scholarly journals Regressor Fitting Of Feature Importance For Customer Segment Prediction With Ensembling Schemes Using Machine Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 952-956 ◽  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Manufacturing Companies ◽  
Data Set ◽  
Feature Importance ◽  
Customer Segment ◽  
Feature Scaling

Prediction of client behavior and their feedback remains as a challenging task in today’s world for all the manufacturing companies. The companies are struggling to increase their profit and annual turnover due to the lack of exact prediction of customer like and dislike. This leads to the accomplishment of machine learning algorithms for the prediction of customer demands. This paper attempts to identify the important features of the wine data set extracted from UCI Machine learning repository for the prediction of customer segment. The important features are extracted for the various ensembling methods like Ada boost regressor, Ada boost classifier, Random forest regressor, Extra Trees Regressor, Gradient booster regressor. The extracted feature importance of each of the ensembling methods is then fitted with logistic regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. The Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). Experimental results shows that after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 94%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.

scholarly journals Exploration of Multiple Linear Regression with Ensembling Schemes for Roof Fall Assessment using Machine Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 134-139
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Gradient Boosting ◽  
The People ◽  
Roof Fall ◽  
Feature Importance ◽  
Feature Scaling

Roof fall of the building is the major threat to the society as it results in severe damages to the life of the people. Recently, engineers are focusing on the prediction of roof fall of the building in order to avoid the damage to the environment and people. Early prediction of Roof fall is the social responsibility of the engineers towards existence of health and wealth of the nation. This paper attempts to identify the essential attributes of the Roof fall dataset that are taken from the UCI Machine learning repository for predicting the existence of roof fall. In this paper, the important features are extorted from the various ensembling methods like Gradient Boosting Regressor, Random Forest Regressor, AdaBoost Regressor and Extra Trees Regressor. The extracted feature importance of each of the ensembling methods is then fitted with multiple linear regression to analyze the performance. The same extracted feature importance of each of the ensembling methods are subjected to feature scaling and then fitted with multiple linear regression to analyze the performance. The Performance analysis is done with the performance parameters such as Mean Squared Log Error (MSLE), Mean Absolute error (MAE), R2 Score, Mean Squared error (MSE) and Explained Variance Score (EVS). The execution is carried out using python code in Spyder Anaconda Navigator IP Console. Experimental results shows that before feature scaling, Extra Tree Regressor is found to be effective with the MSE of 0.06, MAE of 0.07, R2 Score of 87%, EVS of 0.89 and MSLE of 0.02 as compared to other ensembling methods. In the same way, after applying feature scaling, the feature importance extracted from the Extra Tree Regressor is found to be effective with the MSE of 0.04, MAE of 0.03, R2 Score of 96%, EVS of 0.9 and MSLE of 0.01 as compared to other ensembling methods.

scholarly journals Integrating Ensembling Schemes with Classification for Customer Group Prediction using Machine Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 957-961
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Customer Group ◽  
High Profit ◽  
Feature Importance ◽  
Independent Variable ◽  
Turn Over ◽  
Logistic Regression Classifier

In current marketing scenario, it is highly difficult to earn the high profit by satisfying the customers as well to increase to turn over of the company. For increasing the profit, the organizations are struggling to find a method to analyze their marketing strategy and to understand the customer’s requirements. The main solution to increase the profit of any organization is to manufacture the limited and the needed goods based on the customer’s needs and dislikes. For this, they need to find the customers behavior and the opinion regarding their products. This claims the usage of machine learning algorithms to predict and analyze the behavior of the customer. With this information scenario, we have extracted the wine data set from UCI Machine learning repository. The wine data set is analyzed to decide the dependent and independent variable. The dimensionality reduction is done by applying the ensembling methods. The feature importance of the various ensembling methods like Ada boost regressor, Ada boost classifier, Random forest regressor, Extra Trees Regressor and Gradient booster regressor. The extracted feature importance of the wine data set is fitted with logistic regression classifier to analyse the performance of the each ensembling methods. The metrics used for performance analysis are accuracy, precision, recall, and f-score. Experimental results shows that feature importance obtained from Ada Boost regressor fitted with logistic regression classifier is found to be effective with the accuracy of 94%, Precision of 0.95, Recall of 0.94 and FScore of 0.94 compared to other ensembling methods.

scholarly journals Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 127-133
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Heart Diseases ◽  
Experimental Results ◽  
Disease Prediction ◽  
Feature Importance ◽  
The World ◽  
Feature Scaling ◽  
Logistic Regression Classifier

In today’s modern world, the world population is affected with some kind of heart diseases. With the vast knowledge and advancement in applications, the analysis and the identification of the heart disease still remain as a challenging issue. Due to the lack of awareness in the availability of patient symptoms, the prediction of heart disease is a questionable task. The World Health Organization has released that 33% of population were died due to the attack of heart diseases. With this background, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for analyzing and the prediction of heart disease by integrating the ensembling methods. The prediction of heart disease classes are achieved in four ways. Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier. Secondly, the highly importance features of each of the ensembling methods is filtered from the dataset and it is fitted to logistic regression classifier to analyze the performance. Thirdly, the same extracted important features of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. Fourth, the Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that before applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.04, MAE of 0.07, R2 Score of 92%, EVS of 0.86 and MSLE of 0.16 as compared to other ensembling methods. Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%, EVS of 0.93 and MSLE of 0.18 as compared to other ensembling methods.

