Utilizing Exploratory Data Analysis for the Prediction of Campus Placement for Educational Institutions

Nagaria, Jumana; S, Senthil Velan

doi:10.1109/icccnt49239.2020.9225441

Cited by 16 publications

(5 citation statements)

References 9 publications

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“…Employability prediction has been studied over the years using techniques of data mining, machine learning algorithms, and predictive analytics. A study by Nagaria (2020) [17] discusses the importance of using data available in higher education institutions to take decisions for future plans and development of the students. The author has used exploratory data analysis to visualize the trends and patterns observed in the data to enhance future employability among management students.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review on the Employability Prediction Model for the Management Students

Crasta

Shailashri

2023

IJCSBE

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Purpose: The demand for management education is on a constant surge that has resulted in quality check of the students who are employed and will be of value to the organisation and country at large. This paper attempts to explore the literature that will help in understanding the students’ employability based on attributes that are vital to both industry and academia. The employability prediction model will help the management to understand the students’ output quality in terms of being employable or not. It will also help them to improve employability through proper intervention by training and development programs. Methodology: The study is based on the literature of employability models that have been developed using various approaches especially that of machine learning. The published research papers in various journals that were reviewed range from the time period 2006 -2022 have been analysed using ABCD analysis. Findings/Result: The employability models have been developed using machine learning approaches. The necessity of an accurate employability prediction model that serves as a guide to help students and faculty is the need of the hour. Originality Value: Employability models are of great value to the students and institutions as they provide a silver lining in knowing the students’ employability. There are several gaps in terms of building an appropriate model that will serve as a scale to measure the uncertainty that exists in knowing the quality of output produced by management educational institutions. Type of Paper: Review paper.

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Section: Related Workmentioning

confidence: 99%

A Systematic Review on the Employability Prediction Model for the Management Students

Crasta

Shailashri

2023

IJCSBE

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“…A forecast is generated using Random Forest, which may estimate the possibility of putting an understudy in an organization. [4] For classification, it typically takes a majority vote, and for regression, it takes the average of predictions made by individual trees. A Random Forest is essentially a collection of decision trees.…”

Section: ) Random Forestmentioning

confidence: 99%

Using Machine Learning for Campus Placement Prediction

Journal

2023

IJSREM

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Abstract— In the modern world, campus placements play a crucial role in shaping the career trajectories of students and determining the reputation of educational institutions. This study addresses the growing need for accurate and predictive tools in campus placement through the application of machine learning (ML) models. We focus on widely-used ML algorithms that have emerged as powerful tools for predicting fitting technical fields. The research utilizes a comprehensive dataset that incorporates diverse features, including academic performance for technical proficiency. By leveraging these features, this project explores the application of ML algorithms to predict suitable job profiles. By analyzing the academic and technical performance of student's data and identifying relevant features, ML models can learn patterns and relationships that contribute to predict the placement potential of current students, guiding them towards career paths and skill development that align with their strengths and interests. Keywords— Machine Learning, Campus Placement, Random Forest Classification, Logistic Regression, Decision Tree, K-Nearest Neighbour Gradient Boosting Classifier, Predictive Analytics.

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“…Student placement is one of a university's most important objectives. [3] The placements that an institution provides its students are intrinsically related to its repute and annual admissions. As a result, every institution strives arduously to improve each area in command to further its overall objective.…”

Section: Literature Surveymentioning

confidence: 99%

With Ai to Predict and Analyse Campus

2023

IRJMETS

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Student placement is one of a school's most important objectives. The placements a school gives its students are intrinsically related to the institution's status and yearly admissions. Every school laboriously strives to improve their appointment unit Consequently, in direction to promote the institution. Any assistance in this region would have a favourable affect on the institution's ability to locate its students. The school then the students will both profit from this in the long run. In directive to forecast how likely it is that present students would behave, this study will look at info about students from previous years find employment. This form is provided also a technique to arrive at the same conclusion. A company that performs placement prediction provided the statistics for the study, and proper information preparation methods were also applied. The proposed structure is also likened to extra common approaches to classification, like Random Forest and Decision tree, regarding precision, efficacy, and recall. The outcomes demonstrate that the recommended procedure achieves strikingly improved than the alternative methods mentioned. VII.

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Utilizing Exploratory Data Analysis for the Prediction of Campus Placement for Educational Institutions

Cited by 16 publications

References 9 publications

A Systematic Review on the Employability Prediction Model for the Management Students

A Systematic Review on the Employability Prediction Model for the Management Students

Using Machine Learning for Campus Placement Prediction

With Ai to Predict and Analyse Campus

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