This project aims to develop a machine learning model for predicting the likelihood of admission for graduate school applicants based on various factors such as their academic performance, standardized test scores, work experience, and other personal attributes. The model will be trained using a large dataset of past applicants and their admission outcomes and will utilize advanced algorithms and techniques to identify patterns and correlations between different features and admission decisions .The project seeks to provide a reliable and accurate tool for admissions committees to evaluate and select the most qualified candidates for graduate programs, while also helping prospective students to better understand their chances of acceptance and make more informed decisions about their academic and career goals. Student admission problems are very important in educational institutions. This paper addresses machine learning models to predict the chance of a student to be admitted to a master's program. This will assist students to know in advance if they have a chance to get accepted. The machine learning models are Linear Regression, Multiple linear regression, Decision Tree Regressor, K Nearest Neighbor, Random Forest Regressor, Python, Flask, Web technologies.

This project aims to develop a machine learning model for predicting the likelihood of admission for graduate school