/california-school-analysis

Primary LanguageJupyter Notebook

california-school-analysis

Current Roadmap

Overview of Data

  • The California Department of Education offers data on the following categories:
    • Absenteeism
    • Accountability
    • Annual Enrollment
    • Assessment
    • CALPADS Unduplicated Pupil Count (UPC)
    • CBEDS School/District Information
    • Discipline
    • District of Choice
    • Graduate and Dropout
    • Post-Secondary Enrollment
    • Private Schools Data
    • Public School and District
    • Stability Rate Data
    • Staff and Course Enrollment
  • We plan to focus on stricly high school data.

Creating the dataset/Preprocessing

  • n by m array where n = the number of schools and m = number of school features. The features will be drawn from each dataset and joined by school.
  • Normalize and scale all data

Dimensionality Reduction

  • Principal Component Analysis

Unsupervised Clustering

  • Cluster schools using K-Means (or the like)
  • Analyze why certain schools where clustered together
  • Determine optimal number of clusters to use

Supervised Learning of School Performance

  • Design custom objective function that measures student performance/school performance/etc. ???
  • Use a supervised model on each cluster, using school performance metric as labels.
  • Feature importance analysis: What caused rises in school performance increases/decreases by cluster?