“添翼杯”人工智能创新应用大赛-智慧教育赛道

Student Exam Performance Prediction project to provide an intelligent way of predicting a student's math score based on demographic factors and their past academic performance in reading and writing. It leverages a machine learning model trained on historical student data

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Predicts the exam scores of fictitious students using Multiple Linear Regression

Student exam score prediction using React + Vite as well as Python - Streamlit.

Predicting student exam performance using Lasso Regression on academical and behavioral data.

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This project leverages machine learning regression techniques to predict student exam scores based on features such as hours studied, previous exam scores, and attendance records. Using the Student Performance Dataset from Kaggle, the project explores data preprocessing, feature engineering etc

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Student Exam Score Prediction using Deep Learning

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student exam score prediction using multiple ml models

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Predict, analyze, and visualize student performance using Random Forest & Streamlit — from data to deployment.

Students Exam Score Prediction Project based on student habits & other features.

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Student Exam Performance Prediction is a machine learning project that aims to forecast a student’s final exam score based on various influencing factors.

Student Exam Score Prediction using Machine Learning. The project analyzes factors such as hours studied, attendance, previous scores, and parental involvement to predict exam scores. A Linear Regression model is trained and deployed through a Streamlit web app where users can input student details and get real-time score predictions.

Developing an exam performance prediction system leveraging machine learning algorithms to forecast students' academic outcomes." "Creating a predictive model to anticipate students' exam scores based on historical data and relevant factors.

Senior students' National Exam Results prediction using machine learning. Interactive Streamlit dashboard allows students to enter their data and view predicted scores. Powered by Regression

This notebook explores potential models that help and find best optimal model for prediction of what student habits affect exam scores.

Train and optimize 3 AI models to make predictions on student exam scores based on several performance factors using open source kaggle dataset.

A machine learning project that predicts student final exam scores based on study hours using Linear Regression. Includes data visualization, model evaluation, and prediction.

Linear Regression model predicting student exam scores based on study hours. Includes data cleaning, visualization, training/testing split, evaluation (MAE, RMSE, R²), and prediction visualization.

This project predicts student performance based on academic factors such as attendance, assignment scores, quiz scores, final exam scores, and study hours. It includes a FastAPI backend for data processing and machine learning predictions, and a frontend built with HTML, CSS, and JavaScript.

Student Marks Prediction with Streamlit & Machine Learning This project predicts student exam scores based on the number of hours they studied using a simple Linear Regression model. It is built using Python, Pandas, Scikit-learn, and Streamlit for the web interface.

This project predicts students' exam scores based on attendance, study hours, and past results using **Machine Learning** techniques like **Linear Regression** & **Decision Trees**. It also provides a Flask-based web dashboard where the admin can upload student data, view predictions, identify high-risk students, and export results.

SmartExam Predictor is a machine learning project that predicts student exam scores based on their daily habits, lifestyle, and personal factors. It provides an interactive tool to help students and educators understand which habits impact performance the most, and offers predictions based on individual data.