Assessment of feature selection for student academic performance through machine learning classification

Suguna, R.; Devi, M. Shyamala; Bagate, Rupali Amit; Joshi, Aparna

doi:10.1080/09720510.2019.1609729

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…Regression surveys create conditions for describing the measurable link between at least one indicator factor and the reply variable (Suguna et al 2019). The p value for each term tests the invalid hypothesis, that is, the coefficient is equivalent to zero.…”

Section: Logistic Regression and P-value Interpretation: Backward Elimination (Feature Selection)mentioning

confidence: 99%

Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms

Aggrawal¹

2021

TURCOMAT

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Background: Early speculation of cardiovascular disease can help determine the lifestyle change options of high-risk patients, thereby reducing difficulties. We propose a coronary heart disease data set analysis technique to predict people’s risk of danger based on people’s clinically determined history. The methods introduced may be integrated into multiple uses, such for developing decision support system, developing a risk management network, and help for experts and clinical staff. Methods: We employed the Framingham Heart study dataset, which is publicly available Kaggle, to train several machine learning classifiers such as logistic regression (LR), K-nearest neighbor (KNN), Naïve Bayes (NB), decision tree (DT), random forest (RF) and gradient boosting classifier (GBC) for disease prediction. The p-value method has been used for feature elimination, and the selected features have been incorporated for further prediction. Various thresholds are used with different classifiers to make predictions. In order to estimating the precision of the classifiers, ROC curve, confusion matrix and AUC value are considered for model verification. The performance of the six classifiers is used for comparison to predict chronic heart disease (CHD). Results: After applying the p-value backward elimination statistical method on the 10-year CHD data set, 6 significant features were selected from 14 features with p <0.5. In the performance of machine learning classifiers, GBC has the highest accuracy score, which is 87.61%. Conclusions: Statistical methods, such as the combination of p-value backward elimination method and machine learning classifiers, thereby improving the accuracy of the classifier and shortening the running time of the machine.

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Section: Logistic Regression and P-value Interpretation: Backward Elimination (Feature Selection)mentioning

confidence: 99%

Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms

Aggrawal¹

2021

TURCOMAT

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“…A critical review on various feature selection, feature extraction methods, classification methods and the performances parameters are examined for predicting the wine quality [6]- [10].…”

Section: A Literature Reviewmentioning

confidence: 99%

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

2019

ijeat

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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.

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“…The model was created to develop each wine product and the standardization of the product is done based on the benchmark survey level of the wine consumers and the prediction is done with the benchmark data [7]. The application of Feature Selection and Feature Extraction on wine data set is done to predict the target [8]- [12].…”

Section: A Literature Reviewmentioning

confidence: 99%

Composite Model Fabrication of Classification with Transformed Target Regressor for Customer Segmentation using Machine Learning

2019

ijeat

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In Current internet world, the customers prefer to buy the products through online rather than spending their time on show rooms. The online customers of wine increases day by day due to the availability of high brands in online sellers. So the customers buy the wine products based on the product description and the satisfaction of other customers those who have bought before. This makes the industries to focus on machine learning that concentrates on target transformation of the dependent variable. This paper endeavor to forecast the customer segmentation for the wine data set extracted from UCI Machine learning repository. The raw wine data set is subjected to target transformation for various classifiers like Huber Regressor, SGD Regressor, RidgeCV Regression, Logistic RegressionCV and Passive Aggressive Regressor. The performance of the various classifiers is analyzed with and without target transformation using the metrics like Mean Absolute Error and R2 Score. The implementation is done in Anaconda Navigator with Python. Experimental results shows that after applying target transformation RidgeCV Regression is found to be effective with the R2 Score of 82% and Mean Absolute Error of 0.0 compared to other classifiers.

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Assessment of feature selection for student academic performance through machine learning classification

Cited by 9 publications

References 10 publications

Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms

Elimination and Backward Selection of Features (P-Value Technique) In Prediction of Heart Disease by Using Machine Learning Algorithms

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

Composite Model Fabrication of Classification with Transformed Target Regressor for Customer Segmentation using Machine Learning

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