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Predictive Analysis of Campus Recruitment Outcomes: An Integrated Placement and Salary Estimation Model using Random Forest

Author

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  • Pranay Rapartiwar
  • Sanket Agade
  • Ashwini Mirge
  • Janvi Wakde
  • Sumit Muddalkar

Abstract

In the current academic scenario, campus placement is an essential criterion for the success of the academic institution as well as the students. Several predictive models for the status of the students' placement have been proposed. However, the current scenario lacks an integrated model for the simultaneous prediction of the potential salary range. This paper proposes a smart system named PlaceSight that predicts the students' placement as well as the potential salary range. The model for the prediction of the students' placement is based on the Random Forest algorithm. The model has achieved an accuracy of 93.3%, precision of 0.93, and ROC_AUC value of 0.94. The model for the prediction of the potential salary range has been implemented with the Random Forest Regressor algorithm. The model has achieved a Mean Absolute Error value of 10,816, R2 Score value of 0.89, and Mean Squared Error value of 6,332,864,171. The proposed model is capable of performing the entire data preprocessing as well as Exploratory Data Analysis. The proposed model is implemented with a Flask framework.

Suggested Citation

  • Pranay Rapartiwar & Sanket Agade & Ashwini Mirge & Janvi Wakde & Sumit Muddalkar, 2026. "Predictive Analysis of Campus Recruitment Outcomes: An Integrated Placement and Salary Estimation Model using Random Forest," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 13(2), pages 218-224, April.
    • Handle: RePEc:etm:ijsrst:v13:y2026:i2:id:1442
    DOI: 10.32628/IJSRST261324
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