Executive Summary

This is an assignment by John Hopkins University’s Coursera Course - Developing Data Products - Week 4 Project. Below is the requirement:

• Write a shiny application with associated supporting documentation. The documentation should be thought of as whatever a user will need to get started using your application.
• Deploy the application on Rstudio’s shiny server
• Share the application link by pasting it into the provided text box
• Share your server.R and ui.R code on github

Data - mtcars

The dataset is from standard R package - mtcars. For previewing the data, below is the code:

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

• You could access the shiny webapp by clicking here.
• If you want to find out more information of the server.R and ui.R code, you could also visit my github repository here.

Machine Learning Algorithm

Linear model is used to run the machine learning algorithm, to predict mileage per gallon based on variables selected by end-user. - Below is an illustration using ‘No. of cylinders’ variable - The output/ result will be stored in a variable called fit

fit <- lm(mpg ~ cyl, mtcars)

Result

After running the model, the fit result and plot will be shown to the end-user

with(mtcars, {plot(mpg ~ cyl)
        abline(fit, col = 2)})

fit

## 
## Call:
## lm(formula = mpg ~ cyl, data = mtcars)
## 
## Coefficients:
## (Intercept)          cyl  
##      37.885       -2.876