This project predicts a student’s chance of admission into graduate programs using machine learning. By analyzing factors like GRE, TOEFL, CGPA, and research experience, it provides a probability score that helps students understand where they stand. The model is deployed with Streamlit, making it easy to interact with and get instant predictions.

The main aim of every academia enthusiast is placement in a reputed MNC’s and even the reputation and every year admission of Institute depends upon placement that it provides to their students. So, any system that will predict the placements of the students will be a positive impact on an institute and increase strength and decreases some workload of any institute’s training and placement office (TPO). With the help of Machine Learning techniques, the knowledge can be extracted from past placed students and placement of upcoming students can be predicted. Data used for training is taken from the same institute for which the placement prediction is done. Suitable data pre-processing methods are applied along with the feature selections. Some Domain expertise is used for pre-processing as well as for outliers that grab in the dataset. We have used various Machine Learning Algorithms like Logistic, SVM, KNN, Decision Tree, Random Forest and advance techniques like Bagging, Boosting and Voting Classifier Nowadays Placement plays an important role in this world full of unemployment. Even the ranking and rating of institutes depend upon the amount of average package and amount of placement they are providing. So basically, main objective of this model is to predict whether the student might get placement or not. Different kinds of classifiers were applied i.e., Logistic Regression, SVM, Decision Tree, Random Forest, KNN, AdaBoost, Gradient Boosting and XGBoost. For this all over academics of students are taken under consideration. As placements activity take place in last year of academics so last year semesters are not taken under consideration

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🎓 Graduate Admission Chances Prediction – A Machine Learning model that predicts the probability of a student getting admitted to a university based on GRE score, TOEFL score, CGPA, SOP, LOR, and research experience. Built with Regression models, Scikit-Learn, Pandas, and Python.

This repository shows ML Model to predict the Admission chances of a student based on some given features. The model is build using the best algorithm out of 4 popular Machine learning algorithms and the best was KNN Algorithm with an accuracy of ~95%. The dataset includes 7 key features for prediction such as GRE Score , University rating, etc.

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Graduate Admission Prediction Using Machine Learning - Helping Students in Shortlisting Universities with Their Profiles.

A data-driven Student Admission Dashboard built with Streamlit and Machine Learning. Features include graphical cutoff trends, placement stats, college comparisons, and admission predictions to guide students in selecting the best-fit colleges based on MHTCET/JEE results.

A college admission prediction web app using machine learning to forecast admission probabilities based on students' ranks and categories. Built with a responsive frontend (HTML, CSS, Bootstrap, JavaScript) and a Python-based backend (Flask, Django) for seamless data handling and predictions.

Graduation admission prediction uses data like test scores, GPA, work experience, and other factors to forecast a student’s chance of being admitted. With machine learning, patterns are identified to provide accurate insights, helping students and universities make informed decisions.