“添翼杯”人工智能创新应用大赛-智慧教育赛道

Prediction of exam scores using machine learning in Python

Predicts the exam scores of fictitious students using Multiple Linear Regression

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

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.

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

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.

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

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.

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.

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

The aim of the project is determine if a student can gain admission into a university based on the scores of two exams. The prediction is done based on the decision boundary that the logistic regression learns.

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.

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.

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.

Prediction of Exam scores

Student final exam scores prediction

🎓 Prediction of student exam scores.

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Created an end-to-end machine learning project with a CI/CD pipeline deployed on the AWS Cloud.

Machine learning project for predicting student exam scores using Kaggle’s Playground Series S6E1 dataset. Includes data preprocessing, one‑hot encoding, model training (Ridge/Lasso), feature importance analysis, and Kaggle‑ready submission pipeline.

This project successfully employed advanced machine learning protocols, producing highly accurate predictions and providing valuable insights into the impact of health factors on exam performance.

Python

The goal of this project was to develop a machine learning model capable of accurately predicting exam scores based on various medical diagnoses and lifestyle factors.

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Scores Prediction Model using Linear Regression of Student Performance Factors Dataset.

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This project is a machine learning model designed to predict students' exam scores based on their study habits and other related features. It uses regression algorithms like Random Forest, and XGBoost to accurately estimate the scores.

Created a Linear Regression model to predict exam scores of a student based on the amount of time spent on coding daily.