Cleveland school districts: student dropout prevention

Cleveland school districts: student dropout prevention

Service Description

  • Analyze students performance across semesters and subjects
  • Predict students drop out interventions and identify individuals who are at risk of dropping out
  • Determine the most cost-effective intervention for the student
  • Forecast expected drop-outs and plan accordingly
  • Identify underperforming classes and demographics

Service Impact

  • More students stay in school
  • Higher intervention success rates
  • Cost optimization of limited resources
  • Tailored plans for each class, subject, grade, etc.

Features

  • Custom Machine Learning classification algorithms
  • Power BI dashboard to analyze students performance and predict drop outs/interventions
  • Hands-on Machine Learning Modeling

Architecture

  • Process data locally or in the cloud with Microsoft Azure
  • Refresh reports with Azure Data Factory
  • Host data affordably in Azure Synapse Analytics (formerly Azure SQL Data Warehouse)
  • Run advanced machine learning models using R and Python in Azure Machine Learning
  • Export results and insights to Power BI or Excel