Forecasting Sales and Return Products for Retail Corporations and Bridging Among Them

2021 ◽  
pp. 250-281
Author(s):  
Md Mushfique Hasnat Chowdhury ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Machine Learning ◽  
Inventory Management ◽  
Learning Algorithms ◽  
Retail Store ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Forecasting Models ◽  
The Future ◽  
Product Sales

The purpose of this study is to show how we can bridge sales and return forecasts for every product of a retail store by using the best model among several forecasting models. Managers can utilize this information to improve customer's satisfaction, inventory management, or re-define policy for after sales support for specific products. The authors investigate multi-product sales and return forecasting by choosing the best forecasting model. To this aim, some machine learning algorithms including ARIMA, Holt-Winters, STLF, bagged model, Timetk, and Prophet are utilized. For every product, the best forecasting model is chosen after comparing these models to generate sales and return forecasts. This information is used to classify every product as “profitable,” “risky,” and “neutral,” The experiment has shown that 3% of the total products have been identified as “risky” items for the future. Managers can utilize this information to make some crucial decisions.

scholarly journals Mean Received Resources Meet Machine Learning Algorithms to Improve Link Prediction Methods

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 35
Author(s):  
Jibouni Ayoub ◽  
Dounia Lotfi ◽  
Ahmed Hammouch
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Actual State ◽  
The Future ◽  
Auc Value ◽  
The Mean ◽  
Analysis Of Social Networks

The analysis of social networks has attracted a lot of attention during the last two decades. These networks are dynamic: new links appear and disappear. Link prediction is the problem of inferring links that will appear in the future from the actual state of the network. We use information from nodes and edges and calculate the similarity between users. The more users are similar, the higher the probability of their connection in the future will be. The similarity metrics play an important role in the link prediction field. Due to their simplicity and flexibility, many authors have proposed several metrics such as Jaccard, AA, and Katz and evaluated them using the area under the curve (AUC). In this paper, we propose a new parameterized method to enhance the AUC value of the link prediction metrics by combining them with the mean received resources (MRRs). Experiments show that the proposed method improves the performance of the state-of-the-art metrics. Moreover, we used machine learning algorithms to classify links and confirm the efficiency of the proposed combination.

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

Demand forecasting at retail stage for selected vegetables: a performance analysis

2019 ◽  
Vol 14 (4) ◽  
pp. 1042-1063 ◽  
Author(s):  
Rahul Priyadarshi ◽  
Akash Panigrahi ◽  
Srikanta Routroy ◽  
Girish Kant Garg
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Forecast Error ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Models

Purpose The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis. Design/methodology/approach Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables. Findings From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models. Research limitations/implications The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment. Practical implications The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue. Originality/value The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

scholarly journals A Survey on Performing E-Voting through Facial Recognition

2021 ◽  
pp. 141-147
Author(s):  
Pratik Hopal ◽  
Alkesh Kothar ◽  
Swamini Pimpale ◽  
Pratiksha More ◽  
Jaydeep Patil
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Facial Recognition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Voting Systems ◽  
Human Errors ◽  
Research Article ◽  
The Future

The election procedure is one of the most essential processes to take place in a democracy. Even though there have been immense technological advancements, the process of election has been highly limited. Most of the election procedures have been performed using ballot boxes which is an old process and needs to be updated. The security of such practices is also a concern as the identification of the voters is being done manually by the election officers. This process also needs an improvement to increase accuracy and reduce human errors by automating the process. Therefore, for this purpose, this research article analyzes the previous researches on this paradigm. This allows an effective understanding of the machine learning algorithms that are used for automatic facial recognition in the E-voting systems. This paper comes to the conclusion that the Recurrent Neural Networks are best suited for such an application for facial recognition. The future editions of this research will elaborate more on the proposed system in detail.

