Prediction of Final Result and Placement of Students using Classification Algorithm

Naik, Neelam; Purohit, Seema

doi:10.5120/8945-3111

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…While data mining techniques such as clustering, decision trees, and association are applied to higher education processes, this can help to improve student performance, life cycle management, course selection, retention rate, and grant fund management. Seema Purohit and Neelam Naik [4]. Quality higher education is required for the country's growth and development.…”

Section: IImentioning

confidence: 99%

Stream Processing for Association Rule to Generate Student Dataset using Apriori Algorithm

Ghodke¹

2022

IJRASET

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Analytical techniques have been used for many years to analyse and predict academic achievement from various perspectives. One of the most challenging problems for higher education is predicting students' paths through the education system. Many factors influence successful student outcome prediction in the early course stage. Apriori algorithm techniques use a variety of methods to find out and collect based on stored data patterns student information. Colab and Python applications are used in this project to predict each student based on characteristics in the given dataset. Each student's information is included in the dataset. Because it arrives as it is being created, received real-world data is referred to that as streaming data.

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Section: IImentioning

confidence: 99%

Stream Processing for Association Rule to Generate Student Dataset using Apriori Algorithm

Ghodke¹

2022

IJRASET

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“…Prathipa and Sekaran [32] predicted the placement results of the students using Bayesian classification. Naik and Purohit [33] predicted the result of the students along with the placement of the students with the help of classification algorithms. Tripti Mishra et al [34] predicted students' employability using data mining techniques.…”

Section: Related Workmentioning

confidence: 99%

Cognitive and academic-based probability models for predicting campus placements

Alfana

Jammalamadaka

Prakash

et al. 2022

IJ-AI

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<span>Industrial organizations select the students for placement by conducting tests based on the academic content and targeting students' cognitive levels, such as the problem-solving ability. Educational institutes are mostly dependent on the students' academic performance to judge the likelihood of Employing the students. Cognitive and academic-based models are required to accurately predict the students' employment and assess the areas of improvement required. The interrelationships must be established to achieve coherence between the models. In this paper, three predictive models have been presented, which are based on: cognitive factors, Academic factors with and without anomaly correction. The models will help the educational institutions prepare the students for the highest number of placements. The models provide the basis for prediction on the individual subject/factor basis and the overall prediction considering all the subjects/cognitive factors. 98% accuracy in predicting the placement of the students has been achieved considering both the cognitive and Academic models with a built-in anomaly correction mechanism. The anomaly correction mechanism presented in the paper improved the accuracy of prediction from 92% to 98%. The positive correlation between the cognitive and Academic model helps inferencing one model from the other.</span>

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“…T. Jeevalatha et al [30] performed detailed analysis of undergraduate students on placement selection by using algorithms of Decision Tree. NeelamNaik et al [31] predicted the final result of the students along with the placement of the students with the help of classification algorithms. Savita Bakare et al [32] applied Fuzzy logic and K Nearest Neighbor on the students' database to predict the campus placement.…”

Section: Job Predictionmentioning

confidence: 99%

An Expert System to build Cognitive Model of an IT Student using Artificial Neural Networks for Predicting Placements during Campus Recruitments

Khatoon¹

2020

IJETER

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Careers that suits students' needs to be predicted and the students counselled to opt for those suitable careers. An under-graduate student of IT studies has to choose many courses to complete his/her under-graduate program. But, there arevery few courses among them that influence the probability of getting placement during campus drives. Career prediction system needs to consider different aspects related to cognition of the student. An expert system is required for prediction.In this paper, considering the influencing courses an Expert System that uses cognitive metrics is proposed and presented to build a Cognitive Model of a student. The Expert system trains an artificial neural network using the cognitive model and predicts the probability of getting placements for a student in the Target Year.

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Prediction of Final Result and Placement of Students using Classification Algorithm

Cited by 12 publications

References 6 publications

Stream Processing for Association Rule to Generate Student Dataset using Apriori Algorithm

Stream Processing for Association Rule to Generate Student Dataset using Apriori Algorithm

Cognitive and academic-based probability models for predicting campus placements

An Expert System to build Cognitive Model of an IT Student using Artificial Neural Networks for Predicting Placements during Campus Recruitments

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