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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student exam score prediction using multiple ml models

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.

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.

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

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.

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.

Student Exam Performance Predictor is an end-to-end machine learning project that trains a regression model to estimate a student’s maths score from demographic and academic profile data, then serves predictions through a Flask web application. The repository includes the full pipeline.

This work aims to develop student's academic performance prediction model, for the Bachelor's degree students in Computer Science using the classification method; Decision Tree Algorithm. Parameters like semester marks, attendance marks, score obtained from the 10th and 12th examinations, study time, and other aspects were selected to conduct this work. Due to early prediction and solutions are done, better results can be expected in final exams. Students can view their passing percentage in further academics.

Student Exam Score Prediction model

A machine learning model that predicts students’ exam scores based on study habits, attendance, previous performance, and tutoring sessions.

Machine Learning project to predict student exam scores based on academic and lifestyle factors. Includes preprocessing pipelines, model comparison (Linear Regression, Ridge, Lasso), hyperparameter tuning with GridSearchCV, and model deployment using Joblib.

Linear regression model to predict Student Exam Score Predictions

Kaggle Playground Series S6E1 – regression model for student exam score prediction.

Student exam score prediction model with regression - Machine Learning (Python, Scikit-learn)

Student Score Prediction Model using ML | Linear Regression & Polynomial Regression. Predicting exam score of students. This is part of Elevvo Internship Training.

Exam Score Prediction App built with React and Flask, using a custom-trained Machine Learning model to predict student exam scores based on input features

Student Performance Prediction Using Machine Learning This project predicts student exam scores using ML algorithms such as Linear Regression and Random Forest. Features: - Exploratory Data Analysis - Data Preprocessing - Model Training - Score Prediction

An Exploratory analysis for the Israel PISA exam of 2018 & Prediction model for the Reading score of a student

Student Exam Score Prediction A machine learning project that predicts students' exam scores using features like study hours, previous scores, and attendance. The model helps identify key performance factors and supports educators in recognizing students needing extra help. Includes data analysis, regression modeling, and an interactive UI

Student Exam Score Prediction is a machine learning regression project that predicts student exam scores based on factors like study hours, attendance, and previous scores. The project includes data preprocessing, EDA, model building, and evaluation using regression algorithms.

This is my Repo of Student Exam Score Prediction Model webapp using Streamlit for Celebal Technologies Summer Internship Final Project.

Student Score Prediction using Machine Learning. Built Linear & Polynomial Regression models on the Student Performance Factors dataset to predict exam scores. Covered data cleaning, visualization, feature engineering, and model evaluation (MSE, R²).

Student Score Prediction Model using Linear Regression and Polynomial Regression (degree 2). Predicts student exam scores based on study-related features such as hours studied, attendance, and access to resources.

Student Exam Score Prediction using Machine Learning and Data Analysis. This project analyzes factors such as study hours, attendance, sleep, and previous scores to predict student exam performance using Python, Pandas, and Scikit-learn regression models.

Student_score_prediction is a beginner-friendly machine learning project that predicts student exam scores from study-related features using linear regression, covering data preprocessing, model training, and evaluation on real-world-style score data

Student Exam Score Prediction is a simple machine learning project that predicts a student’s exam score using hours studied and attendance percentage. Linear Regression is used to train the model, evaluate performance, visualize trends, and make real-time predictions based on user input.

Student Score Prediction: Predict students' exam scores using Python. Perform data cleaning, visualization, and linear regression. Split data into training/testing sets, train models, evaluate performance, and visualize predictions. Bonus: try polynomial regression and experiment with feature combinations.