Python-for-Machine-Learning/C3/K-Means-Clustering/English - Revision history

Madhurig at 07:49, 14 July 2025

2025-07-14T07:49:55Z

Madhurig at 10:39, 9 July 2025

2025-07-09T10:39:23Z

Madhurig at 09:55, 9 July 2025

2025-07-09T09:55:10Z

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Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: || Welcome to the Spoken Tutorial on''' K..."

2025-06-30T16:21:49Z

Created page with " <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: || Welcome to the Spoken Tutorial on''' K..."

New page

<div style="margin-left:1.27cm;margin-right:0cm;"></div>
{| border="1"
|-
|| '''Visual Cue'''
|| '''Narration'''
|-
|| Show Slide:
|| Welcome to the Spoken Tutorial on''' K-means Clustering.'''
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | In this tutorial, we will learn about
* <div style="margin-left:1.27cm;margin-right:0cm;">Introduction to''' K-means Clustering'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Steps involved in''' K-means Algorithm'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Choosing the''' '''Optimal''' '''number of''' Clusters (k)'''</div>
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''System Requirements'''
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | To Record this tutorial, I am using
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Ubuntu Linux OS version 24.04'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Jupyter Notebook''' '''IDE'''</div>
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | To follow this tutorial,
* <div style="margin-left:1.27cm;margin-right:0cm;">The Learner must have basic knowledge of '''Python''' </div>
* <div style="margin-left:1.27cm;margin-right:0cm;">For prerequisite '''Python''' tutorials, please visit this website</div>

|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''Code files'''

| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" |
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#252525;">The files used in this tutorial are provided in the </span><span style="color:#252525;">'''Code files'''</span><span style="color:#252525;"> link.</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#252525;">Please download and extract the files.</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#252525;">Make a copy and then use them while practicing.</span></div>
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''K-means Clustering'''

| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" |
* <div style="margin-left:1.27cm;margin-right:0cm;">'''K-means clustering''' is an '''unsupervised''' machine learning algorithm.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It partitions data into a predetermined number of '''clusters'''.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">This algorithm aims to group similar '''data points''' together.</div>
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''K-means Clustering'''
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" |
* <div style="margin-left:1.27cm;margin-right:0cm;">It creates '''distinct clusters''' by separating dissimilar points.</div>
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" |

Show Slide:

'''Working '''
'''working.png'''
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Let us see the working of '''K-means clustering'''.
* <div style="margin-left:1.27cm;margin-right:0cm;">First, choose the number of clusters, '''k''', to divide the data.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Then, randomly pick''' k data points''' as''' initial centroids '''which is the center of each cluster.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It represents the average position of all points in that '''cluster'''.</div>

|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Show Slide:

'''Working'''
'''working.png'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">Each '''data point''' is assigned to the nearest '''cluster centroid'''.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Update the centroids by averaging the '''data points''' in each '''cluster'''.</div>

|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Show Slide:

'''Working'''
'''working.png'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;">New </span><span style="background-color:transparent;">'''centroids'''</span><span style="background-color:transparent;"> replace the old ones based on these mean values.</span></div>

* <div style="margin-left:1.27cm;margin-right:0cm;">This is repeated until the '''cluster''' assignments no longer change.</div>

|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Hover over the files

Point to '''KMeansClustering.ipynb'''
|| I have created the required files for the demonstration of '''K-Means clustering.'''

'''K means clustering dot ipynb '''is the python notebook file used in this tutorial.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Open '''Customers.csv dataset'''
|| For this tutorial, we use a '''dataset''' of customer income and spending scores.

The goal is to '''group individuals''' based on''' income''' and '''spending patterns'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Press '''Ctrl+Alt+T '''keys

Type '''conda activate ml'''

Press '''Enter'''
|| Let us open the Linux terminal by pressing '''Ctrl,Alt and T '''keys together.

Activate the machine learning environment as shown.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| To go to the '''Downloads '''folder,

Type '''cd Downloads'''

Type '''jupyter notebook'''
|| I have saved my code file in the '''Downloads '''folder.

Please navigate to the respective folder of your code file location.

Then type, '''jupyter space notebook '''and press '''Enter.'''
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Show Jupyter Notebook Home Page:Double click on

'''KMeansClustering.ipynb '''file
|| We can see the Jupyter Notebook Home page has opened in the web browser.

