Predictive Analysis of Campus Placement of Student Using Machine Learning Algorithms

In today's competitive job market, student campus placement process becomes very important role in the academic journey of students and the reputation of educational institutions alike. As the demand for skilled professionals continues to rise, the ability to accurately predict and optimize student placement outcomes becomes increasingly essential. This research paper proposes a machine learning algorithms analysis to forecast the campus placement of students with the help of various academic factors. The dataset contains historical placement records of students received from open-source repository named as kaggle. It includes information such as student academic achievements, internship, project, workshop, soft skill, extracurricular activities, and placement outcomes. Through data preprocessing and feature engineering, we transform raw data into a structured format suitable for predictive modeling. One hot encoding (Dummy encoding) applied on dataset to represent categorical variables as numerical values to provide more information to the model about the categorical variables. Predictive analysis work on various machine learning algorithms, including logistic regression, decision