scholarly journals Future Trend Prediction in Share Market with Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3978-3981
Author(s):  
Prof. Kanchan Mahajan
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Life Span ◽  
Learning Algorithms ◽  
Future Trend ◽  
Machine Learning Algorithms ◽  
Trend Prediction ◽  
Stock Market Prediction ◽  
The Future

In Stock Market Prediction, the point is to estimate the future worth of the monetary loads of an organization. The new pattern in securities exchange forecast advances is the utilization of AI which makes expectations dependent on the upsides of current financial exchange lists via preparing on their past qualities. AI itself utilizes various models to make expectation simpler and credible. The thought centers on the utilization of dissimilar Machine learning algorithms to anticipate stock qualities. Variables considered are open, close, low, high and volume. The principal thing we have considered is the dataset of the securities exchange costs from earlier year. The dataset was pre-handled and adjusted for genuine examination. What's more, the proposed thought inspects the utilization of the forecast framework in verifiable settings and issues related with the accuracy of the general qualities given. The thought additionally portrays AI model to foresee the life span of the stock in a serious market. The effective forecast of the stock will be an extraordinary resource for the securities exchange establishments and will give genuine answers for the issues that stock financial backers face.

scholarly journals Machine learning algorithms to predict 30-day readmission in patients with stroke: a prospective cohort study

2020 ◽  
Author(s):  
Yu-Ching Chen ◽  
Jo-Hsuan Chung ◽  
Yu-Jo Yeh ◽  
Hsiu-Fen Lin ◽  
Ching-Huang Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cox Regression ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Significant Variable ◽  
Ann Model ◽  
Stroke Patients ◽  
Forecasting Models

Abstract Background No studies have discussed machine learning algorithms to predict the risk of 30-day readmission in patients with stroke. The objective of the present study was to compare the accuracy of the artificial neural network (ANN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models and to explore the significant factors in predicting 30-day readmission after stroke. Methods This study prospectively compared the accuracy of the models using clinical data for 1,476 patients with stroke treated in six hospitals between March, 2014 and September, 2019. A training dataset (n=1,033) was used for model development, a testing dataset (n=443) was used for internal validation, and a validating dataset (n=167) was used for external validation. A global sensitivity analysis was performed to compare the significance of the selected input variables. Results Of all forecasting models, the ANN model had the highest accuracy in predicting 30-day readmission after stroke and had the highest overall performance indices. According to the ANN model, 30-day readmission was significantly associated with post-acute care (PAC) program, patient attributes, clinical attributes, and functional status scores before re-habilitation (all P <0.05). Additionally, PAC program was the most significant variable affecting 30-day readmission, followed by nasogastric tube insertion, and stroke type ( P <0.05). Conclusions Comparisons of the five forecasting models indicated that the ANN model had the highest accuracy in predicting 30-day readmission in stroke patients. Before stroke patients are discharged from hospitalization, they should be counseled regarding their potential for recovery and other possible outcomes. These important predictors can also be used to educate candidates for stroke patients who underwent PAC rehabilitation with respect to the course of recovery and health outcomes.

scholarly journals An Efficient Suicide based Dataset using Machine Learning Algorithms

2019 ◽  
Vol 8 (12S) ◽  
pp. 1267-1271
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Indian Government ◽  
The Future ◽  
Death State ◽  
The Government ◽  
Government Records ◽  
Major Factors ◽  
Year 2000

A study is presented on analyzing the major factors that affect the number of suicides in different parts of India from year 2000 to 2012 and using them to predict the number of suicides in the future. By analyzing the data and predicting the major causes of suicides it can help government to know which part of population is most affected, so that the government can provide required steps to avoid suicides. The Indian government records the database of each suicide occurs in India. Along with the age-group, cause of death, state of victim, this data was made public by crime branch bureau of the data analytics purpose. Relationship will be made between the different features of suicide so that a linear relationship can be formed with the help of linear regression and other machine learning algorithms will be used to develop a model for the prediction of number of suicides in the future. It has been found that the results obtained by machine learning algorithms are more accurate when compared with the traditional algorithms.

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