scholarly journals Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

2020 ◽  
Vol 19 ◽  
pp. 153303382090982
Author(s):  
Melek Akcay ◽  
Durmus Etiz ◽  
Ozer Celik ◽  
Alaattin Ozen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Nasopharyngeal Cancer ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tumor Diameter ◽  
Survival Prognosis ◽  
Data Set

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

scholarly journals Comparison of Machine Learning Algorithms for Discharge Prediction of Multipurpose Dam

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3369
Author(s):  
Jiyeong Hong ◽  
Seoro Lee ◽  
Gwanjae Lee ◽  
Dongseok Yang ◽  
Joo Hyun Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Gradient Boosting ◽  
Activity Schedules ◽  
Discharge Data ◽  
Dam Inflow

For effective water management in the downstream area of a dam, it is necessary to estimate the amount of discharge from the dam to quantify the flow downstream of the dam. In this study, a machine learning model was constructed to predict the amount of discharge from Soyang River Dam using precipitation and dam inflow/discharge data from 1980 to 2020. Decision tree, multilayer perceptron, random forest, gradient boosting, RNN-LSTM, and CNN-LSTM were used as algorithms. The RNN-LSTM model achieved a Nash–Sutcliffe efficiency (NSE) of 0.796, root-mean-squared error (RMSE) of 48.996 m3/s, mean absolute error (MAE) of 10.024 m3/s, R of 0.898, and R2 of 0.807, showing the best results in dam discharge prediction. The prediction of dam discharge using machine learning algorithms showed that it is possible to predict the amount of discharge, addressing limitations of physical models, such as the difficulty in applying human activity schedules and the need for various input data.

scholarly journals Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

2021 ◽  
Vol 4 (4) ◽  
pp. 77
Author(s):  
Md. Murad Hossain ◽  
Md. Asadullah ◽  
Abidur Rahaman ◽  
Md. Sipon Miah ◽  
M. Zahid Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Domestic Violence ◽  
Logistic Regression ◽  
Random Forest ◽  
Family Violence ◽  
Violence Against Women ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Domestic Violence Against Women ◽  
Women And Children

The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members.

scholarly journals Linear Attribute Projection and Performance Assessment for Signifying the Absenteeism at Work using Machine Learning

2019 ◽  
Vol 8 (3) ◽  
pp. 1262-1267
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Working Hours ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Working Environment ◽  
Experimental Result ◽  
Technological Advancement ◽  
Development Environment

In recent times, with the technological advancement the industry and organization are transforming all their inflow and outflow operations into digital identity. At the outset, the name of the organization is also in the hands of the employee. One of the major needs of the employee in the working environment is to avail leave or vacation based on their family circumstances. Based on the health condition and need of the employee, the organization must extend their leave for the satisfaction of the employee. The performance of the employee is also predicted based on the working days in the organization. With this view, this paper attempts to analyze the performance of the employee and the number of working hours by using machine learning algorithms. The Absenteeism at work dataset from UCI machine learning Repository is used for prediction analysis. The prediction of absent hours is achieved in three ways. Firstly, the correlation between each of the dataset attributes are found and depicted as a histogram. Secondly, the top most high correlated features are identified which are directly fitted to the regression models like Linear regression, SRD regression, RANSAC regression, Ridge regression, Huber regression, ARD Regression, Passive Aggressive Regression and Theilson Regression. Thirdly, the Performance analysis is done by analyzing the performance metrics like Mean Squared Error, Mean Absolute Error, R2 Score, Explained Variance Score and Mean Squared Log Error. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Result shows that the Passive Aggressive Regression have achieved the effective prediction of number of absent hours with minimum MSE of 0.04, MAE of 0.16, EVS of 0.03, MSLE of 0.32 and reasonable R2 Score of 0.89.

scholarly journals Predicting and Analysing the Behaviour of COVID-19

2021 ◽  
pp. 40-46
Author(s):  
Gaurav Singh ◽  
Shivam Rai ◽  
Himanshu Mishra ◽  
Manoj Kumar
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Systems Science ◽  
Data Repository ◽  
Support Vector ◽  
Squared Error

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

scholarly journals Predicting Future Products Rate using Machine Learning Algorithms

2020 ◽  
Vol 12 (5) ◽  
pp. 41-51
Author(s):  
Shaimaa Mahmoud ◽  
◽  
Mahmoud Hussein ◽  
Arabi Keshk
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Regression ◽  
Data Set ◽  
Squared Error

Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

scholarly journals Air Pollution Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (6S3) ◽  
pp. 160-164
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Support Vector Regression ◽  
Air Pollutants ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Time Interval ◽  
K Nearest Neighbors ◽  
Data Set

Air pollution has a serious impact on human health. It occurs because of natural and man-made factors. The major contribution of this research is that it provides a comparison between different methodologies and techniques of mathematical and machine learning models. The process began with integrating data from different sources at different time interval. The preprocessing phase resulted in two different datasets: one-hour and five-minute datasets. Next, we established a forecasting model for particulate matter PM2.5, which is one of the most prevalent air pollutants and its concentration affects air quality. Additionally, we completed a multivariate analysis to predict the PM2.5 value and check the effects of other air pollutants, traffic, and weather. The algorithms used are support vector regression, k-nearest neighbors and decision tree models. The results showed that for the one-hour data set, of the three algorithms, support vector regression has the least root-mean-square error (RMSE) and also lowest value in mean absolute error (MAE). Alternatively, for the five-minute dataset, we found that the auto-regression model showed the least RMSE and MAE; however, this model only predicts short-term PM2.5.

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