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.

Placement prediction by Artificial Neural Networks. Final Year Bachelor's Project.

Placement Prediction using Machine Learning Models.

Campus Placement Prediction & Management System

This is my final year project. This repository contains everything that you will need for your final project (PPT, Report, Website)

Welcome to the Linear Regression Repository! This repository is dedicated to providing a comprehensive collection of resources and code examples for two types of linear regression: Simple Linear Regression and Multiple Linear Regression.

A machine learning-based system using NLP to automate resume screening and predict placement suitability. It extracts key features and applies KNN for accurate, data-driven recruitment decisions.

This app utilizes machine learning to predict student placement outcomes based on CGPA, IQ, and Profile Score, aiding both students and institutions in crucial placement decisions.

This project on placement prediction integrates machine learning with database management using MySQL for user authentication. The project involves data preprocessing, feature engineering, and the implementation of supervised learning techniques to train the model.

Student Performance Portfolio WebApp

A machine learning project that predicts student placement outcomes based on academic and personal features. Trained using real-world data to help forecast job placement chances accurately.

This project uses the Kaggle competition dataset from the "ML with Python Course Project" to predict campus recruitment outcomes. It includes preprocessing, EDA, feature engineering, and model training using classification algorithms such as Logistic Regression, Decision Tree, and K-Nearest Neighbors.

The project aims to analyze past placement data, uncover factors affecting success, and develop a machine learning model to predict future placement outcomes. Through this, we aim to gain insights and build a reliable model for accurately forecasting candidate placements.

A Streamlit web app that predicts student placement chances using an AdaBoost classifier, based on academic scores, certifications, and soft skills. Integrated with MySQL for result logging.

Tensorflow Sequential Model to predict the placement probability percentage of a student

PLACEMATE is a helping tool for engineering students who wants to predict their placement possibility and evaluate themselves. It can also generate professional resume for a student in PDF format

🎯 CareerPredict is your smart career assistant! Just upload your 📄 resume — it auto-fills forms, analyzes your ATS score 🤖, matches you with job descriptions 💼, and gives real-time placement insights 📊 — all in an interactive and engaging way! 🚀✨