scholarly journals Data Mining Approach for Educational Decision Support

2021 ◽  
Vol 2 (1) ◽  
pp. 33-44
Author(s):  
Sinta Septi Pangastuti ◽  
Kartika Fithriasari ◽  
Nur Iriawan ◽  
Wahyuni Suryaningtyas
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Classification Accuracy ◽  
Storage System ◽  
Performance Criteria ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Mining Approach ◽  
Educational Decision ◽  
Boosting Algorithm

data mining techniques in education sector have begun to evolve, along with the development of technology and the amount of data that can be stored in an education database storage system. One of them is a database of Bidikmisi scholarships in Indonesia. The Bidikmisi data used in this study will be classified using classification data mining technique. The technique that used in this study is random forest in combination with boosting algorithm and bagging algorithms. These algorithms also combine with SMOTE algorithm to handling the imbalance class in dataset. Based on the performance criteria G-mean and AUC, the algorithm combines with SMOTE tended to be better. The classification accuracy of each method being more than 90%

scholarly journals Salary Estimator using Data Science

2020 ◽  
Vol 6 (12) ◽  
pp. 319-322
Author(s):  
Winner Walecha and Dr. Bhoomi Gupta
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Data Science ◽  
The Internet ◽  
Prediction System ◽  
Lasso Regression ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Number Of Factors ◽  
Using Data

This paper presents a salary prediction system using the job listings from an employment website, in this case Glassdoor.com. A data mining technique is used to generate a model which will scrape number of jobs from the employment website, clean it on the basis of number of factors including the rival companies, revenue and skill required thereby predicting the salary to be expected when applying for a data science job. Techniques like linear regression, lasso regression, random forest regressors are optimised using GridsearchCV to reach the best model. The model can be further extended to build a flask API thus can be deployed on the internet for public usage.

scholarly journals Predicting Diabetes Disease using Random Forest Tree (Rft) Data Mining Technique

2020 ◽  
Vol 8 (4S5) ◽  
pp. 46-48
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Blood Sugar ◽  
Energy Use ◽  
Primary Source ◽  
Organ Damage ◽  
Forest Tree ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Random Forest Tree

Diabetes is a condition that happens when the blood glucose is too high, also known as blood sugar. The primary source of energy is blood sugar, and it comes from the food you eat. Insulin, a pancreatic hormone, helps food glucose get into the cells for energy use. It also leads for an unrelated condition named, "Diabetes Insipidus”, which entails complications with the processing of fluids in the kidney. Insulin is the key to the ability of the cell to use glucose. Problems with the processing of insulin or how cells perceive insulin can easily cause out of control the body's carefully balanced glucose metabolism process [1]. Diabetes emerges when either of these conditions happens, blood sugar levels rise and crash and the risk of organ damage. Earlier prediction of this diabetes condition could provide proper treatment to protect the people from un avoided illness. For this prediction we can apply data mining which is used predominantly in healthcare organizations for decision making, disease detection purpose. In this paper data have been collected from UCI repositories and the data mining tool (WEKA) is used to predict diabetes. In this database there are 768 instances in which 500 instances belongs to tested negative and 268 instances belongs to tested positive. An experimental study is carried out using data mining technique classification technique called Random Forest Tree (RFT) classifier to predict diabetes. In this research, we have used different cross fold validation to achieve better accuracy and we found that cross fold validation k= 8 gives high accuracy 76.69% while compared with other cross fold validation values.

scholarly journals Boosted Regression (Boosting): An Introductory Tutorial and a Stata Plugin

2005 ◽  
Vol 5 (3) ◽  
pp. 330-354 ◽  
Author(s):  
Matthias Schonlau
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Stepwise Logistic Regression ◽  
Data Mining Technique ◽  
Test Dataset ◽  
Mining Technique ◽  
Considerable Success ◽  
Boosting Algorithm ◽  
Gaussian Regression

Boosting, or boosted regression, is a recent data-mining technique that has shown considerable success in predictive accuracy. This article gives an overview of boosting and introduces a new Stata command, boost, that implements the boosting algorithm described in Hastie, Tibshirani, and Friedman (2001, 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regression example, the R2 value computed on a test dataset is R2 = 21.3% for linear regression and R2 = 93.8% for boosting. In the logistic regression example, stepwise logistic regression correctly classifies 54.1% of the observations in a test dataset versus 76.0% for boosted logistic regression. Currently, boost accommodates Gaussian (normal), logistic, and Poisson boosted regression. boost is implemented as a Windows C++ plugin.

scholarly journals Data Mining Approach in Preterm Birth Prediction

2010 ◽  
Vol 9 (1) ◽  
pp. 18-30 ◽  
Author(s):  
Jyothi Thomas ◽  
G. Kulanthaivel
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Preterm Birth ◽  
Field Data ◽  
Risk Groups ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Mining Approach ◽  
Health Field

Data mining refers to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning, pattern recognition, neural networks and database. In particular this paper reveals out how the problem of preterm birth prediction is approached by a data mining analyst with a background in machine learning. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. Data mining technique such as clustering has not been used to predict preterm birth. Hence this paper made an attempt to identify patterns from the database of the preterm birth patients using clustering.

scholarly journals Predict Students' Performance by Using J48 Algorithm

2020 ◽  
pp. 578-582
Author(s):  
Myo Thandar Tun ◽  
Yin Yin Htay
Keyword(s):  
Data Mining ◽  
Classification Accuracy ◽  
Critical Issue ◽  
Academic Community ◽  
Classification Algorithms ◽  
Data Mining Technique ◽  
Data Set ◽  
Mining Technique ◽  
Computer Studies ◽  
Machine Learning Tool

The critical issue to the academic community of higher education is to monitor the progress of students’ academic performance. We can use data mining techniques for this purpose. J48 algorithm is one of the famous classification algorithms present today to generate decision trees in data mining technique. The data set used in this study is taken from University of Computer Studies (Mandalay). Weka machine learning tool is applied to make classification. In this work, we tested result classification accuracy was computed. This J48 classification algorithm give accuracy with 78.2%.

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