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.

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

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).

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

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.

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.

Scholar Guide aims to provide proper mentorship to students who want to excel but cannot find a suitable mentor.