IRIS Dataset Explained

If you’ve ever wondered what the IRIS dataset means in data science, it is one of the most well-known datasets used in machine learning processes. The IRIS dataset is a collection of flower measurements that helps train algorithms to identify and classify three types of IRIS flowers: Setosa, Versicolor, and Virginica. By the end of this blog, you’ll build a model that can analyse a flower’s features and predict its species. We will teach you how to download the IRIS dataset CSV or get it from web pages such as the IRIS dataset Kaggle page. This will get you one step closer to your predictive model. So, let’s explore how to make a genuine ML model using the IRIS dataset from start to finish.

Table of Contents:

• What is IRIS Dataset?
• What You’ll Build: Machine Learning with the IRIS Dataset
• Essential Skills and Tools Required for This ML Project
• Setting Up Your Development Environment (Google Colab or Jupyter)
• Importing Libraries and Loading the IRIS Dataset
• Exploring and Visualizing the IRIS Dataset Features
• Preparing the Data for Model Training
• Training a Classifier Using Support Vector Machines (SVM)
• Evaluating Model Accuracy with Test Data
• Testing Predictions on New Input Data
• Final Code for Working with the IRIS Dataset
• Comparing Other ML Models on the IRIS Dataset
• Tips for Improving Model Accuracy in Classification Projects
• Conclusion

What is IRIS Dataset?

In the field of machine learning, the IRIS dataset is one of the most frequently used datasets as it is beginner-friendly. It was first proposed by the British statistician Ronald Fisher in the year 1936 and consists of 150 samples of iris flowers from three species, including Setosa, Versicolor, and Virginica. In every sample, there are four critical features of the flowers: sepal length, sepal width, petal length, and petal width, used in the training of the classification models. The small size, clean structure, and balanced classes are some of the reasons why the IRIS dataset is widely used to practice model training, testing, and visualization.

What You’ll Build: Machine Learning with the IRIS Dataset

In this project, you’ll build a simple yet powerful machine learning model using the IRIS dataset. The idea is to develop a program, which, when shown the measurement of an IRIS flower-such as the length of the sepal and width of the petal, will be able to give you the appropriate result indicating whether it’s Setosa, Versicolor, or Virginica. It is an interesting method to understand the system of classification in life with real data. At the end of the section, you will have a working classifier that could predict new features for flower types. In case you have ever encountered the iris dataset CSV or have ever searched iris dataset download, this is the type of project that people employ it in. And on the way, you will learn how to see the iris flower not just in terms of petals and colors, but as a collection of meaningful data.

Essential Skills and Tools Required for This ML Project

• Basic knowledge of Python programming: You do not have to be an expert coder, but a basic understanding of Python programming concepts such as looping, object-oriented programming, functions, and lists will go a long way in assisting you to follow along as well as practice with the IRIS dataset at your own pace.
• Acquaintance with Machine Learning terminology: As you will be developing a flower classifier, it helps if you have an idea of what classification models are and how they operate, and in particular, algorithms such as Support Vector Machines (SVM).
• Data Library Familiarity: You will be using powerful Python libraries such as Pandas (to load and to manipulate the iris CSV file), NumPy (to work with the numerical data), Seaborn/Matplotlib (to display the iris flower patterns) and Scikit-learn (to train and test the model you create).
• Familiarity with Jupyter Notebook or Google Colab: These platforms let you write and execute your programs interactively. Google Colab is online and ideal when installation is not desired. Jupyter is awesome when you are working offline.
• Fundamentals of handling data: You are expected to understand how to load a CSV file (such as iris data CSV), navigate it, clean it up, and then you can prepare it by training your model. This involves identifying features and labels and splitting the data into a training and testing set.

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Setting Up Your Development Environment (Google Colab or Jupyter)

Before we introduce the IRIS dataset, you’ll need a place to write and edit your Python code. Now you can choose between two popular environments that are the best when it comes to Machine Learning projects, which are Google Colab and Jupyter Notebook.

Steps to get started in Google Colab:

1. Visit: https://colab.research.google.com
2. Press “New Notebook”.
3. Begin typing your code in the code cells in Python

Working offline using Jupyter Notebook (Installation needed)

In case you prefer to work offline, you can download Jupyter Notebook with Anaconda or pip.

To install with pip:

pip install notebook

Then proceed to run:

jupyter notebook

This will launch a local server in your browser where you can create and execute the .ipynb files.

You can download the IRIS Dataset CSV file.

Either in Colab or Jupyter, you can download the CSV file of the iris dataset on Kaggle or use the integrated version in Scikit-learn.

To access your own downloaded version:

import pandas as pd

# Load the CSV file

iris = pd.read_csv("Iris.csv")

print(iris.head())

You have successfully downloaded the iris dataset.

