilakukan pengujian. Ketika menggunakan FUCOM untuk menghitung bobot kriteria, terlihat bahwa faktor harga (C5) adalah faktor yang paling diperhitungkan. Perhitungan menggunakan FUCOM- MARCOS menunjukkan bahwa Garuda Indonesia merupakan maskapai terfavorit di Indonesia dengan nilai preferensi 0,7390, disusul Citilink, sebagai peringkat kedua, dan Batik Air menduduki peringkat ketiga. Pengujian menggunakan analisis konsistensi menunjukkan bahwa Garuda Indonesia tetap stabil dan menjadi pilihan pertama, menduduki peringkat pertama sebanyak 15 kali dari 17 pengujian, dengan rata-rata sebaran peringkat mencapai 1.23466.
ABOUT AIRLINE RESERVATION SYSTEM
FULL DETAILS ABOOUT CODING AND PROCESS
HOW IT WORKS AND WHAT PROBLEMS COMES WHILE ONLINE RESERVATION OF AIR TICKET.
TECHNICAL DETAILS IS ALSO MENTION IN THIS .
TABLE STRUCTURE ALSO GIVING WHERE WE CAN SEE CODING ALSO IN THAT . FULL DETAILS PROCESS HAS BEEN GIVING IN THIS
Can we predict Airline fares from London to cities in Asia Karim Awad
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My MSc dissertation where the optimal time to purchase flights to 7 cities in Asia from London is predicted based solely on 5 weeks of data prior to departure. This relies solely on pricing data scraped from the web.
The analysis shows that despite incomplete data, experimenting with different machine learning techniques culminates in being able to predict optimal purchase times for flights to two cities, saving between 7-8% on average expected air fares over this period.
This study suggests a new travel recommendation system (NTRS) that was developed
to generate alternative travel destinations for customers. The proposed approach employs
hybrid data mining methods on NTRS by combining classification and clustering
algorithms. NTRS can be used for different travel data resources to find the best
prediction model to generate accurate recommendations. NTRS was tested by using real
travel data which contains flight and hotel bookings. Before applying data mining
algorithms, data set was cleansed, grouped and preprocessed. Then classification
techniques; ANFIS, RBFN and NaĂŻve Bayes were combined with X-means and Fuzzy Cmeans
clustering algorithms to find the best prediction model for proposing alternative
trips via NTRS. To identify the most suitable prediction model; recall, specificity,
precision, correctness, and RMSE scores were benchmarked and the best one was
dynamically selected. According to the testing scenario results, ANFIS and X-means
combination scored the finest RMSE and correctness values. Based on the proposed
approachâs algorithm, travel locations including trip durations and airline companies
were generated as recommendation output of the testing scenario. Generated
recommendation items can be used for providing suggestions for individuals or it can be
used by travel agencies for planning travel campaigns for target traveler groups. NTRS
proves that it can be executed for different data sets with hybrid data mining methods.
Travel agencies should be able to judge the market demand for tourism to develop sales plans accordingly. However, many travel agencies lack the ability to judge the market demand for tourism, and thus make risky business decisions. Based on the above, this study applied the Artificial Neural Network combined with the Genetic Algorithm (GA) to establish a prediction model of air ticket sales revenue. GA was used to determine the optimum number of input and hidden nodes of a feedforward neural network. The empirical results suggested that the mean absolute relative error(MARE) of the proposed hybrid modelâs predicted value of air ticket sales revenue and the actual value was 10.51%and the correlation coefficient was 0.913. The proposed model had good predictive capability and could provide travel agency operators with reliable and highly efficient analysis data.
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Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
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Francesca Gottschalk from the OECDâs Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasnât one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
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International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!