Click the '''K means clustering dot ipynb''' file to open it.

<div style="color:#000000;">Note that each cell will have the output displayed in this file.</div>

Let us see the implementation of the '''K-Means Clustering'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''import numpy as np'''

'''import pandas as pd'''

|| First, we import the '''necessary libraries''' required for '''K-means Clustering'''.

Make sure to Press''' Shift '''and''' Enter''' to execute the code in each cell.

|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

|| We load the '''customers dataset''' into the '''df''' dataframe.
Let us display the first few rows.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''print(df.describe())'''

Hover over the output
|| To understand the data distribution, we use the '''describe''' function.

The dataset contains '''200''' entries for '''Annual Income''' and '''Spending Score'''.

'''Income ranges '''from''' 15K to 137K''', while the '''spending score varies '''from''' 1 to 99'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''df.isnull()'''

'''Hover over the output'''
|| We check for''' missing values''' in the dataset using '''isnull '''function.

The output shows '''False''' for all entries, confirming '''no missing values'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
||
|| To visualize the dataset, we plot '''histograms''' for each numerical feature.

We also use '''boxplots''' to detect potential '''outliers'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Hover over the output plots as per the narration
|| The''' histogram''' reveals the distribution of '''annual income''' and '''spending scores'''.

'''Income''' is more spread out, while '''spending scores''' form '''distinct clusters'''.

The boxplot identifies an outlier in the '''Annual Income''' feature.

It is indicated by a point above the '''upper whisker'''.

The '''Spending Score''' feature does not exhibit clear outliers.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Only narration

Highlight:

'''scaler = MinMaxScaler()'''

'''scaled_df = scaler.fit_transform(df)'''
|| Now, let us preprocess the dataset.

First, we normalize the dataset using '''MinMaxScaler''' to scale feature values.

This transformation ensures all values fall between 0 and 1.

It improves uniformity and enhances the clustering performance.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''silhouette_scores = []'''

'''K_range = range(2, 11) '''

'''for K in K_range:'''

'''kmeans = KMeans(n_clusters=K, random_state=42, n_init=10)'''

'''cluster_labels = kmeans.fit_predict(scaled_df)'''

'''score = silhouette_score(scaled_df, cluster_labels)'''

'''silhouette_scores.append(score)'''
'''plt.show()'''

|| Next, we find the '''optimal number of clusters''' using the '''Silhouette Score method'''.

The highest '''Silhouette score''' suggests the best '''k''' value.

To do this, we initialize an empty list to store '''silhouette scores'''.

The for loop iterates through a range of''' K''' values to perform clustering.

For each K, a '''KMeans''' model is created and trained.

Cluster labels are predicted, and the '''silhouette score''' is calculated.

The scores are stored for further analysis.

Further, we generate a plot to visualize the '''silhouette scores'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Only narration

Hover over output plot
|| Ignore the warning in the output cell.

The plot shows '''silhouette scores''' for different '''cluster '''values.

A higher score means better defined '''clusters''' with less overlap.

In this plot, the optimal '''k''' is 5 as it has the highest '''silhouette score'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''optimal_k = 5'''
|| We store the''' optimal k '''value in the '''optimal underscore k''' variable.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Only Narration

Highlight:
|| Next, we create a '''K-means model''' using the optimal '''k value'''.

The model is fitted to the '''scaled data''', assigning cluster labels.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:
|| We extract the '''cluster centers''' from the '''trained K-means '''model.

The coordinates of these centers are printed for analysis.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Show output and hover over it
|| Each '''cluster center''' represents the average value of points in that cluster.

Each row corresponds to a '''centroid’s position''' for '''income '''and''' spending score'''.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''df['Cluster'] = kmeans.labels_'''
|| We then assign each data point to its respective cluster.

A new column '''Cluster''' is added to store cluster labels.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''silhouette_avg = silhouette_score(scaled_df, kmeans.labels_)'''

'''print("Silhouette Score: ", silhouette_avg)'''
|| Next, we calculate the '''silhouette score''' to evaluate clustering quality.

A''' higher score''' indicates well-separated and dense clusters.

|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Show the output
|| The '''silhouette score''' of '''0.559''' suggests a '''moderate clustering quality'''.

