UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
Call for Paper
Volume 11 | Issue 7 | July 2024

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 7 Issue 5
May-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2005405


Registration ID:
233490

Page Number

659-662

Share This Article


Jetir RMS

Title

Prediction of Workforce Required for a Laundry Firm Using Machine Learning Algorithms

Abstract

For most people, washing laundry at home is a good option. But there are times where people may not be able to do it because of several reasons like work schedules that leave you too exhausted to even do your own laundry. While doing your own laundry might be a good option for saving some money, it can be time consuming. Choosing a laundry service for your clothes and other items has several advantages. Laundry establishments face difficulty in maintaining detailed records of customer clothing; this little problem as seen in most laundry firms discourages customers. In the current system used by laundry firms, several problems arise: customer clothes mix-up, untimely pick-up, and drop-off of clothes, billing related issues, and so on. The aim of this application is to digitalize the current system which will make laundry firms more efficient and ensure customer satisfaction. In this paper, machine learning algorithms are used to build a prediction model which can be used to determine the workforce required to help the firm allocate and utilize resources effectively and efficiently. This study compares two different machine learning algorithms: Random Forest and Bayesian Regression from Linear Models to determine which algorithm predicts the output more accurately. A database was created after conducting a detailed survey and interacting with various firms to understand the inflow and outflow of clothing items and other garments and how they were handled: right from collection to delivery. The resulting database was used to train and test the above-mentioned algorithms. This paper provides information about which machine learning algorithm may be incorporated with existing commercial laundry applications as an additional feature to improve their respective businesses.

Key Words

Laundry Management, Machine learning, Prediction, Random Forest, Bayesian regression, Linear models.

Cite This Article

"Prediction of Workforce Required for a Laundry Firm Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.659-662, May 2020, Available :http://www.jetir.org/papers/JETIR2005405.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Prediction of Workforce Required for a Laundry Firm Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp659-662, May 2020, Available at : http://www.jetir.org/papers/JETIR2005405.pdf

Publication Details

Published Paper ID: JETIR2005405
Registration ID: 233490
Published In: Volume 7 | Issue 5 | Year May-2020
DOI (Digital Object Identifier):
Page No: 659-662
Country: Bangalore, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

0003118

Print This Page

Current Call For Paper

Jetir RMS