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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.
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
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.
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.
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 website(Flask web application) predicts the chance of getting placed on-campus based on various parameters like CGPA, backlogs, internships, etc. This website uses a Machine Learning model trained using Random Forest Classification technique. The machine learning model achieved 94% precision and 88% accuracy.