Abstract

This project explores the issue of high dropout rates in school education. We utilize a machine learning-driven approach to analyze data on student demographics, academic performance, attendance, and socioeconomic factors. By identifying at-risk students early, we aim to provide targeted interventions that will reduce dropout rates. This document outlines the structure, methodology, and findings of the project, leveraging techniques such as data preprocessing, model training, hyperparameter tuning, and risk stratification.