IEEE Style Citation: Tadvi Shabana, Shaikh Afifa, Sayyed Naziya, Khan Mariya, “A Novel Machine Learning Methodology to Increase Sales in Business Services,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.924-926, 2018.
MLA Style Citation: Tadvi Shabana, Shaikh Afifa, Sayyed Naziya, Khan Mariya "A Novel Machine Learning Methodology to Increase Sales in Business Services." International Journal of Computer Sciences and Engineering 6.12 (2018): 924-926.
APA Style Citation: Tadvi Shabana, Shaikh Afifa, Sayyed Naziya, Khan Mariya, (2018). A Novel Machine Learning Methodology to Increase Sales in Business Services. International Journal of Computer Sciences and Engineering, 6(12), 924-926.
BibTex Style Citation:
@article{Shabana_2018,
author = {Tadvi Shabana, Shaikh Afifa, Sayyed Naziya, Khan Mariya},
title = {A Novel Machine Learning Methodology to Increase Sales in Business Services},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2018},
volume = {6},
Issue = {12},
month = {12},
year = {2018},
issn = {2347-2693},
pages = {924-926},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3439},
doi = {https://doi.org/10.26438/ijcse/v6i12.924926}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i12.924926}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3439
TI - A Novel Machine Learning Methodology to Increase Sales in Business Services
T2 - International Journal of Computer Sciences and Engineering
AU - Tadvi Shabana, Shaikh Afifa, Sayyed Naziya, Khan Mariya
PY - 2018
DA - 2018/12/31
PB - IJCSE, Indore, INDIA
SP - 924-926
IS - 12
VL - 6
SN - 2347-2693
ER -
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Abstract
Ticket purchasing in advance is a well- known traditional approach but it entirely depends on the Airline industry to change the fare according to factors whether the travel is during the holidays, the number of free seats in the plane etc. Some of the features are seen, but some of them remained hidden. We are using Indian Domestic Airline Dataset which contains multiple columns so over a period as the data increases (approx. 1 year) we will be able to extract few more hidden features to increase the efficiency and accuracy of the system. The goal is to use machine learning techniques to model the behaviour of flight ticket prices over the time. In other words system will be able to provide a general idea to the clients when to increase or decrease the fares i.e. prediction of Airfare. For that after collecting the dataset the proposed system will extract important features from dataset, cleaning of data and using Regression Machine Learning Algorithms multiple models will be trained and the accuracy of those models will be compared and prediction report will be given to client.
Key-Words / Index Term
Airfare, Feature Extraction, Cleaning data, Regression, Machine Learning, Data Analytics
References
[1] A regression model for predicting optimal purchase timing for airline tickets.
[2] “A Model of Optimal Consumer Search and Price Discrimination in the Airline Industry”. David Li Sunday 15th November, 2015
[3] International Journal of Computer Science and Mobile Computing “big data analysis of airline data set using hive” by p. swathi1, j. kumari2.
[4] International journal of engineering science invention “airfare analysis and prediction using data mining and machine learning” by bhavuk chawla1,ms.chandandeep kaur2.
[5] “Dynamic Pricing in the Airline Industry” By R. Preston McAfee and Vera Te Velde: California Institute of Technology.
[6] Airline Data Set,United States Department of Transportation, Office of the Assistant Secretary for Research and Technology,BureauofTransportationStatistics,http://www.tr anstats.bts.gov/DL_SelectFields.asp?Table_ID=236
[7] William Groves and Maria Gini, ”On Optimizing Airline Ticket Purchase Timing”, University of Minnesota, 2011
[8] ManolisPapadakis, “Predicting Airfare Prices” in Stanford,2013
[9] Yuwen Chen, Jian Cao, Shanshan Feng and Yudong Tan,“An ensemble learning based approach for building airfare forecast service” Big Data (Big Data), 2015 IEEE International Conference, 29 Oct.-1 Nov. 2015.
[10] WEKA Manual for Version 3-6-8, The University of Waikato, 2012
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