An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.

Springforth University Food Bank: A collaborative initiative with UNESCO to address student food insecurity. Contains code and resources for the web application, data analysis, and insights into the prevalence and impact of food insecurity on academic performance.

Analyzes student behavior patterns to understand their impact on academic performance. Provides clear visual insights and correlations from real data. Supports early prediction and decision-making for improving student outcomes.

EDA project analyzing how study habits, sleep, attendance, stress, and screen time affect student academic performance.

Tracks faculty academic activities, automates API score calculation based on UGC guidelines, and provides dashboards and reports for performance evaluation.

Study of US/CA High School Performance and Socioeconomic Factors with SQL and GeoPandas

Student Feedback Sentiment Analysis for Synthetic and Real-World Dataset Using Multi-Filter CNN and BiLSTM

Este repositorio da cuenta de información de un trabajo sobre calificaciones de asignaturas troncales entre las generaciones 2016 -2020 de un colegio particular pagado.

EDA of the AMCAT job dataset to uncover trends in salary, academic performance, skills, and job locations using Python and data visualization tools.

ML model that predicts student academic performance using classification and regression algorithms, analyzing factors like attendance, grades, and socioeconomic data to forecast outcomes.

📊 Detect academic fragility early with this analytics engine that identifies instability before grades reveal the problem.