Student Performance (Multiple Linear Regression)
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.
Student performance prediction models
Exploratory Data Analysis and Visualization of Student Performance using Python (Pandas, Matplotlib, Seaborn).
Predicting students performance in exams using machine learning classifiers : Logistic regression, KNN and SVM. Extraction of factors impacting students' performances.
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.
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.
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