Iris prediction model is used to classify iris species created julia's DecisionTree, DataFrames, JLD2, PlotlyJS and Statistics packages.

Iris Species Predictor

Iris prediction is used to classify iris species using their sepal length, sepal width, petal length and petal width created using julia's DecisionTree, DataFrames, JLD2, PlotlyJS and Statistics packages.

Dataset Description :-

This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

Dataset Format :-

iris is a data frame with 150 cases (rows) and 5 variables (columns) named sepal_length, sepal_width, petal_length, petal_width, and species.

Installation :-

Open command prompt and Change directory to the extracted github repository folder 👇

cd <path>

Type julia to open julia interactive prompt 👇

julia

Then Activate Pkg by typing ] 👇

julia> ]

To install all requirement packages 👇

pkg> instantiate

it will install all the required packages mentioned in Manifest.toml

Packages Used :-

using JLD2
using PlotlyJS
using Statistics
using DataFrames
using DecisionTree
using MLJ: load_iris, selectrows, pretty, schema, nrows

Demo GIF Image 👇 :-