k means clustering using iris dataset step by step python code

 K-Means clustering is an unsupervised machine learning algorithm used to group similar data points together without predefined labels. When applied to the famous Iris dataset, it groups 150 flower samples based on their structural features: sepal length, sepal width, petal length, and petal width.

Because the Iris dataset contains three known biological species (Iris setosa, Iris versicolor, and Iris virginica), we naturally choose a target cluster size of K = 3.

1. Load and prepare data

The dataset contains 150 rows of data. Each row consists of 4 numerical features. For this process, we isolate these four feature dimensions and drop the actual species names (labels), forcing the algorithm to find patterns blindly.

2. Choose cluster count K

We choose K = 3. In scenarios where you do not know the real group count beforehand, you can run the Elbow Method to plot the Within-Cluster Sum of Squares (WCSS) against various K values, picking the point where the rate of decrease dramatically slows down.

3. Initialize random centroids

The algorithm randomly selects 3 data points from the dataset (or random coordinates within the data boundaries) to serve as initial center points, known as centroids.

4. Assign points to centroids

For every single data point in the dataset, calculate its Euclidean distance to all three centroids,Assign each data point to its closest centroid to form three temporary clusters.

5. Update centroid positions

Recalculate the position of each centroid by taking the mathematical mean of all coordinates assigned to that specific cluster

6. Repeat until convergence

Go back to Step 4 and reassign all points based on the newly calculated centroid coordinates. Repeat the assignment and update steps iteratively. The loop finishes when:

Centroids stop moving to new positions.
No data point changes its cluster assignment.
The maximum number of preset iterations is reached.

7. Evaluate and interpret

Once converged, your 150 flowers are split into three distinct mathematically optimized buckets. You can evaluate the quality of your clustering by tracking metrics like the Silhouette Score or by cross-referencing your final clusters with the original species labels to check the model's structural accuracy

from sklearn.cluster import KMeans

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

Iris = pd.read_csv(r'C:\Users\admin\Downloads\Iris.csv')

x1 = np.array(Iris['SepalLengthCm'])

x2 = np.array(Iris['PetalWidthCm'])

plt.plot()

plt.title('Dataset')

plt.scatter(x1, x2)

plt.show()

Iris

output:

Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	1	5.1	3.5	1.4	0.2	Iris-setosa
1	2	4.9	3.0	1.4	0.2	Iris-setosa
2	3	4.7	3.2	1.3	0.2	Iris-setosa
3	4	4.6	3.1	1.5	0.2	Iris-setosa
4	5	5.0	3.6	1.4	0.2	Iris-setosa
...	...	...	...	...	...	...
145	146	6.7	3.0	5.2	2.3	Iris-virginica
146	147	6.3	2.5	5.0	1.9	Iris-virginica
147	148	6.5	3.0	5.2	2.0	Iris-virginica
148	149	6.2	3.4	5.4	2.3	Iris-virginica
149	150	5.9	3.0	5.1	1.8	Iris-virginica

150 rows × 6 columns

Iris.head()

output:

Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	1	5.1	3.5	1.4	0.2	Iris-setosa
1	2	4.9	3.0	1.4	0.2	Iris-setosa
2	3	4.7	3.2	1.3	0.2	Iris-setosa
3	4	4.6	3.1	1.5	0.2	Iris-setosa
4	5	5.0	3.6	1.4	0.2	Iris-setosa

Iris.tail()

output:

Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
145	146	6.7	3.0	5.2	2.3	Iris-virginica
146	147	6.3	2.5	5.0	1.9	Iris-virginica
147	148	6.5	3.0	5.2	2.0	Iris-virginica
148	149	6.2	3.4	5.4	2.3	Iris-virginica
149	150	5.9	3.0	5.1	1.8	Iris-virginica

len(Iris)

output:

150

Iris.shape

(150, 6)

Iris.columns

output:

Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',
       'Species'],
      dtype='object')

for i,col in enumerate(Iris.columns):

    print(f'Column number {1+i} is {col}')

output:

Column number 1 is Id
Column number 2 is SepalLengthCm
Column number 3 is SepalWidthCm
Column number 4 is PetalLengthCm
Column number 5 is PetalWidthCm
Column number 6 is Species

Iris.dtypes

output:

Id                 int64
SepalLengthCm    float64
SepalWidthCm     float64
PetalLengthCm    float64
PetalWidthCm     float64
Species           object
dtype: object

Iris.info()

output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             150 non-null    int64  
 1   SepalLengthCm  150 non-null    float64
 2   SepalWidthCm   150 non-null    float64
 3   PetalLengthCm  150 non-null    float64
 4   PetalWidthCm   150 non-null    float64
 5   Species        150 non-null    object 
dtypes: float64(4), int64(1), object(1)
memory usage: 7.2+ KB

Iris.isna().sum()

output:

Id               0
SepalLengthCm    0
SepalWidthCm     0
PetalLengthCm    0
PetalWidthCm     0
Species          0
dtype: int64

Iris.isnull().sum()

output:

Id               0
SepalLengthCm    0
SepalWidthCm     0
PetalLengthCm    0
PetalWidthCm     0
Species          0
dtype: int64

Iris['Species'].value_counts()

output:

Species
Iris-setosa        50
Iris-versicolor    50
Iris-virginica     50
Name: count, dtype: int64

target_data = Iris.iloc[:,5]

target_data

output:

0         Iris-setosa
1         Iris-setosa
2         Iris-setosa
3         Iris-setosa
4         Iris-setosa
            ...      
145    Iris-virginica
146    Iris-virginica
147    Iris-virginica
148    Iris-virginica
149    Iris-virginica
Name: Species, Length: 150, dtype: object

clustering_data = Iris.iloc[:,[1,2,3,4]]

clustering_data

output:

SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
...	...	...	...	...
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

150 rows × 4 columns

fig, ax = plt.subplots(figsize=(15,7))

sns.set(font_scale=1.5)

ax = sns.scatterplot(x=Iris['SepalLengthCm'],y=Iris['SepalWidthCm'], s=70, color='#f73434', edgecolor='#f73434', linewidth=0.3)

ax.set_ylabel('Sepal Width (in cm)')

ax.set_xlabel('Sepal Length (in cm)')

plt.title('Sepal Length vs Width', fontsize = 20)

plt.show()

output:

from sklearn.cluster import KMeans

wcss=[]

for i in range(1,11):

    km = KMeans(i)

    km.fit(clustering_data)

    wcss.append(km.inertia_)

np.array(wcss)

output:

array([680.8244    , 152.36870648,  78.94506583,  57.34540932,
        46.80170193,  44.81835983,  37.52130193,  32.69137106,
        28.29091775,  26.66453557])

fig, ax = plt.subplots(figsize=(15,7))

ax = plt.plot(range(1,11),wcss, linewidth=2, color="red", marker ="8")

plt.axvline(x=3, ls='--')

plt.ylabel('WCSS')

plt.xlabel('No. of Clusters (k)')

plt.title('The Elbow Method', fontsize = 20)

plt.show()

output:

from sklearn.cluster import KMeans

kms = KMeans(n_clusters=3, init='k-means++')

kms.fit(clustering_data)

output:

clusters = clustering_data.copy()

clusters['Cluster_Prediction'] = kms.fit_predict(clustering_data)

clusters

output:

SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Cluster_Prediction
0	5.1	3.5	1.4	0.2	1
1	4.9	3.0	1.4	0.2	1
2	4.7	3.2	1.3	0.2	1
3	4.6	3.1	1.5	0.2	1
4	5.0	3.6	1.4	0.2	1
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	2
146	6.3	2.5	5.0	1.9	0
147	6.5	3.0	5.2	2.0	2
148	6.2	3.4	5.4	2.3	2
149	5.9	3.0	5.1	1.8	0

