Need R $$ Code for the Following:

Iris Flower Species Classification $$ In Built Dataset in R

Objective: The primary objective of this assignment is to explore classification techniques using the 'Iris' dataset and collectively create a comprehensive report summarizing the classification process and results.

Dataset Description:

$$ The 'Iris' dataset is a widely used dataset in the field of machine learning. It contains measurements of sepal length, sepal width, petal length, and petal width for three species of Iris flowers: Setosa, Versicolor, and Virginica.

Assignment Components:

$1.$ Data Exploration:

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o As a class, explore the 'Iris' dataset to understand its structure and contents.

o Calculate and visualize the class distribution to assess the balance of the target variable $($Iris species$).$

o Visualize the distribution of the four numeric features: sepal length, sepal width, petal length, and petal width.

$2.$ Data Splitting:

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o Divide the dataset into training such as $(75\%)$ and test $(25\%)$ sets to evaluate model performance on unseen data.

o Utilize the 'sample.split' function to perform the data split.

$3.$ Model Selection:

o As a class, decide on a classification algorithms to explore:

$$ Logistic Regression

$$ k$-$Nearest Neighbors $($KNN$)$

$$ Support Vector Machine $($SVM$)$

$$ Naive Bayes

$$ Decision Tree

$$ Random Forest

o Each group or individual will choose one of these models to work on$.$

$4.$ Model Building:

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o For the chosen classification method, construct the model to predict the Iris species.

o Define the parameters and hyperparameters used in the model.

o Ensure clear documentation of the modeling process.

$5.$ Model Evaluation:

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o Evaluate the model's performance using the following metrics:

$$ Accuracy: Overall correct classifications.

$$ Precision: The model's ability to classify a specific class correctly.

$$ Recall: The model's ability to identify all relevant instances of a class.

$$ F$1-$Score: The balance between precision and recall.

$$ ROC$-$AUC: The model's ability to distinguish between classes. $($Optional$)$

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