Tip: Ensure that there are no errors and be sure of the path of your files, whether you are loading an iris CSV on your system or loading it on Scikit-learn’s built-in dataset.

Importing Libraries and Loading the IRIS Dataset

It is now time to introduce the tools you will use to construct your classifier, given that your environment is now set. These libraries are essential Python tools that help load the iris dataset CSV, investigate it, and prepare your model efficiently and accurately.

1. Remember to import essential Python libraries first

Code snippet to import important libraries:

import pandas as pd         # For handling data

import numpy as np          # For numerical operations

import seaborn as sns       # For visualizations

import matplotlib.pyplot as plt  # For plots

from sklearn.model_selection import train_test_split  # For splitting the data

from sklearn.svm import SVC  # The Support Vector Classifier

from sklearn.metrics import accuracy_score  # To check model accuracy

These are the most standard tools that you, as a coder, would require in any machine learning project. This is especially true for datasets like the iris CSV.

2. Next, you will load the IRIS dataset from a CSV file

Suppose that you downloaded the iris dataset CSV (e.g., on Kaggle), use the following code:

# Load the iris dataset

iris = pd.read_csv('Iris.csv')

# Show the first 5 rows

iris.head()

Explanation: In this case, a row will mark the sample (a single flower), and the column will be a variable (such as sepal length or petal width). We are about to predict the last column, namely, Species.

3. Now we drop the ID column

Since the ID column is not useful in prediction, we take it out:

iris.drop('Id', axis=1, inplace=True)

This clean iris data set can now be explored, and a classifier can be built using this data set.

Exploring and Visualizing the IRIS Dataset Features

The first step is to get acquainted with the structure of the IRIS dataset before you can commit to training your machine learning model. That is, to analyze the characteristics and pick out the trends through basic yet effective visualizations. You can imagine it as familiarizing yourself with your flowers, and then requesting a machine to classify your flowers.

Explore the Dataset Structure

Use the code below to get an immediate summary of the dataset you are going to implement classification on:

# Check dataset info

iris.info()

Explanation: Here, the output showcases that the dataset has 150 rows and no missing values. This is ideal for training.

Visualize the Dataset

Now we will begin plotting the dataset using Seaborn to check in what ways the features are related:

# Pairplot to visualize relationships

sns.pairplot(iris, hue='Species')

plt.show()

Explanation: Here, this scatterplot demonstrates to you the difference between the flowers in the size of sepals and petals. It will be noted that Iris-setosa is highly different, whereas Iris-versicolor and Iris-virginica are slightly overlapping.

Check Feature Distribution

You can start checking how the values are scattered for each flower species by implementing this snippet:

# Distribution plot for petal length

sns.boxplot(x='Species', y='PetalLengthCm', data=iris)

plt.title('Petal Length per Iris Flower Type')

plt.show()

Explanation: The graph here helps you spot differences in various flowers according to their sizes. Just for example, Iris-setosa has much shorter petals as compared to the other ones.

Preparing the Data for Model Training

Often, two steps are required before feeding the data to your machine learning model, you must split the inputs (features) and output (labels). This is essential to make your model not only understand what to learn and what to predict. Preparation will be fast and easy because the IRIS dataset is clean.

Separate Features and Labels

The four measurements constitute the features, and they are sepal length, sepal width, petal length, and petal width. The label is the species of the flower.

# Convert DataFrame to NumPy array

iris_data = iris.values

# Split into features and labels

X = iris_data[:, 0:4]  # Features

y = iris_data[:, 4]    # Labels

The features are in x, while the corresponding flower types (Setosa, Versicolor, Virginica) are in y, which can be Iris-setosa, Iris-versicolor, and Iris-virginica, can be found in y.

Split into Training and Testing Sets

You require two parts: first would train the model, and the second part would test the performance of the model on unseen data. We are going to keep 80 percent to train and 20 percent to test.

from sklearn.model_selection import train_test_split

# Split the data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

The model will be trained using X_train and y_train. It will be utilized later to calculate the X_test and the y_test to test its accuracy.

Training a Classifier Using Support Vector Machines (SVM)

Since you have your IRIS dataset all prepared, it is high time you used it to prepare a machine learning model to classify the flowers. We are going to employ Support Vector Machines (SVM), which is an effective classification algorithm. Just imagine the SVM as an intelligent separator that creates boundaries between flower types so that these flowers could be distinguished by the model effectively.

Training the SVM Model

You will import the SVM classifier module of scikit-learn, instantiate it as a model, and then train it with your training data.

from sklearn.svm import SVC

# Create the SVM classifier

model = SVC()

# Train the model using the training data

model.fit(X_train, y_train)

Once this has been done, your model has learned from the measurements of the sepal and petals of each flower and how to make the predictions on the species.