<span style="color:#252525;">A score close to </span><span style="color:#252525;">'''-1'''</span><span style="color:#252525;"> means the data point is </span><span style="color:#252525;">'''poorly clustered'''</span><span style="color:#252525;">.</span>

<span style="color:#252525;">A score close to </span><span style="color:#252525;">'''1'''</span><span style="color:#252525;"> means the data point is </span><span style="color:#252525;">'''well-clustered'''</span><span style="color:#252525;">.</span>

This suggests that while '''clusters''' are distinguishable, some overlap may exist.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Highlight:

'''plt.figure(figsize=(8, 5)) '''

'''plt.show()'''
|| Finally, we plot data points using the '''scaled income''' and '''spending score''' values.

To enhance clarity, each point is color-coded based on its cluster assignment.

The cluster centers are marked with red X for easy identification.

This visualization helps to understand how data points are grouped.
|- style="background-color:transparent;border:1pt solid #000000;padding:0.176cm;"
|| Show the output plot
|| The scatter plot visualizes '''K-means clustering''' results.

Each dot represents a '''data point''', color-coded by its assigned '''cluster'''.

The''' clusters''' show distinct groupings based on '''annual income''' and''' spending score'''.
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''Summary'''
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | This brings us to the end of the tutorial.

Let us summarize.
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''Assignment'''
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | <div style="color:#000000;">As an assignment,</div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">Use '''Elbow''' method instead of''' Silhouette''' method to find the optimal k</div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">Use '''inertia''' to identify it</div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">Then, plot the '''elbow''' curve</div>

<div style="color:#000000;"></div>
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''Assignment'''

Show elbow.png
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Refer to the code given:
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''Assignment Solution'''

Show plot.png
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | After completing the assignment, we’ll get the following plot.
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''FOSSEE Forum'''
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | For any general or technical questions on <span style="background-color:#ffffff;">'''Python for Machine Learning'''</span>, visit the''' FOSSEE forum''' and post your question
|-
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | Show Slide:

'''Thank you'''
| style="border:1pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | This is '''Anvita Thadavoose Manjummel''', a FOSSEE Summer Fellow 2025, IIT Bombay signing off

Thanks for joining.
|-
|}
<div style="margin-left:0cm;margin-right:0cm;"></div>

@@ Line 6: / Line 6: @@
 |-
 || Show Slide:
-|| Welcome to the Spoken Tutorial on ''' K-means Clustering.'''
+|| Welcome to the Spoken Tutorial on ''' K-means Clustering'''.
 |-
 || Show Slide:
@@ Line 19: / Line 19: @@
 || To Record this tutorial, I am using
 * '''Ubuntu Linux OS version 24.04'''
-* '''Jupyter Notebook''' '''IDE'''
+* '''Jupyter Notebook IDE'''
 |-
 || Show Slide:
@@ Line 25: / Line 25: @@
 || To follow this tutorial,
 * The Learner must have basic knowledge of '''Python'''
-* For prerequisite '''Python''' tutorials, please visit this website
+* For prerequisite '''Python''' tutorials, please visit this website.
 |-
@@ Line 87: / Line 87: @@
 || I have created the required files for the demonstration of '''K-Means clustering.'''
-'''K means clustering dot ipynb '''is the python notebook file used in this tutorial.
+'''K means clustering dot ipynb ''' is the python notebook file used in this tutorial.
 |-
 || Open '''Customers.csv dataset'''
@@ Line 117: / Line 117: @@
 '''KMeansClustering.ipynb '''file
-|| We can see the Jupyter Notebook Home page has opened in the web browser.
+|| We can see the '''Jupyter Notebook Home''' page has opened in the web browser.
 Click the '''K means clustering dot ipynb''' file to open it.
@@ Line 133: / Line 133: @@
 || First, we import the '''necessary libraries''' required for '''K-means Clustering'''.
-Make sure to Press''' Shift '''and''' Enter''' to execute the code in each cell.
+Make sure to Press''' Shift ''' and ''' Enter''' to execute the code in each cell.
 |-
@@ Line 139: / Line 139: @@
 || We load the '''customers dataset''' into the '''df''' dataframe.
 Let us display the first few rows.
 |-
@@ Line 171: / Line 172: @@
 '''Income''' is more spread out, while '''spending scores''' form '''distinct clusters'''.
-The '''boxplot''' identifies an outlier in the '''Annual Income''' feature.
+The '''boxplot''' identifies an '''outlier''' in the '''Annual Income''' feature.
 It is indicated by a point above the '''upper whisker'''.
-The '''Spending Score''' feature does not exhibit clear outliers.
+The '''Spending Score''' feature does not exhibit clear '''outliers'''.
 |-
 || Only narration
@@ Line 239: / Line 240: @@
 '''optimal_k = 5'''
-|| We store the''' optimal k '''value in the '''optimal underscore k''' variable.
+|| We store the ''' optimal k '''value in the '''optimal underscore k''' variable.
 |-
 || Only Narration
@@ Line 272: / Line 273: @@
 || Next, we calculate the '''silhouette score''' to evaluate clustering quality.
-A''' higher score''' indicates well-separated and dense clusters.
+A higher score  indicates well-separated and dense clusters.
 |-
@@ Line 278: / Line 279: @@
 || The '''silhouette score''' of '''0.559''' suggests a '''moderate clustering quality'''.
-A score close to '''-1''' means the data point is '''poorly clustered'''.
+A score close to '''-1''' means the data point is  poorly clustered .
-A score close to '''1''' means the data point is '''well-clustered'''.
+A score close to '''1''' means the data point is  well-clustered.
 This suggests that while '''clusters''' are distinguishable, some overlap may exist.

