The Iris flower data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. The data set consists of 50 samples from each of three species of Iris (Iris Setosa, Iris virginica, and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters.

Collect the dataset from https://www.kaggle.com/datasets/arshid/iris-flower-dataset

#import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

#create the dataframe
df=pd.read_csv("E:\dataset\IRIS.csv")
df.head()

df.shape

Output : (150, 5)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
#    Column         Non-Null Count   Dtype
---  ------         --------------   -----
0    sepal_length   150 non-null     float64
1    sepal_width    150 non-null     float64
2    petal_length   150 non-null     float64
3    petal_width    150 non-null     float64
4    species        150 non-null     object
dtypes: float64(4), object(1)
memory usage: 6.0+ KB

df.describe().T

#check mssing values
df.isnull().sum()

Output:

sepal_length      0
sepal_width       0
petal_length      0
petal_width       0
species                0
dtype: int64

#Choose X and y
X=df.iloc[:,:-1].values
y=df.iloc[:,4].values

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,rando

#Scaling operation
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()

scaler.fit(X_train)

Output : StandardScaler()

X_train=scaler.transform(X_train)
X_test=scaler.transform(X_test)

y_test

Output:
array(['Iris-virginica', 'Iris-versicolor', 'Iris-setosa',
'Iris-virginica', 'Iris-setosa', 'Iris-virginica', 'Iris-setosa',
'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',
'Iris-virginica', 'Iris-versicolor', 'Iris-versicolor',
'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',
'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',
'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',
'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',
'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',
'Iris-versicolor', 'Iris-setosa', 'Iris-virginica',
'Iris-virginica', 'Iris-versicolor', 'Iris-setosa',
'Iris-versicolor'], dtype=object)

#create KNN model
from sklearn.neighbors import KNeighborsClassifier
model=KNeighborsClassifier()

#trained the model
model.fit(X_train,y_train)

Output: KNeighborsClassifier()

#predict
y_pred=model.predict(X_test)
print(y_pred)

['Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica'
'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'
'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'
'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'
'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'
'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'
'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'
'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor'
'Iris-setosa' 'Iris-virginica']

from sklearn.metrics import confusion_matrix, classification_report
print(classification_report(y_test,y_pred))
print(confusion_matrix(y_test,y_pred))

from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Accuracy: 0.9736842105263158