Pranav Manjunath

Summary

In this project, we aim to build an application to predict job placements for undergraduate students using PESIT BSC placement data from 2013-2019. The user has to input his student details and the application will display the predicted Firm CTC and Firm Type for the respected student. A special feature of the application is the feedback mechanism. A survey was sent to alumni of PESIT, obtaining their feedback on placements. The survey con- sisted of questions regarding their employee satisfaction, potential career growth, and whether they felt it would be better to go to higher studies or directly go for placements. Based on the predicted CTC and Type of the firm, the model will predict and display the appropriate feedback from the PESIT alumni. This gives university students direct advice from their seniors and along with predicting where the student will be placed. The application uses two main sources of data, PESIT BSC placement dataset and senior’s survey answers. A comparative analysis for various Machine Learning and Deep Learning algorithms have been implemented to decide the most accurate models. The final application uses Random Forest and KNN classification algorithms for classifying and predicting the Student’s placeement. The application consists of a tab regarding exploratory data analysis of placements in PESIT. This tab consists of graphs and tables regarding the number of students who got placed via placements over the years, with respect to Tier, Branch of Engineering. This will help the students identify trends in the placements that occur in PESIT BSC. There are buttons on this page which takes the user to the college department’s homepage.