SVM identifies the optimal linear (or dimensional in higher dimensions) sheet that divides the various kinds of flowers according to the given features. It attempts to optimize the diversity among classes. Therefore, it is able to give more ascertained predictions, particularly when new information arrives.

Evaluating Model Accuracy with Test Data

So now that you have trained your model with the dataset of IRIS, it is time to know how good your model is doing. You perform this by trying its hand on the data that it has yet to see, the test set. The step indicates whether the model is actually capable of recognizing the right species of the iris flower using new inputs.

Make Predictions Using the Test Data

We will proceed to ask the model to predict the species from the test set for us:

# Predict the test dataset

predictions = model.predict(X_test)

The line here orders the model to classify every flower in the test set depending on the patterns it has learned.

Check the Accuracy

Now look how many of those predictions were right. This can be done by using accuracy_score from scikit-learn.

from sklearn.metrics import accuracy_score

# Calculate the accuracy of the model

accuracy = accuracy_score(y_test, predictions)

print("Accuracy:", accuracy)

Explanation: This implies that approximately 96.6 percent of the predictions were correct, i.e., it was accurate most of the time, which is very good considering a simple problem like classification of the IRIS dataset. And why do we need accuracy? Accuracy will tell you how well the model has learnt. When it is too low, then your model may require better features, or a larger size of data points, or a different algorithm. However, with clean IRIS CSV data like this, your SVM model is doing a fantastic job already.

Testing Predictions on New Input Data

Now you have to see how good your model worked for you. After learning and testing your model using the IRIS dataset, it is time to give it the most challenging task of them all, i.e., Predict the different species of iris flower using a brand new dataset that you are going to provide. This process tests the capability of the model to recognize a flower that it does not recognize.

Try making custom predictions

Let us set conditions for a found flower with the following features:

• Sepal length: 6 cm
• Sepal width: 3 cm
• Petal length: 4 cm
• Petal width: 2 cm

Predict the species in this manner: 

# Predicting a new data sample

sample = [[6, 3, 4, 2]]

prediction = model.predict(sample)

print("Predicted Iris Species:", prediction)

Your model identified this flower as an Iris versicolor.

This is how theory is translated into practice. Training predictions by hand, you are getting an insight into how your model could perform in a real-life situation, e.g., in a botanical garden where flowers are labeled using sensor data, or as a mode of educating children about plant species. Now that you’ve trained and tested your IRIS flower classifier, it’s ready for predictions.

Final Code for Working with the IRIS Dataset

This is the final code that you will come up with. It has all the necessary steps to perform IRIS database classification, and it will give you precision, recall, f1-score, etc.

Code:

Output:

Comparing Other ML Models on the IRIS Dataset 

Tips for Improving Model Accuracy in Classification Projects

1. Normalize Feature Data: The numerical attributes in the IRIS dataset (such as sepal length, petal width) can occur on a range. Such algorithms as SVM and KNN are scale-sensitive. Normalization (such as MinMaxScaler or StandardScaler) should be performed in order to have all features on a similar scale. This makes sure that none of the large-scale features overpowers others throughout training.
2. Tune Hyperparameters Using Grid Search: Any ML model has its parameters that one can fine-tune to perform better. In SVM, as an example, this can result in a significant increase in accuracy when the values of C and kernel change. GridSearchCV can automatically identify the best combinations.
3. Use Stratified Sampling for Train-Test Split: In order to guarantee that the three types of iris flowers (Setosa, Versicolor, Virginica) are well represented in the training as well as testing datasets, always stratify your splits. This discourages the model against bias and overfitting towards a single class.
4. Add Feature Engineering or Interaction Terms: In some cases, some patterns can be hidden by the combination of existing characteristics. You are able to design novel characteristics, such as petal area or sepal-to-petal ratio of length, that add more data to models.
5. Cross-Validation for Robust Accuracy: Rather than a single random train-test split, perform cross-validation to estimate the performance of the model on various folds of the data. This eliminates spurious high or low accuracies, which are misleading by chance.

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Conclusion

Now you’ve completed a full walkthrough using the popular and beginner-friendly IRIS dataset to build and test a machine learning model. You have gone through a complete ML pipeline, which starts with learning about the iris flower and training an SVM classifier using it, and then testing the classifier on actual data. This type of project lets you see how to transform simple data into a consistent sequence of predictions, whether using the iris dataset CSV from Kaggle or other sources. The greater you use these ideas and implement such techniques as data scaling, model tuning, and visualization, the more confident you will become in solving real-world machine learning problems.

Try our machine learning interview questions to enhance your technical knowledge. Also, check out our blog on Gradient Boosting in ML.

IRIS Dataset Explained – FAQs