College Placement Analysis: An Interactive Dashboard for Placement Statistics and Readiness Prediction

Author

Dr. T. Esther, Krithik K, Mohamed Ishak J, Muthusukesh M, Praveen D


Abstract

 College placement records are often maintained in spreadsheets, making it challenging to manage student information, review placement indicators, and provide individual guidance efficiently. This paper presents the College Placement Analysis System, a Python-based web application developed using Streamlit, Pandas, and SQLite. The system provides a dashboard summarising key indicators such as total students, placement rate, average package, average CGPA, and department count. Its student record management features support searching, filtering, editing, and deleting records, while a student registration form enables new records to be added with input validation and duplicate student-ID protection. The system includes a rule-based placement-readiness predictor that calculates an indicative score from student academic performance, skill scores, internship status, and backlogs. A career guidance module recommends potential job roles and identifies areas where a student may need further improvement. Student data can be imported from CSV or Excel files and exported as CSV, while SQLite provides persistent storage for student records. The system was tested with a sample dataset of 500 student records and through functional checks of its main operations. The readiness score is a weighted estimate rather than a machine-learning prediction, and the sample dataset is intended to demonstrate system functionality rather than represent actual placement outcomes. The proposed system offers a centralised and practical way to manage placement information and support students with accessible analysis and career guidance.  


Keywords

College Placement Analysis, Streamlit, Python, SQLite, Student Record Management, Placement Readiness, Career Guidance, CSV and Excel Data Handling.



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References


  1. Khodaskar, M., Gajare, A., Bochare, C., & Patil, S. (2025). AI-driven placement prediction and recommendation system for college students. International Research Journal of Engineering and Technology, 12(10).  
  2. Kumar, A. D. P., Kuchhadia, V., Charan, G., Sreeja, G., & Pavan, N. (2025). Predicting student employability using machine learning: A comparative study of classification algorithms. In Proceedings of the International Conference on Computer Science and Communication Engineering (ICCSCE 2025). https://www.atlantis-press.com/proceedings/iccsce-25/126017440 Atlantis Press.
  3. Pathak, A., Matcha, M., Gopisetti, M., & Joshi, S. (2025). A machine learning framework for predicting student placement outcomes. Ingénierie des Systèmes d’Information, 30(7), 1715–1721.https://iieta.org/node/21236
  4. Rao, K. E., Terlapu, P. R. V., Salakapuri, R., Ramakrishna, G., Pemula, R., & Rao, D. P. (2025). Student placement performance analysis and prediction systems at higher education institutions in South India region: A machine learning approach with ranked features. U.Porto Journal of Engineering, 11(2), 183–219. https://ijooes.fe.up.pt/index.php/upjeng/article/view/2183-6493_0011-002_002916 
  5. Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H.-Y., & Hussain, A. (2023). Educational data mining to predict students’ academic performance: A survey study. Education and Information Technologies, https://doi.org/10.1007/s10639-022-11152-y 28(1), 905–971.  
  6. Bin Roslan, M. H., & Chen, C. J. (2022). Educational data mining for student performance prediction: A systematic literature review (2015–2021). International Journal of Emerging Technologies in Learning, https://doi.org/10.3991/ijet.v17i05.27685 17(5), 147–179.  
  7. Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9, Article 11. https://doi.org/10.1186/s40561-022-00192-z  
  8. Khan, A., & Ghosh, S. K. (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1), 205–240. https://doi.org/10.1007/s10639-020-10230-3
  9. Maurya, L. S., Hussain, M. S., & Singh, S. (2021). Developing classifiers through machine learning algorithms for student placement prediction based on academic performance. Applied Artificial https://doi.org/10.1080/08839514.2021.1901032 Intelligence, 35(6), 403–420.
  10. Harihar, V. K., & Bhalke, D. G. (2020). Student placement prediction system using machine learning. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(Special Issue 2). https://doi.org/10.18090/samriddhi.v12is2.17 
  11. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), Article e1355. https://doi.org/10.1002/widm.1355 
  12. Srinivas, C. K., Yadav, N. S., Pushkar, A. S., Somashekar, R., & Sundeep, K. R. (2020).  Students placement prediction using machine learning. International Journal for Research in  Applied Science and Engineering Technology, 8(5). https://doi.org/10.22214/ijraset.2020.5466
  13. Francis, B. K., & Babu, S. S. (2019). Predicting academic performance of students using a hybrid data mining approach. Journal of Medical Systems, 43(6), Article 162. https://doi.org/10.1007/s10916-019-1295-4  
  14. Khandale, S., & Bhoite, S. (2019). Campus placement analyzer: Using supervised machine learning algorithms. International Journal of Computer Applications Technology and Research, 8(9), 358–362.  
  15. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x 
  16. Thangavel, S. K., Bharathi, P. D., & Sankar, A. (2017). Student placement analyzer: A recommendation system using machine learning. In 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICACCS.2017.8014632 
  17. Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157  
  18. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6),  601–618. https://doi.org/10.1109/TSMCC.2010.2053532 
  19. Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17. https://doi.org/10.5281/zenodo.3554657 
  20. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861– 874. https://doi.org/10.1016/j.patrec.2005.10.010

 

 

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