Numerical Predictions Using Polynomial Regression

Monday, February 18, 2019

Getting Started

If you are interested in reproducing this work, here are the versions of R, Python, and Julia used (as well as the respective packages for each). Additionally, Leland Wilkinson’s approach to data visualization (Grammar of Graphics) has been adopted for this work. Finally, my coding style here is verbose, in order to trace back where functions/methods and variables are originating from, and make this a learning experience for everyone—including me.

cat(R.version$version.string, R.version$nickname)

R version 4.2.3 (2023-03-15) Shortstop Beagle

require(devtools)
devtools::install_version("ISLR", version="1.4", repos="http://cran.us.r-project.org")
devtools::install_version("dplyr", version="1.1.4", repos="http://cran.us.r-project.org")
devtools::install_version("ggplot2", version="3.5.0", repos="http://cran.us.r-project.org")
devtools::install_version("caret", version="6.0.94", repos="http://cran.us.r-project.org")

library(ISLR)
library(dplyr)
library(ggplot2)
library(caret)

Python

import sys
print(sys.version)

3.11.4 (v3.11.4:d2340ef257, Jun  6 2023, 19:15:51) [Clang 13.0.0 (clang-1300.0.29.30)]

!pip install pandas==2.0.3
!pip install plotnine==0.12.1
!pip install scikit-learn==1.3.0

import random
import datetime
import pandas
import plotnine
import sklearn

Julia

using InteractiveUtils
InteractiveUtils.versioninfo()

Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin22.4.0)
  CPU: 8 × Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, skylake)
  Threads: 1 on 8 virtual cores
Environment:
  DYLD_FALLBACK_LIBRARY_PATH = /Library/Frameworks/R.framework/Resources/lib:/Library/Java/JavaVirtualMachines/jdk-21.jdk/Contents/Home/lib/server

using Pkg
Pkg.add(name="HTTP", version="1.10.2")
Pkg.add(name="CSV", version="0.10.13")
Pkg.add(name="DataFrames", version="1.6.1")
Pkg.add(name="CategoricalArrays", version="0.10.8")
Pkg.add(name="StatsBase", version="0.34.2")
Pkg.add(name="Gadfly", version="1.4.0")

using HTTP
using CSV
using DataFrames
using CategoricalArrays
using StatsBase
using Gadfly

Importing and Examining Dataset

Upon importing and examining the dataset, we can see that the data frame dimension is 200 rows and 5 columns.

Python

Julia

Getting Started

Importing and Examining Dataset

Further Readings