"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

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.

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 ML-based Placement Prediction system that forecasts a student's job placement chances using academic and aptitude data. Built with a classification model, it provides real-time predictions via a Streamlit web app, helping students, educators, and placement officers assess placement probability quickly."

A machine learning–based placement prediction model that analyzes student IQ and CGPA to determine placement likelihood. The system applies supervised classification techniques and demonstrates end-to-end ML workflow including data preprocessing, model training, evaluation, and prediction.

Job placement prediction using supervised machine learning algorithms. This project analyzes student academic records, skills, experience, and interview performance to predict placement status using classification models like Logistic Regression, Random Forest, and XGBoost.

Student Placement Prediction using Machine Learning. Analyzes academic performance, skills, and experience data to identify factors influencing student placements and build predictive models. Includes exploratory data analysis, data preprocessing, feature analysis, and classification models built using Python and Scikit-learn.

Job Placement Prediction analyzes student academic and skill data to predict placement outcomes. The project uses data cleaning, EDA, feature encoding, and classification models to identify key employability factors such as academic performance, specialization, test scores, and work experience using Python.

The "Student Performance Prediction Project with ML" utilized machine learning techniques to analyze factors influencing student scores. It developed accurate regression and classification models for predicting future academic performance and placements. This data-driven project enhances student success by providing valuable insights .

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.

This project develops a Random Forest classification model to predict student placement outcomes. It uses academic performance, technical skills, and personal attributes to determine whether a student is likely to be placed. The project demonstrates the application of an ensemble learning method for prediction tasks.

Developed an ML-based Student Placement and Salary Prediction system to analyze academic and employability factors. Built classification and regression models using Logistic Regression and Random Forest, evaluated performance with accuracy and R² metrics, and deployed an interactive web app using Streamlit for real-time predictions.

Developed an ML-based Student Placement and Salary Prediction system to analyze academic and employability factors. Built classification and regression models using Logistic Regression and Random Forest, evaluated performance with accuracy and R² metrics, and deployed an interactive web app using Streamlit for real-time predictions.

End-to-end ML pipeline predicting student placement & salary. Features data preprocessing, scaling, and PCA dimensionality reduction. Uses Logistic Regression for classification, evaluated via confusion matrices & metrics. Includes a real-time prediction interface, demonstrating a complete workflow from raw data to deployment-ready insights.