150 rows × 5 columns

kms.cluster_centers_

output:

array([[5.9016129 , 2.7483871 , 4.39354839, 1.43387097],
       [5.006     , 3.418     , 1.464     , 0.244     ],
       [6.85      , 3.07368421, 5.74210526, 2.07105263]])

fig, ax = plt.subplots(figsize=(15,7)) 

plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 0]['SepalLengthCm'],

            y=clusters[clusters['Cluster_Prediction'] == 0]['SepalWidthCm'],

            s=70,edgecolor='teal', linewidth=0.3, c='teal', label='Iris-versicolor')

plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 1]['SepalLengthCm'],

            y=clusters[clusters['Cluster_Prediction'] == 1]['SepalWidthCm'],

            s=70,edgecolor='lime', linewidth=0.3, c='lime', label='Iris-setosa')

plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 2]['SepalLengthCm'],

            y=clusters[clusters['Cluster_Prediction'] == 2]['SepalWidthCm'],

            s=70,edgecolor='magenta', linewidth=0.3, c='magenta', label='Iris-virginica')

plt.scatter(x=kms.cluster_centers_[:, 0], y=kms.cluster_centers_[:, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)

plt.legend(loc='upper right')

plt.xlim(4,8)

plt.ylim(1.8,4.5)

ax.set_ylabel('Sepal Width (in cm)')

ax.set_xlabel('Sepal Length (in cm)')

plt.title('Clusters', fontsize = 20)

plt.show()

fig, ax = plt.subplots(figsize=(15,7)) 

plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 0]['SepalLengthCm'],

            y=clusters[clusters['Cluster_Prediction'] == 0]['SepalWidthCm'],

            s=70,edgecolor='teal', linewidth=0.3, c='teal', label='Iris-versicolor')

plt.scatter(x=kms.cluster_centers_[0, 0], y=kms.cluster_centers_[0, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)

plt.legend(loc='upper right')

plt.xlim(4,8)

plt.ylim(1.8,4.5)

ax.set_ylabel('Sepal Width (in cm)')

ax.set_xlabel('Sepal Length (in cm)')

plt.title('Individual Clusters', fontsize = 20)

plt.show()

output:

fig, ax = plt.subplots(figsize=(15,7)) 

plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 1]['SepalLengthCm'],

            y=clusters[clusters['Cluster_Prediction'] == 1]['SepalWidthCm'],

            s=70,edgecolor='lime', linewidth=0.3, c='lime', label='Iris-versicolor')

plt.scatter(x=kms.cluster_centers_[1, 0], y=kms.cluster_centers_[1, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)

plt.legend(loc='upper right')

plt.xlim(4,8)

plt.ylim(1.8,4.5)

ax.set_ylabel('Sepal Width (in cm)')

ax.set_xlabel('Sepal Length (in cm)')

plt.title('Individual Clusters', fontsize = 20)

plt.show()

output:

fig, ax = plt.subplots(figsize=(15,7)) 

plt.scatter(x=clusters[clusters['Cluster_Prediction'] == 2]['SepalLengthCm'],

            y=clusters[clusters['Cluster_Prediction'] == 2]['SepalWidthCm'],

            s=70,edgecolor='magenta', linewidth=0.3, c='magenta', label='Iris-versicolor')

output:

plt.scatter(x=kms.cluster_centers_[2, 0], y=kms.cluster_centers_[2, 1], s = 170, c = 'yellow', label = 'Centroids',edgecolor='black', linewidth=0.3)

plt.legend(loc='upper right')

plt.xlim(4,8)

plt.ylim(1.8,4.5)

ax.set_ylabel('Sepal Width (in cm)')

ax.set_xlabel('Sepal Length (in cm)')

plt.title('Individual Clusters', fontsize = 20)

plt.show()

output: