Iris Dataset Classification in Python | Machine Learning

This machine learning model predicts the species of iris flower and classifies the flower to setosa, versicolor, and virginica based on given features like sepal length, sepal width, petal length, and petal width in cms. The dataset for this model is in-built into the sklearn module of python, so we can directly import our dataset from the sklearn module.

Objective:

The objective of building this model is to classify the species of iris flower into mainly three categories. This model predicts the species of iris flower based on features like sepal length, sepal width, petal length, and petal width in cms. This model can be useful to the research departments that have been working in the field of botany. People like plant scientists can also use this model for research purposes.

Timestamp:

00:12 - Project Overview
00:45 - Code Explanation
05:23 - Demonstration

Requirements:

1. Python installed with all the necessary libraries
2. Jupyter notebook or Visual Studio Code

Explanation of the Code

1. Initially, we imported the dataset from the sklearn module of python,

2. The sklearn module contains the features like sepal length, sepal width, petal length, and petal width. On the basis of all these features, our machine-learning model predicts the species of the iris flower.

3. Then we trained our classifier using a Logistic Regression algorithm which is a type of regression algorithm that helps to solve classification problems.

#machinelearning #iris #artificialintelligence