Prediction on large scale data using extreme gradient boosting

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.

Manylion Llyfryddiaeth
Prif Awduron:	Sawon, Md.Tariq Hasan, Hosen, Md. Shazzed
Awduron Eraill:	Mostakim, Moin
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	BRAC University 2016
Pynciau:	Extreme gradient boost Prediction modelling Sales prediction Linear regression Time series Gradient boosting
Mynediad Ar-lein:	http://hdl.handle.net/10361/6391

id	10361-6391
record_format	dspace
spelling	10361-63912022-01-26T10:08:24Z Prediction on large scale data using extreme gradient boosting Sawon, Md.Tariq Hasan Hosen, Md. Shazzed Mostakim, Moin Department of Computer Science and Engineering, BRAC University Extreme gradient boost Prediction modelling Sales prediction Linear regression Time series Gradient boosting This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. Cataloged from PDF version of thesis report. Includes bibliographical references (page 42-45). This paper presents a use case of data mining for sales forecasting in retail demand and sales prediction. In particular, the Extreme Gradient Boosting algorithm is used to design a prediction model to accurately estimate probable sales for retail outlets of a major European Pharmacy retailing company. The forecast of potential sales is based on a mixture of temporal and economical features including prior sales data, store promotions, retail competitors, school and state holidays, location and accessibility of the store as well as the time of year. The model building process was guided by common sense reasoning and by analytic knowledge discovered during data analysis and definitive conclusions were drawn. The performances of the XGBoost predictor were compared with those of more traditional regression algorithms like Linear Regression and Random Forest Regression. Findings not only reveal that the XGBoost algorithm outperforms the traditional modeling approaches with regard to prediction accuracy, but it also uncovers new knowledge that is hidden in data which help in building a more robust feature set and strengthen the sales prediction model. Md.Tariq Hasan Sawon Md. Shazzed Hosen B. Computer Science and Engineering 2016-09-08T04:45:31Z 2016-09-08T04:45:31Z 2016 2016-08 Thesis ID 11201030 ID 11221039 http://hdl.handle.net/10361/6391 en BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 45 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Extreme gradient boost Prediction modelling Sales prediction Linear regression Time series Gradient boosting
spellingShingle	Extreme gradient boost Prediction modelling Sales prediction Linear regression Time series Gradient boosting Sawon, Md.Tariq Hasan Hosen, Md. Shazzed Prediction on large scale data using extreme gradient boosting
description	This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
author2	Mostakim, Moin
author_facet	Mostakim, Moin Sawon, Md.Tariq Hasan Hosen, Md. Shazzed
format	Thesis
author	Sawon, Md.Tariq Hasan Hosen, Md. Shazzed
author_sort	Sawon, Md.Tariq Hasan
title	Prediction on large scale data using extreme gradient boosting
title_short	Prediction on large scale data using extreme gradient boosting
title_full	Prediction on large scale data using extreme gradient boosting
title_fullStr	Prediction on large scale data using extreme gradient boosting
title_full_unstemmed	Prediction on large scale data using extreme gradient boosting
title_sort	prediction on large scale data using extreme gradient boosting
publisher	BRAC University
publishDate	2016
url	http://hdl.handle.net/10361/6391
work_keys_str_mv	AT sawonmdtariqhasan predictiononlargescaledatausingextremegradientboosting AT hosenmdshazzed predictiononlargescaledatausingextremegradientboosting
_version_	1814307611884912640

Prediction on large scale data using extreme gradient boosting

Eitemau Tebyg