DATA ANALYSIS : Shiny App Project

The Objective of the project is to perform user friendly data analysis on web and develop predictive models.The app may be viewed at https://shan4224.shinyapps.io/Project/
The .csv data can be uploaded . We first visualize the summary of data.

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50  

We select a train data in the left side panel of the app. We select a dependent variable and fit a predictive model using tree algorithms.

n= 150 

node), split, n, loss, yval, (yprob)
      * denotes terminal node

1) root 150 100 setosa (0.33333333 0.33333333 0.33333333)  
  2) Petal.Length< 2.45 50   0 setosa (1.00000000 0.00000000 0.00000000) *
  3) Petal.Length>=2.45 100  50 versicolor (0.00000000 0.50000000 0.50000000)  
    6) Petal.Width< 1.75 54   5 versicolor (0.00000000 0.90740741 0.09259259) *
    7) Petal.Width>=1.75 46   1 virginica (0.00000000 0.02173913 0.97826087) *

We may select any test data in leftside panel of the app. We predict the dependent variable for test data.

Prediction for last six observations.

    setosa versicolor virginica
145      0 0.02173913 0.9782609
146      0 0.02173913 0.9782609
147      0 0.02173913 0.9782609
148      0 0.02173913 0.9782609
149      0 0.02173913 0.9782609
150      0 0.02173913 0.9782609