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Planning Public Policy in Argentina

Apply unsupervised learning techniques to help plan an education program in Argentina.

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12 Tasks1,500 XP

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Project Description

As statistics and machine learning methods become more pervasive, policymakers have also increased their use of these techniques when deciding how to allocate public resources. In this project, you will analyze economic and social development indicators for 22 provinces of Argentina to help plan an education program. This project assumes you can manipulate data frames using `dplyr`, make plots using `ggplot2`, and understand principal component analysis (PCA) and k-means clustering. The data for this project was published by INDEC and includes indicators such as poverty, population, and GDP for each province.

Project Tasks

  1. 1
    Provinces of Argentina
  2. 2
    Most populous, richest provinces
  3. 3
    A matrix for PCA
  4. 4
    Reducing dimensions
  5. 5
    PCA: Variables & Components
  6. 6
    Plotting the components
  7. 7
    Cluster using K means
  8. 8
    Components with colors
  9. 9
    Buenos Aires, in a league of its own
  10. 10
    The rich provinces
  11. 11
    The poor provinces
  12. 12
    Planning for public policy

Technologies

R R

Topics

Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data
Rafael La Buonora Headshot

Rafael La Buonora

Data Scientist at Transforma Uruguay

With a background in economics, Rafael works as a data scientist at Transforma Uruguay, assessing the impact of public policy in the welfare of the citizens of Uruguay.
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