Placement prediction using machine learning is a technique that analyzes data from past student placements to forecast future job prospects. It uses factors like grades, skills, and experience to estimate the likelihood of a student getting hired. This helps students and institutions better prepare for the job market.

Student Campus-Placement Prediction ML Project using Python

"Student_Placement_Prediction_Web_App" that focuses on a web application for predicting student placements using machine learning algorithms like Random Forest Classification , Logistic Regression and using Streamlite give an web interference

student placement package prediction by using regression analysis

Prediction model for Predicting Student's Placement (Machine Learning Project)

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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A Classification ML model for predicting the placement of students depending on his/her percentage, streams, and work experience.

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Machine Learning project that predicts student placement outcomes based on academic performance, employability test scores, and work experience using classification models.

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This project is related to Student Campus-Placement Prediction

Prediction student's placement problems using binary perceptron

A machine learning-powered placement prediction system that forecasts campus recruitment outcomes based on academic, technical, and behavioral student data. Built with classification models and deployed using Streamlit, it also provides a personalized Placement Readiness Score and actionable profile improvement tips.

📊 End-to-end data analytics & machine learning project to analyze student performance and predict campus placement outcomes using Python.

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Student placement prediction using ML with python

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The Campus Placements Prediction Web App is a tool designed to predict the likelihood of a student getting placed during campus placements based on various factors such as academic performance, skills, and other relevant data. This web app is trained and works as per the given dataset this can be easily modified based on the needs of recruiter.

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Student placement prediction system

prediction of student placement

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Predicts college student placement based on academic performance, skills, and experience. Includes preprocessing, EDA, and ML modeling to analyze key factors like CGPA, internships, and aptitude scores to improve placement outcome insights.

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