Application Overview

This web application is based on MTCARS dataset in “DASETS” package in R. This applications aims to predict miles per gallon of the given Horse Power, Transmission Type and Number of cylinder in a CAR.

Dataset Overview

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

Usage mtcars Format A data frame with 32 observations on 11 (numeric) variables.

[, 1] mpg Miles/(US) gallon [, 2] cyl Number of cylinders [, 3] disp Displacement (cu.in.) [, 4] hp Gross horsepower [, 5] drat Rear axle ratio [, 6] wt Weight (1000 lbs) [, 7] qsec 1/4 mile time [, 8] vs Engine (0 = V-shaped, 1 = straight) [, 9] am Transmission (0 = automatic, 1 = manual) [,10] gear Number of forward gears [,11] carb Number of carburetors Note Henderson and Velleman (1981) comment in a footnote to Table 1: ‘Hocking [original transcriber]’s noncrucial coding of the Mazda’s rotary engine as a straight six-cylinder engine and the Porsche’s flat engine as a V engine, as well as the inclusion of the diesel Mercedes 240D, have been retained to enable direct comparisons to be made with previous analyses.’

Source Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411.

How It Works

  1. This Web Application uses linear regression to train the models for given dataset.
  2. It predicts based on fitted model on new data.
  3. It has also an option to see the fitted regression line in the scatter plot.
  4. It fetches the data from the given feilds and then transform into a dataframe for the prediction.
  5. Below the plot, predicted results are shown

Requirements

There is no other requirement other than a internet connection to open the page,

Screenshot

URL

URL to access the app: https://amanbhagat.shinyapps.io/course-project/