Using Python in Rstudio!

We will reproduce the example given in the ebook here fitting a simple machine learnig model. Exactly follow the example from the notes to do this part of the project using the data aircraft from robustbase R package.

Aircraft Data, deals with $23$ single$-$engine aircraft built over the years $1947-1979,$ from Office of Naval Research. The dependent variable is cost $($in units of $$100,000)$ and the explanatory variables are aspect ratio, lift$-$to$-$drag ratio, weight of plane $($in pounds$)$ and maximal thrust. A data frame with $23$ observations on the following $5$ variables:

X$1$ is Aspect Ratio,

X$2$ is Lift$-$to$-$Drag Ratio,

X$3$ is Weight,

X$4$ is Thrust, and

Y is Cost.

We shall build a predictive model for the Cost $($Y$)$ given X$1,$ X$2,$ X$3,$ and X$4.$

# Load the data

import statsmodels.api as stat

aircraft $=$ stat.datasets.get$\_$rdataset$("$aircraft$",$ "robustbase"$).$data # load data "aircraft" from the R package 'robustbase'

Plot histogram of the Cost $($see examples from notes$)$

#Code here

Split data to $80\%$ training and $20\%$ testing data sets

#Code here

Create outcome $($Cost$)$ and features $($Xi$)$ from training data and testing data $($see examples from notes$)$

#Code here

fit a linear regression on training data set

#Code here

Predict using the fit and testing data set

#Code here

Calculate MAPE

#Code here