Feature extraction for classifying students based on academic performance involves identifying relevant variables like grades, GPA, test scores, attendance, demographics, and extracurricular activities. The goal is to create a dataset with features that accurately represent students' academic capabilities. This data is then used to train machine learning models to classify students into performance categories (e.g., high, medium, low performers). Key steps include defining the classification objective, collecting comprehensive data, preprocessing to handle missing values, normalizing features, and selecting the most relevant features using techniques like correlation analysis and principal component analysis (PCA).
Feature extraction for classifying students based on academic performance involves identifying relevant variables like grades, GPA, test scores, attendance, demographics, and extracurricular activities. The goal is to create a dataset with features that accurately represent students' academic capabilities. This data is then used to train machine learning models to classify students into performance categories (e.g., high, medium, low performers). Key steps include defining the classification objective, collecting comprehensive data, preprocessing to handle missing values, normalizing features, and selecting the most relevant features using techniques like correlation analysis and principal component analysis (PCA).
