Data projects: WIFI Positioning

Many real-world applications need to know the localization of a user in the world to provide their services. Outdoor localization problem can be solved very accurately thanks to the inclusion of GPS sensors into the mobile devices. However, indoor localization is still an open problem due mainly to the loss of GPS signal in indoor environments. For this reason, we evaluated the application of machine learning techniques to this problem, replacing the GPS signal with the WAPS signal.




  • Programming language: R
  • Used packages: rstudioapi, dplyr, lubridate, shiny, caret, reshape2, randomForest, class, e1071, RColorBrewer, plotly
  • Main problems: –
  • Next steps: 
    • Improve the structure of the report
    • A deeper analysis of errors
    • Normalize rows
    • PCA
    • H2O package