Predicting students admission with Logistic Regression, Decision Tree, SVM (SVC) and Random Forest

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

A 2-layer Neural network which Predicts whether the student will get admitted to the university or not on the basis of data of previous years.

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The "Student Admission Prediction" project is designed to leverage machine learning techniques to predict the likelihood of student admissions based on various factors such as GRE scores, TOEFL scores, university ratings, statement of purpose (SOP) strength and other relevant features.

The objective is to predict the chances of a student to get admission in a particular university based on its GRE score, TOEFL score , University Rating, Statement of Purpose, Letter of Recommendation.Technology used- Prediction and Classification Using R programming

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This is the basic flask web app implemented for the prediction of Student Admission.

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The goal here is to find the chance of admission of a candidate based on his/her GRE Score (out of 340), TOEFL Score (out of 120), rating of the University (out of 5) in which he/she is trying to get admission, Strength of the SOP (out of 5), strength of the Letter Of Recommendation (out of 5), CGPA (out of 10) and the research experience (0 or 1). Technologies: pandas , Numpy , Matplotlib. Algorithms: Multiple Linear Regression,Django for Deployment

The goal here is to find the chance of admission of a candidate based on his/her GRE Score (out of 340), TOEFL Score (out of 120), rating of the University (out of 5) in which he/she is trying to get admission, Strength of the SOP (out of 5),strength of the Letter Of Recommendation (out of 5), CGPA (out of 10) and the research experience (0 or 1).

The "Student Admission Prediction" project leverages advanced machine learning techniques to predict the likelihood of student admissions based on a variety of influential factors. These factors include GRE scores, TOEFL scores, university ratings, the strength of the statement of purpose (SOP), and other relevant features. By analyzing historical.

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student admission prediction in ML using support vector machine

One input layer and one output layers to get the student admission prediction.

Admission Prediction System: Neural network-based tool for automating student evaluation and improving outcomes.

My project based on Prediction of Admission of Students in colleges based on their rank.

The University Admission Prediction System database is designed to store and manage student application data, university admission requirements, and prediction outcomes. The system leverages historical admission records and machine learning predictions to assess an applicant’s likelihood of acceptance into a chosen university.

UniMap - University Navigation Platform for Kazakhstan students. AI-powered admission predictions, ЕНТ analysis, grant data 2025.

Efficient resource allocation and strategic planning in educational institutions heavily rely on accurate predictions of student admissions. This paper presents a detailed investigation into the application of time series analysis techniques for admission prediction.

I am pleased to upload my project on GitHub, titled "Admission Prediction," which focuses on predicting admission outcomes for prospective students. This project aims to assist students in understanding their chances of admission to a specific educational institution based on various factors.

The Graduate Admission Prediction Using ML involves a systematic process to develop an accurate model for forecasting a student's admission likelihood. Initially, a diverse dataset is collected, encompassing key features like GRE scores, TOEFL scores, university ratings, SOP, LOR, CGPA, and research experience. Training model and making predictions

AdmitRank — A multimodal admissions prediction and ranking system. Train on historical student data (CSV), fuse optional SOP/LOR/CV PDFs, and predict admission chances with explanations, top-K ranking, and charts. Built with Streamlit, scikit-learn, and NLP features.

Python code using selenium to scrape yocket website for student profile data. This data is used for Training Machine Learning Models for Graduate College Admission Prediction Project.

This is a student course prediction system for tertiary institutions, using regression analysis to predict the likely course to be offered based on students performances in required examination (WAEC, UTME, Post UTME) and the pattern of inclination of the schools in giving admissions

This project predicts the chances of a student's admission to a graduate school based on various academic factors. An Artificial Neural Network (ANN) is built using TensorFlow and Keras to perform this prediction.

The aim of the project is determine if a student can gain admission into a university based on the scores of two exams. The prediction is done based on the decision boundary that the logistic regression learns.

he project aims to create a web application tailored for engineering students, offering tools to manage academic work and assistance in selecting overseas colleges. Key features include an interactive chatbot integrated with ChatGPT, access to course notes, project ideas, and a prediction model for estimating students' admission probabilities.