End-to-End Machine Learning: IRIS Flower Prediction in R

End-to-End Machine Learning: IRIS Flower Prediction in R

IRIS flower prediction is a machine learning task that involves identifying the type of IRIS flower based on certain characteristics such as the flower’s petal length and width, and sepal length and width. There are three types of IRIS flowers: Setosa, Versicolor, and Virginica, and being able to classify them correctly is important for botanical research and study.

In R, there are several libraries such as caret, randomForest, glmnet and xgboost that provide functions to train machine learning models for IRIS flower prediction. The process of building a IRIS flower prediction model typically involves the following steps:


  1. Collecting and cleaning the data. This includes acquiring a dataset of IRIS flowers and relevant features such as petal and sepal length and width.
  2. Exploratory data analysis. This includes visualizing and understanding the relationship between different features and the outcome of interest, classifying the IRIS flower type.
  3. Preprocessing the data. This includes normalizing, scaling, or transforming the data to prepare it for the model.
  4. Choosing and training a model. This includes selecting an appropriate model, such as a logistic regression or a random forest, and training it on the preprocessed data.
  5. Evaluation. This includes evaluating the model’s performance on a separate test dataset and comparing it to other models or to a baseline.
  6. Fine-tuning. This includes finding the optimal parameters for the chosen model using techniques like grid search or random search.


It’s important to note that IRIS flower prediction is a well-established area of machine learning and there are many models and techniques that can be used to improve the performance of the model. Additionally, it’s important to use cross-validation to ensure that the model generalizes well to new data. The performance metric used to evaluate the model will depend on the specific use case, for example in a botanical research setting, it may be more important to have a high accuracy rather than high precision.

Overall, using machine learning techniques to predict the type of IRIS flowers in R can help to improve the understanding of the IRIS flowers characteristics and aid in botanical research. It’s important to use appropriate techniques like cross-validation to ensure that the model generalizes well to new data, and to use appropriate performance metrics that align with the specific use case of the model.


In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: IRIS Flower Prediction in R.

End-to-End Machine Learning: IRIS Flower Prediction in R


Fund SETScholars to build resources for End-to-End Coding Examples – Monthly Fund Goal $1000


Sign up to get end-to-end “Learn By Coding” example.




There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!



IRIS Flower Classification using SKLEARN DecisionTree Classifier with Grid Search Cross Validation

IRIS Flower Classification using SKLEARN RandomForest Classifier with Monte Carlo Cross Validation

How to classify Flowers (iris data) using a keras deep learning model