Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Now assume that you have the measurements of the iris flowers according to their species, and here your task is to train a machine learning model that can learn from the measurements of the iris species and classify them.

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier

Load the Iris dataset

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

Display the first few rows and basic statistics of the dataset

print(iris.head()) print(iris.describe())

Print unique target labels

print("Target Labels", iris["species"].unique())

Visualize the dataset using Plotly Express

import plotly.express as px fig = px.scatter(iris, x="sepal_width", y="sepal_length", color="species") fig.show()

Split the dataset into features (x) and target labels (y)

x = iris.drop("species", axis=1) y = iris["species"]

Split the dataset into training and testing sets

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)

Train a KNN classifier with k=1

knn = KNeighborsClassifier(n_neighbors=1) knn.fit(x_train, y_train)

Take user input for new data point

user_input = input("Enter values for sepal length, sepal width, petal length, and petal width (comma-separated): ") try: # Convert user input string into a list of float values x_new = [float(i) for i in user_input.split(',')]

# Make prediction based on user input
prediction = knn.predict([x_new])
print("Prediction: {}".format(prediction))

except ValueError: print("Invalid input. Please enter comma-separated float values.")

So this is how you can train a machine learning model for the task of Iris classification using Python. Iris Classification is one of the most popular case studies among the data science community.

this project is developed by ansh singh of vytoflow tech
all copyright claim is to vytoflow . It comes under MIT license thus it is open for any changes and suggestion you can copy the code and run this in google colab


.. Further improvements - 1. website of same using streamlin or flask can be made 
2. tensor flow , open cv , etc. libraries can be used to further improve this project on image recognition basis .