Predicting Student Placement Readiness: A Machine Learning Approach Using Coding Activities and Multi-Dimensional Performance Indicators

Year : 2026 | Volume : 03 | 02 | Page :
    By

    Priyanshu Chouhan,

  • Swapnil Borkar,

  • Sonu Gupta,

  1. Research Scholar, Thakur Institute of Management Studies,Career Development & Research Mumbai, Maharastra, India
  2. Research Scholar, Thakur Institute of Management Studies,Career Development & Research Mumbai, Maharastra, India
  3. Assistant Professor, Thakur Institute of Management Studies,Career Development & Research Mumbai, Maharastra, India

Abstract

In the modern information-driven academic world, identifying student employability and placement preparedness has predicted. be made a part and parcel of academic planning and career. development. This study provides a machine learning-based. structure to evaluate and forecast student placement pre-paredness by combining various performance aspects-academic achieve- ment, coding activity, aptitude and behavioral engage-ment metrics. Multi-source was gathered and preprocessed in the study. student information, such as student records (CGPA, attendance), logging time spent and difficulty of the problem solved, and indicators of co-curricular involvement. Advanced preprocessing data generation, feature engineering and tech-niques. The dataset quality and volume were improved by using (CTGAN)s. Various machine learning hypothesis, such as Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), and XGBoost, were implemented and evaluated. These findings showed that the stacking ensemble model (XGBoost, Random Forest, and Gradient Boosting) was the best and had the highest score having F1-score of 0.7044 and cross-validation consistency (± 0.0253). SHAP interpretability it was found that the most influential are co-curricular involve-ment, academic consistency, and technical coding proficiency. selection of placement forecasts. The suggested framework offers a predictable instrument that can be effectively interpreted and is clear. Educational institutions may use to detect at-risk students, track whole-child development, and promote data-driven programs of readiness to place.

Keywords: Placement Readiness Prediction, Educational Data Mining, Coding Activity Analytics, Multi-Dimensional Performance Indicators, Ensemble Learning, XGBoost, SHAP Interpretability, Co-Curricular Engagement, Predictive Analytics in Education

How to cite this article:
Priyanshu Chouhan, Swapnil Borkar, Sonu Gupta. Predicting Student Placement Readiness: A Machine Learning Approach Using Coding Activities and Multi-Dimensional Performance Indicators. International Journal of Education Sciences. 2026; 03(02):-.
How to cite this URL:
Priyanshu Chouhan, Swapnil Borkar, Sonu Gupta. Predicting Student Placement Readiness: A Machine Learning Approach Using Coding Activities and Multi-Dimensional Performance Indicators. International Journal of Education Sciences. 2026; 03(02):-. Available from: https://journals.stmjournals.com/ijes/article=2026/view=248403


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Ahead of Print Subscription Review Article
Volume 03
02
Received 28/04/2026
Accepted 30/06/2026
Published 01/07/2026
Publication Time 64 Days
2

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