scholarly journals Applying Convolutional-GRU for Term Deposit Likelihood Prediction

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Shawni Dutta ◽  
Payal Bose ◽  
Vishal Goyal ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Predictive Model ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Saving Account ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Gated Recurrent Unit

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.

scholarly journals Applying Convolutional-GRU for Term Deposit Likelihood Prediction

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Bandyopadhyay
Keyword(s):  
Predictive Model ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Saving Account ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Gated Recurrent Unit

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

scholarly journals Recommender System for Term Deposit Likelihood Prediction Using Cross-Validated Neural Network

2020 ◽  
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Squared Error ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network(NN) along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and Mean Squared Error (MSE) of 0.1168.

scholarly journals Prediction of Diagnosing Chronic Kidney Disease using Machine Learning: Classification Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 1922-1924
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Renal Disorder ◽  
Machine Learning Classification ◽  
Knn Classifier ◽  
Tree Classifier

Chronic Kidney Disease is a very dangerous health problem that has been spreading as well as growing due to diversification in life style such as food habits, changes in the atmosphere, etc. The branch of biosciences has progressive to a bigger extent and has bring out huge amounts of data from Electronic Health Records. The primary aim of this paper is to classify using various Classification techniques like Logistic Regression (LR), K-Nearest Neighbor (KNN) Classifier, Decision Tree Classifier Tree, Random Forest Classifier, Support Vector Machine (SVM), and SGD Classifier. According to the health statistics of India 63538 cases has been registered on chronic renal disorder. Average age of men and women susceptible to renal disorders occurs within the range of 48 to 70 years.

scholarly journals Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

2021 ◽  
Vol 18 (12) ◽  
pp. 6429
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Malak Aljabri ◽  
Sumayh S. Aljameel ◽  
Mariam Moataz Aly Kamaleldin ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Computational Intelligence ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Detection And Identification ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The World ◽  
Detection And Diagnosis

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

scholarly journals Performance of Naïve Bayes, C4.5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets

2020 ◽  
Vol 9 (3) ◽  
pp. 2193-2197
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Specific Pattern ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Digital Format ◽  
Tree Classifier

Data mining usually specifies the discovery of specific pattern or analysis of data from a large dataset. Classification is one of an efficient data mining technique, in which class the data are classified are already predefined using the existing datasets. The classification of medical records in terms of its symptoms using computerized method and storing the predicted information in the digital format is of great importance in the diagnosis of various diseases in the medical field. In this paper, finding the algorithm with highest accuracy range is concentrated so that a cost-effective algorithm can be found. Here the data mining classification algorithms are compared with their accuracy of finding exact data according to the diagnosis report and their execution rate to identify how fast the records are classified. The classification technique based algorithms used in this study are the Naive Bayes Classifier, the C4.5 tree classifier and the K-Nearest Neighbor (KNN) to predict which algorithm is the best suited for classifying any kind of medical dataset. Here the datasets such as Breast Cancer, Iris and Hypothyroid are used to predict which of the three algorithms is suitable for classifying the datasets with highest accuracy of finding the records of patients with the particular health problems. The experimental results represented in the form of table and graph shows the performance and the importance of Naïve Bayes, C4.5 and K-Nearest Neighbor algorithms. From the performance outcome of the three algorithms the C4.5 algorithm is a lot better than the Naïve Bayes and the K-Nearest Neighbor algorithm.

Land Cover Classification Using the Proposed Texture Model and Fuzzy k-NN Classifier

2018 ◽  
pp. 226-261
Author(s):  
Jenicka S.
Keyword(s):  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Texture Feature ◽  
Image Texture ◽  
Kappa Statistics ◽  
Grey Level ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Texture Model ◽  
Proposed Model

Texture feature is a decisive factor in pattern classification problems because texture features are not deduced from the intensity of current pixel but from the grey level intensity variations of current pixel with its neighbors. In this chapter, a new texture model called multivariate binary threshold pattern (MBTP) has been proposed with five discrete levels such as -9, -1, 0, 1, and 9 characterizing the grey level intensity variations of the center pixel with its neighbors in the local neighborhood of each band in a multispectral image. Texture-based classification has been performed with the proposed model using fuzzy k-nearest neighbor (fuzzy k-NN) algorithm on IRS-P6, LISS-IV data, and the results have been evaluated based on confusion matrix, classification accuracy, and Kappa statistics. From the experiments, it is found that the proposed model outperforms other chosen existing texture models.

scholarly journals Chicago Crime Analysis using R Programming

2019 ◽  
pp. 937-944
Author(s):  
Monish N
Keyword(s):  
Law Enforcement ◽  
Data Science ◽  
Nearest Neighbor ◽  
Crime Rates ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
The Us ◽  
Steep Decline ◽  
R Programming ◽  
And Behavior

In recent years law enforcement have improved by taking better strategies, computer aided technology, efficient use of resource, etc. As a result of these over the couple of years there has been a steep decline in crime rate in the US (United States). Law enforcement have turned to data science for insights (ranging from reports, corrective analysis and behavior modelling). There has been an overall drop in crime rates in Chicago in recent years. In fact, these rates are at the lowest when compared to the previous decades. This paper uses the criminal dataset found at “data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2” to describe historical trends, insights, etc. in Chicago from 1965 to 2018 and not to assign any casual interpretation of the vanguards of crime rates during this period. Here K-Nearest Neighbor (KNN) classification is used for training and crime predication. Discussions on future investigation can also be found. The proposed model has an accuracy of 83.2%.

scholarly journals K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment

2022 ◽  
Vol 25 (1) ◽  
pp. 521
Author(s):  
Omar Freddy Chamorro-Atalaya ◽  
Guillermo Morales Romero ◽  
Adrián Quispe Andía ◽  
Beatriz Caycho Salas ◽  
Elizabeth Katerin Auqui Ramos ◽  
...  
Keyword(s):  
Predictive Model ◽  
Engineering Students ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Operational Characteristic ◽  
K Nearest Neighbor ◽  
Administrative Service

The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.

scholarly journals Heart disease prediction model with k-nearest neighbor algorithm

2021 ◽  
Vol 10 (3) ◽  
pp. 225
Author(s):  
Tssehay Admassu Assegie
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
Nearest Neighbor ◽  
Predictive Performance ◽  
Data Repository ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
K Nearest Neighbor Algorithm ◽  
Learning Data

<span>In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction model. The author conducted an experiment to evaluate the performance of the proposed model. Moreover, the result of the experimental evaluation of the predictive performance of the proposed model is analyzed. To conduct the study, the author obtained heart disease data from Kaggle machine learning data repository. The dataset consists of 1025 observations of which 499 or 48.68% is heart disease negative and 526 or 51.32% is heart disease positive. Finally, the performance of KNN algorithm is analyzed on the test set. The result of performance analysis on the experimental results on the Kaggle heart disease data repository shows that the accuracy of the KNN is 91.99%</span>

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