Here the prediction and analysis of student scores using selected features is done entirely by linear regression machine learning algorithm. This project covers all methods of linear regression theory.

Internship designed to give a real-world project experience, educational materials, and personalized feedback. Will work on practical tasks aligned with industry skills and tools.

Evaluating student demographics, engagement and performance metrics for predicting dropout likelihood using supervised learning - Neural Networks & Decision Trees.

ML project to predict student marks

Taking samples from Students activities, building a Model using Linear Regression that predicts the performance of Students.

Regression pipeline predicting total student exam scores from demographic and behavioral features — Linear Regression, Decision Tree, Random Forest, GridSearchCV tuning, and Gradio UI.

A Machine Learning project to predict student academic performance using regression and classification models. Includes data preprocessing, visualization, model training, and evaluation on real-world student data.

Student Score Prediction using Machine Learning. This project uses Linear Regression to predict Score. Data visualization is done using Seaborn and Matplotlib.

ML end-to-end project for prediction of student marks. Implemented using regression algorithms and deployed using Flask and rest api is implemented

🎓 Predict student exam scores using machine learning regression models with a focus on multi-variable analysis and feature selection for accurate results.