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.

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.

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).

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