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

This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course

Data exploration done using students performance dataset that involves while playing an educational game.

To understand the how the student's performance (test scores) is affected by the other variables (Gender, Ethnicity, Parental level of education, Lunch, Test preparation course).

EduRisk is a machine learning project designed to predict and analyze academic risk among students. It leverages data-driven insights through preprocessing, feature engineering, and predictive modeling (Ensemble & C-CatBoost) to help identify underperforming learners early and support timely interventions from teachers.

Created a ML-based Student Performance Predictor that forecasts pass/fail outcomes using exam scores. Built and deployed with Python & Streamlit.

Involves using machine learning techniques for creating a linear regression model to predict students' math scores.

In this analysis, I investigate the relationship between school size, type, and spending per student with academic performance across different schools and within a District.

🐙 Descriptive and inferential statistics to explore how gender, parental education, lunch type, and test prep affect Math, Reading, and Writing scores among students.