Career Prediction Model Using Data Mining and Linear Classification

Rangnekar, Rucha; Suratwala, Khyati Pradeep; Krishna, Sanjana; Dhage, Sudhir

doi:10.1109/iccubea.2018.8697689

Cited by 17 publications

(2 citation statements)

References 11 publications

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“…The Intuitive Career System was created to predict a career that meets the student's aptitude and personality in [19]. The students' personalities are defined through their social media accounts via Facebook Graph API.…”

Section: Related Work On Predicting and Guiding Graduates' Careersmentioning

confidence: 99%

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

Mahboob¹,

Asif²,

Haider³

2023

Mehran Univ. res. j. eng. technol.

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The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field.

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Section: Related Work On Predicting and Guiding Graduates' Careersmentioning

confidence: 99%

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

Mahboob¹,

Asif²,

Haider³

2023

Mehran Univ. res. j. eng. technol.

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“…The results revealed that these factors should be considered, even though they would predict different careers. Additionally, the model performance of the created intuitive career system was the highest with an accuracy of 77.41%, 75.4%, and 60.09% for aptitude, personality, and background information, respectively [Rangnekar et al (2018)].…”

Section: Related Workmentioning

confidence: 99%

The Effective Prediction Model Based on Employment Positions for Computer Curriculums Using Classification Techniques

Silprachawong¹,

Kaewchuea²,

Srisawat³

2021

INDJCSE

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Data mining techniques were used to build a prediction model prototype based on employment positions for computer majors in the Faculty of Science and Technology, Suratthani Rajabhat University. This study investigated the impact factors of employment positions of computer careers, compared model performances that consisted of six classification techniques and developed a web application using the effective prediction model that could be implemented in four majors using data collected from the graduates of sessions 2016-2020. The results of this research demonstrated that factors of seven courses and cumulative grade point average predicted employment positions. The models with best performances were rule-based and naive Bayes methods with a classification accuracy of 89.66%, precision of 90.48%, and recall of 90.00%. Therefore, a web application of the prediction model was developed using PHP programming. In practice, this application can suitably predict employment positions for computer majors.

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