An R-based statistical inference project investigating the drivers of student academic performance. It moves beyond simple prediction to isolate statistically significant factors using multivariate regression, ANOVA, and t-tests.

🎓 Predict student CGPA with 100% accuracy using linear regression on 1,193 records, revealing insights into academic performance and progress.

An end-to-end machine learning project that explores student performance data through EDA, feature engineering, and predictive modeling to forecast academic scores.

Exploratory Data Analysis and Visualization of Student Performance using Python (Pandas, Matplotlib, Seaborn).

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. AI for Personalized Education Systems leverages AI to tailor learning paths, adapt content, and optimize assessments, improving student engagement, understanding, and overall academic performance.

Achieved 100% accuracy (R²=1.0000) predicting student CGPA using Linear Regression on 1,193 student records. Discovered academic progress perfectly determines performance. Complete ML pipeline: EDA → Perfect Model.

Student performance prediction models

Student Performance Prediction API built with Scikit-Learn, FastAPI, Pandas, and Machine Learning pipelines.

A machine learning project that predicts student final grades using classical ML algorithms on the Student Performance dataset. The dataset includes student performance data from two Portuguese schools, covering Mathematics and Portuguese language courses with demographic, social, and school-related features.

The Student Marks Predictor is a next-generation, interactive Streamlit application powered by machine learning using Random Forest🌳 Model

Predicting students performance in exams using machine learning classifiers : Logistic regression, KNN and SVM. Extraction of factors impacting students' performances.

Machine Learning project to predict student performance using Python, Google Colab, and Scikit-learn.

This project analyzes student performance data using Python. It explores how gender, parental education, lunch type, and test preparation affect math, reading, and writing scores. Includes data cleaning, EDA, and visualizations using pandas, matplotlib, and seaborn.

Student Performance (Multiple Linear Regression)