Pitch Presentation

Larem

1. Shiny Project Pitch

My Shiny Project, California Housing Data Modeling App, is the worlds best California housing price analyzer in the world. If you don’t believe me, feel free to see for yourself. https://larem.shinyapps.io/FinalProjectShinyApp/

2. Quick Reasons

It is awesome because I made it
You can tune any of the other variables from the data (apart from median house value and distance from the ocean)
It updates immediately with any change made by the user
Includes a trend line to compare the means of different distances from ocean

3. The Tuneable Variables

The filterable variables are listed below (exclude median house data and ocean proximity)

dat <- read.csv("housing.csv")
colnames(dat)

 [1] "longitude"          "latitude"           "housing_median_age"
 [4] "total_rooms"        "total_bedrooms"     "population"        
 [7] "households"         "median_income"      "median_house_value"
[10] "ocean_proximity"

4. Large Dataset

The data set used in my project is large. Many projects can not handle large data sets like this but it is very efficient making it very capable.

My data set has the following row count.

nrow(dat)

[1] 20640

5. Drawbacks

Nobody is perfect and neither is my project, its predictive capabilities are non existent since there is no model applied to the data. In the future it would be best to include at least a linear regression or radioButtons to let the user choose a specific predictive model.