@@ Line 1: / Line 1: @@
 {| border="1"
@@ Line 8: / Line 6: @@
 |-
 || Show Slide:
-|| Welcome to the Spoken Tutorial on''' K-means Clustering.'''
+|| Welcome to the Spoken Tutorial on ''' K-means Clustering.'''
 |-
 || Show Slide:
@@ Line 14: / Line 12: @@
 * Introduction to''' K-means Clustering'''
 * Steps involved in''' K-means Algorithm'''
-* Choosing the''' '''Optimal''' '''number of''' Clusters (k)'''
+* Choosing the Optimal number of ''' Clusters (k)'''
 |-
 || Show Slide:
@@ Line 54: / Line 52: @@
 * It creates '''distinct clusters''' by separating dissimilar points.
 |-
-||
+|| Show Slide:
-−
 Show Slide:
 '''Working '''
 '''working.png'''
 || Let us see the working of '''K-means clustering'''.
@@ Line 102: / Line 99: @@
 Press '''Enter'''
-|| Let us open the Linux terminal by pressing '''Ctrl,Alt and T '''keys together.
+|| Let us open the Linux terminal by pressing '''Ctrl, Alt and T '''keys together.
 Activate the machine learning environment as shown.
@@ Line 124: / Line 121: @@
 Click the '''K means clustering dot ipynb''' file to open it.
-<div style="color:#000000;">Note that each cell will have the output displayed in this file.
+Note that each cell will have the output displayed in this file.
 Let us see the implementation of the '''K-Means Clustering'''.
@@ Line 153: / Line 150: @@
 The dataset contains '''200''' entries for '''Annual Income''' and '''Spending Score'''.
-'''Income ranges '''from''' 15K to 137K''', while the '''spending score varies '''from''' 1 to 99'''.
+'''Income ranges '''from''' 15K to 137K''', while the '''spending score varies '''from  1 to 99.
 |-
 || Highlight:
@@ Line 164: / Line 161: @@
 The output shows '''False''' for all entries, confirming '''no missing values'''.
 |-
-||
+|| Highlight the code.
 || To visualize the dataset, we plot '''histograms''' for each numerical feature.
@@ Line 174: / Line 171: @@
 '''Income''' is more spread out, while '''spending scores''' form '''distinct clusters'''.
-The boxplot identifies an outlier in the '''Annual Income''' feature.
+The '''boxplot''' identifies an outlier in the '''Annual Income''' feature.
 It is indicated by a point above the '''upper whisker'''.

Python-for-Machine-Learning/C3/K-Means-Clustering/English - Revision history

Madhurig at 07:49, 14 July 2025

Madhurig at 10:39, 9 July 2025

Madhurig at 09:55, 9 July 2025

Nirmala Venkat: Created page with " {| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: || Welcome to the Spoken Tutorial on''' K..."

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: || Welcome to the Spoken Tutorial on''' K..."