Efficient Student Profession Prediction Using XGBoost Algorithm

Vignesh, A.; Selvan, Tirumarai; Krishnan, Gautham; Sasikumar, Arun; Kumar, Vaibhav

doi:10.1007/978-3-030-32150-5_15

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…In their research, Vignesh et al, (2020) conducted a study using XGBoost on a vast dataset comprising various aspects such as students' academic ability, competition involvement, programming languages, and personal interests to predict their future career paths. The researchers collected the data from multiple platforms, namely Twitter, Kaggle, Google Forms (and more), and consolidated this information into 15 distinct profession labels inhabiting the target variable, along with 36 other parameters characterizing the students for the model.…”

Section: Student Career Prediction Using Xgboost Decision Tree Classi...mentioning

confidence: 99%

Career path prediction using XGBoost Model and students’ academic results

Nguyen,

et al. 2023

CTU J. of Inn. & Sus. Dev.

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This paper proposes an approach for constructing a system for career prediction by applying the eXtreme Gradient Boosting (XGBoost) Decision Tree model to the academic results of Ho Chi Minh International University’s School of Computer Science and Engineering graduates in the past 5 years. Initially, the dataset is cleaned up and normalized to be usable for the prediction algorithm with the help of Python 3 programming language. It is then split into 2 subsets: one for training (80 percent) and the other for testing (20 percent). After that, the algorithm uses the training subset to build the classification model. Finally, the testing subset is loaded into the model to predict each student’s career path based on the respective inputs and hyper-parameters tuning is employed to boost the model’s accuracy. By utilizing this solution, the problem related to predicting students’ future career paths based on their performance throughout their years studying at the university can be adequately addressed and handled.

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Section: Student Career Prediction Using Xgboost Decision Tree Classi...mentioning

confidence: 99%

Career path prediction using XGBoost Model and students’ academic results

Nguyen,

et al. 2023

CTU J. of Inn. & Sus. Dev.

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“…XGBoost [15] is an ensemble machine learning algorithm, XGBoost means extreme Gradient Boosting and XGBoost is used for both regression and classification problems. XGBoost uses the boosting technique; it builds a strong classifier by combining number of weak classifiers.…”

Section: Xgboostmentioning

confidence: 99%

Early Detection of Breast Cancer Using Ensemble Machine Learning Algorithm

Sainikhileaswar

Parthasarathy

2020

Intelligent Systems and Computer Technology

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As showed by World Health Organization (WHO), Breast cancer growth is the most incessant disease among ladies, 627,000 ladies died due to breast cancer in the year 2018, which implies about 15 present of all cancer deaths among ladies. In order to improve breast cancer results and endurance, early recognition is significant. For detecting breast cancer growth early for the most part AI techniques are used. Right now proposed flexible outfit utilizing ensemble procedure for examining breast cancer using Wisconsin Breast Cancer (WBC) dataset. The purpose of the introduced paper is to consider and explain how different classification algorithms works on our dataset and to identify best algorithm for outfit models, for example, Random Forest Voting Ensemble and XGBoost work and how ensemble systems make the performance of the predictive models better by improving their precision and accuracy.

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“…If the camera is in a modified setting, self-modify, auto introduction, and customized white altering counts (all in all implied as "A*") will have identified the scene edge content, quality level, and illuminant concealing and have adjusted the relating camera get parameters before the shade get has been crushed. Demosaicking [13] is the most intriguing movement performed by automated cameras.…”

Section: Hvs System To Rgb Formatmentioning

confidence: 99%

An Efficient Data Hiding Approach on Digital Color Image

Sreesubha¹,

Babu²,

Vijayakumar³

et al. 2020

Intelligent Systems and Computer Technology

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A concealing picture is worked of various assortments of a comparative scene, one for each repulsive section. These dim level picture gives the gathering of light, each at the unequivocal awful part and at the circumstance of every pixel. Red, Green and Blue are three frightful gatherings. Each image is created of three diminish measurement pictures, three gatherings.We’ll conceal the substance in diminish pictures. Using Image improvement methodology, when we change the power estimations of basic tones by then the disguised substance is undeniable. From the preliminary outcomes we get 96% exactness

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Efficient Student Profession Prediction Using XGBoost Algorithm

Cited by 6 publications

References 1 publication

Career path prediction using XGBoost Model and students’ academic results

Career path prediction using XGBoost Model and students’ academic results

Early Detection of Breast Cancer Using Ensemble Machine Learning Algorithm

An Efficient Data Hiding Approach on Digital Color Image

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