Student Campus-Placement Prediction ML Project using Python

This project is related to Student Campus-Placement Prediction

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.

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.

Placement Prediction to predict the student of BE getting placement or not during their Campus Placement.

Full-stack Smart Campus System with role-based access (Student, Faculty, Admin), real-time analytics, performance insights, and placement prediction using Flask + PostgreSQL.

Campus placement prediction is the process of using machine learning (ML) techniques to predict whether a student will be placed in a job during campus recruitment drives. This prediction helps colleges, students, and recruiters understand the chances of getting hired based on various factors.

This project is a Campus Placement Prediction Flask App, designed to help students predict their likelihood of getting placed on campus. The application utilizes a machine learning model, specifically a Random Forest classifier, to analyze input data and provide predictions.

ML classification project that predicts student campus placements based on a dataset of academic and demographic features. This project involves data preprocessing, model training, and evaluation to provide insights into the factors influencing placement outcomes and to enhance prediction accuracy.

A Streamlit web app for predicting campus job placement of students using Random Forest. Enter academic and profile details to get Placed/Not Placed prediction. Model trained on Indian Engineering college dataset. Easy to use, open-source!

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Master's College Project

Student Campus-Placement Prediction ML Project using Python

Student Campus-Placement Prediction ML Project using Python

Campus placement prediction (Campus Placement Portal), is a comprehensive web application designed to streamline the campus placement process for educational institutions. It serves as a centralized platform connecting students and faculty/Training and Placement Officers (TPOs) to manage placement drives and student profiles effectively.

Campus Placement prediction is a Machine Learning model built to predict whether a student gets placement in the campus based on the various marks obtained by the students.

Campus placement prediction leverages data analysis and machine learning to estimate a student’s chances of securing a job during campus recruitment.

Campus Placement prediction is a Machine Learning model built to predict whether a student gets placement in the campus based on the various marks obtained by the students in SSC Level, degree level, MBA ..

Campus Recruitment Prediction using Logistic Regression predicts the likelihood of a student getting selected in campus placements based on academics, skills, internships, and extracurricular activities. The model classifies candidates as “selected” or “not selected,” helping students and colleges improve placement outcomes.

The Placement Prediction System is an Machine-Learning based application designed to analyze students' academic records, skills, and other factors to predict their chances of securing campus placements. It helps students understand their employability and assists institutions in improving placement strategies.

The "Campus Placement Prediction" project predicts students' chances of securing placements based on their academic and extracurricular data. Utilizing machine learning algorithms like Logistic Regression and Random Forest, the project builds a predictive model to evaluate student profiles.

This project involves the prediction of student placement via campus recruitment using classical machine learning methods. Here, the output is a binary class: "Placed" or "Not Placed".

Campus Placement Prediction Using Data Science analyzes student academic and skill-related data to predict placement outcomes. By applying data preprocessing, exploratory analysis, and machine learning models, the project identifies key factors affecting employability and supports data-driven placement and training decisions.

This project is an intern project associated with iNeuron PVT Ltd, it includes prediction for the students whether they will be getting an on-campus placement or not.

Full-stack web application for managing campus recruitment. Provides portals for students to create and manage profiles, companies to post jobs and review applicants, and administrators to control the placement workflow. Includes AI-based resume feedback and placement readiness prediction to support students during recruitment.

The Campus Placement Prediction System is a full-stack, machine-learning–driven application designed to predict whether a student is likely to be placed during campus recruitment based on academic consistency, behavioral patterns, lifestyle habits, and skill readiness.

Student Employability & Salary Prediction ML models predict placement (84% accuracy) and salary (7.5% error). Work experience boosts placement by 31%. Key factors: academic performance for jobs, experience for salary. Based on campus, industry & survey data. Saudi Arabian context.

The Campus Placement Prediction project is a machine learning-based application built using Python and the Django framework. The goal of this project is to predict whether a student is likely to be placed during campus recruitment based on their academic and personal data.

Campus Placement Prediction System- It is a Machine Learning Project using Linear Regression model that predicts whether the student will be placed or unplaced according to his marks scored in graduation, AMCAT, Internships and many more.

The Online Placement Prediction System, where student can estimate their chances of securing on-campus placements, considering several parameters such as Stream, CGPA, Internship, Backlogs and more. This website uses a Machine Learning model trained using Decision Tree Classification technique. The model achieved 97% precision and 86% accuracy.