Higher education institutions view the placement procedure as a major concern. The placement variables have a tremendous influence on both the names of the schools and the people who choose to attend them. As a result, educational institutions such as colleges and universities work very hard to improve the programs that they offer to students in order to help them find work. The main goal of this article is to assess the previous academic year's student placement statistics, speculate on what the placement outcomes would be like for current students, and offer some suggestions on how the educational system might improve its placement rate. This study provides a strategy to help educational institutions choose applicants for admission. You can predict how well a company will perform in the future if you successfully place a student and then use data from previous students who have been put in the same firm. Machine learning employs a number of classification algorithms, including the Naive Bayes Classifier and the K-Nearest Neighbors (KNN) approach. Both of these algorithms categorize data. The algorithms create their own predictions about the outcomes, and the dataset is used to assess how well the algorithms did their jobs. A corporation's positioning division may use the aforementioned framework to find applicants for employment and aid those individuals in focusing their education on the development of their technical and social talents