APA Style
Sarisa, M. , Sarisa, M. Boddapati, V. N. , Boddapati, V. N. Patra, G. K. , Patra, G. K. Kuraku, C. , Kuraku, C. Konkimalla, S. , & Konkimalla, S. (2021). An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods.
Current Research in Public Health, 1(1), 75-85.
https://doi.org/10.31586/jaibd.2020.1110
ACS Style
Sarisa, M. ; Sarisa, M. Boddapati, V. N. ; Boddapati, V. N. Patra, G. K. ; Patra, G. K. Kuraku, C. ; Kuraku, C. Konkimalla, S. ; Konkimalla, S. An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods.
Current Research in Public Health 2021 1(1), 75-85.
https://doi.org/10.31586/jaibd.2020.1110
Chicago/Turabian Style
Sarisa, Manikanth, Manikanth Sarisa. Venkata Nagesh Boddapati, Venkata Nagesh Boddapati. Gagan Kumar Patra, Gagan Kumar Patra. Chandrababu Kuraku, Chandrababu Kuraku. Siddharth Konkimalla, and Siddharth Konkimalla. 2021. "An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods".
Current Research in Public Health 1, no. 1: 75-85.
https://doi.org/10.31586/jaibd.2020.1110
AMA Style
Sarisa M, Sarisa MBoddapati VN, Boddapati VNPatra GK, Patra GKKuraku C, Kuraku CKonkimalla S, Konkimalla S. An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods.
Current Research in Public Health. 2021; 1(1):75-85.
https://doi.org/10.31586/jaibd.2020.1110
@Article{crph1110,
AUTHOR = {Sarisa, Manikanth and Boddapati, Venkata Nagesh and Patra, Gagan Kumar and Kuraku, Chandrababu and Konkimalla, Siddharth and Rajaram, Shravan Kumar},
TITLE = {An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {75-85},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1110},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2020.1110},
ABSTRACT = {Due to influence of Internet, this e-commerce sector has developed rapidly. Most of the online retailing or selling businesses are seeking for way for predicting their products demand. Sales forecasting may help retailers develop a sales strategy that will enhance sales and attract more money and investment. The current research work puts forward a machine learning framework to forecast E-commerce sales for strategic management using a dataset of E-commerce transactions. With 70 percent of the data for train and 30 percent for test, three models were produced, namely, Random Forest, Decision Tree, and XGBoost. In order to evaluate the models, performance measures inclusive of R-squared (R²) and Root Mean Squared Error (RMSE) were employed. Thus, the XGBoost model was the most accurate in marketing predictive capabilities for E-commerce sales with the R² score of 96.3%. This has demonstrated the increased capability of XGBoost algorithm to forecast E-commerce monthly sales more accurately than other models and can assist decision makers for managing inventory and arriving smart and quick decisions in this rapidly growing E-commerce market. The findings reiterate the importance of using advanced analytics in order to drive effectiveness and customer experience within E-commerce sector.},
}
%0 Journal Article
%A Sarisa, Manikanth
%A Boddapati, Venkata Nagesh
%A Patra, Gagan Kumar
%A Kuraku, Chandrababu
%A Konkimalla, Siddharth
%A Rajaram, Shravan Kumar
%D 2021
%J Current Research in Public Health
%@ 2831-5162
%V 1
%N 1
%P 75-85
%T An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods
%M doi:10.31586/jaibd.2020.1110
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1110
TY - JOUR
AU - Sarisa, Manikanth
AU - Boddapati, Venkata Nagesh
AU - Patra, Gagan Kumar
AU - Kuraku, Chandrababu
AU - Konkimalla, Siddharth
AU - Rajaram, Shravan Kumar
TI - An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods
T2 - Current Research in Public Health
PY - 2021
VL - 1
IS - 1
SN - 2831-5162
SP - 75
EP - 85
UR - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1110
AB - Due to influence of Internet, this e-commerce sector has developed rapidly. Most of the online retailing or selling businesses are seeking for way for predicting their products demand. Sales forecasting may help retailers develop a sales strategy that will enhance sales and attract more money and investment. The current research work puts forward a machine learning framework to forecast E-commerce sales for strategic management using a dataset of E-commerce transactions. With 70 percent of the data for train and 30 percent for test, three models were produced, namely, Random Forest, Decision Tree, and XGBoost. In order to evaluate the models, performance measures inclusive of R-squared (R²) and Root Mean Squared Error (RMSE) were employed. Thus, the XGBoost model was the most accurate in marketing predictive capabilities for E-commerce sales with the R² score of 96.3%. This has demonstrated the increased capability of XGBoost algorithm to forecast E-commerce monthly sales more accurately than other models and can assist decision makers for managing inventory and arriving smart and quick decisions in this rapidly growing E-commerce market. The findings reiterate the importance of using advanced analytics in order to drive effectiveness and customer experience within E-commerce sector.
DO - An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods
TI - 10.31586/jaibd.2020.1110
ER -
