Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- | style="border-top:none;border-bottom:0.75pt solid #0000..."

2025-06-09T13:12:20Z

Created page with " <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- | style="border-top:none;border-bottom:0.75pt solid #0000..."

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{| border="1"
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|| '''Visual Cue'''
|| '''Narration'''
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'''Welcome '''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Welcome to the Spoken Tutorial on '''K Nearest Neighbor Regression.'''
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'''Learning Objectives'''

| style="border-top:none;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | In this tutorial, we will learn about
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Distance metrics''' for nearest neighbor identification.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Applying '''KNN''' regression to predict the petal length of '''iris''' flowers.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Evaluation using '''MSE''' and '''Adjusted R squared score.'''</div>

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'''System Requirements'''
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Ubuntu Linux OS 24.04'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Jupyter Notebook IDE'''</div>

|| 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>

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'''Pre-requisites'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | 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 pre-requisite '''Python''' tutorials, please visit this website.</div>

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'''Code files'''
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* <div style="margin-left:1.27cm;margin-right:0cm;">The files used in this tutorial are provided in the Code files link.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Please download and extract the files.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Make a copy and then use them while practicing.</div>

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'''KNN Regression'''
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* <div style="margin-left:1.27cm;margin-right:0cm;">'''KNN regression''' is an algorithm that predicts values using nearby data points.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">The prediction in regression is based on the average value of the target variable.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''K''' indicates the''' number of neighboring points '''to be considered for prediction.</div>

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'''Distance Metrics'''
| style="border:0.75pt solid #000000;padding:0.106cm;" | The various distance metrics used in '''KNN''' for finding Nearest Neighbors are
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Euclidean distance '''measures the shortest path between two points in space.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Scikit-learn '''library uses '''Euclidean '''as the default distance metric.</div>

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'''Distance Metrics'''
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* <div style="margin-left:1.27cm;margin-right:0cm;">'''Manhattan distance '''is the sum of absolute differences between coordinates.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Minkowski '''and '''Chebyshev '''distances are other common distance metrics used in KNN.</div>

|-
| style="border-top:none;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | Hover over the file
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | I have created the required file for the demonstration of '''KNN regression.'''<span style="color:#ff0000;"> </span>
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| style="border-top:none;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | Point to the '''KNNregression.ipynb'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | '''KNNregression dot ipynb '''is the ipython notebook file for this demonstration.
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| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | Press '''Ctrl,Alt'''+'''T '''keys

Type '''conda activate ml'''

Press '''Enter'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Let us open the Linux terminal by pressing '''Ctrl,Alt''' and '''T '''keys together.

Activate the machine learning environment as shown.
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| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | Go to the '''Downloads '''folder

Type '''cd Downloads'''

Press '''Enter '''

Type '''jupyter notebook'''

Press '''Enter '''
| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | 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.'''
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| style="border:0.5pt solid #000000;padding:0.106cm;" | Show '''Jupyter Notebook Home page'''

Click on

'''KNNregression.ipynb '''file
| style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;" | We see the '''Jupyter Notebook''' Home page.

Let us open the '''KNNregression dot ipynb''' file by '''clicking '''it.

<div style="color:#000000;">Note that each cell will have the output displayed in this file.</div>
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|| Highlight''' '''the lines:'''import pandas as pd '''

'''import matplotlib.pyplot as '''

'''import seaborn as sns '''

|| These are the necessary libraries to be imported for '''KNN '''regression.

Please remember to Execute the cell by pressing '''Shift and Enter''' to get output.
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'''iris = load_iris()'''

Press''' Shift '''and '''Enter'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | First, we load the dataset into a variable named '''iris.'''

<span style="background-color:#ffffff;">We are using the </span><span style="background-color:#ffffff;">'''Iris dataset'''</span><span style="background-color:#ffffff;">, loading it from the </span><span style="background-color:#ffffff;">'''sklearn library.'''</span>
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'''iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)'''

'''iris_df['target'] = iris.target'''

Press '''Shift '''and''' Enter'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We create a '''DataFrame''' with feature names as '''columns'''.

Then we create a new column '''target''' and assign class labels to it.
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'''iris_df'''

Press''' Shift '''and '''Enter'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Now we display the '''Dataframe''' showing all the feature values and target labels.
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'''print("Length of Dataset:",'''

Press''' Shift '''and '''Enter'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Then we print the dataset length, shape, and the names of all features.

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'''target_feature = 'petal length (cm)''''

Press''' Shift '''and''' Enter'''
| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | After that we store petal length as the target feature name.
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'''X = iris_df.drop(columns=[target_feature, 'target'],axis=1)'''
| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Now we separate the '''features X''' and the '''target variable y'''.
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'''X '''

Press''' Shift '''and '''Enter'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We see that feature set '''X''' contains all features except the petal length.
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'''y '''

Press''' Shift '''and '''Enter'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | The target set '''y''' contains the target feature petal length.
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'''plt.figure(figsize=(10, 6))'''

'''sns.boxplot(data=iris_df.drop(columns=['target']))'''

| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Now we create a '''boxplot''' to visualize feature distributions '''before scaling'''.

The boxplot shows how the features vary, their range, and any unusual values.
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'''scaler = StandardScaler()'''

'''X_scaled = scaler.fit_transform(X) '''

Press''' Shift '''and '''Enter'''
|| Next, we apply Standard Scaling to normalize features using '''StandardScaler.'''

'''X underscore scaled''' is the transformed data.

It contains the data with '''mean 0''' and '''standard deviation 1'''.
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'''plt.figure(figsize=(10, 6))'''
| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Then, we plot a '''boxplot''' to show feature distributions '''after scaling.'''

This helps us visualize how '''standardization''' affects the data.

We can observe in the output that all features are scaled to a mean of 0.

The standard deviation of all the features is now 1.
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| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Next, we split the data into '''training''' and '''testing sets'''.
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'''mse_scores = []'''

'''K_range = range(1, 15)'''

Press''' Shift '''and '''Enter'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We use the '''Elbow''' method to help identify the optimal number of neighbors.
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'''plt.figure(figsize=(10, 6))'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Now, we visualize the '''Elbow''' method for KNN regression using a plot.
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| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We can observe that the error decreases initially and then increases after a point.

In the plot, the lowest '''MSE''' value appears to be at '''K''' '''equals''' '''5 '''and '''2.'''

So, we infer that the model performs best at '''K equals 5 '''or '''2.'''

After '''K''' equals '''5''', the '''MSE''' increases, suggesting no further improvement.
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'''knn = KNeighborsRegressor(n_neighbors=5) '''

Press''' Shift '''and '''Enter'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Then we initialize the '''KNN Regressor''' using the '''KNeighborsRegressor '''function.

Our ideal '''K '''value is '''5''', so we initialize '''KNN regressor''' with''' 5''' neighbors.
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'''knn.fit(X_train, y_train)'''

Press''' Shift '''and '''Enter'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Further, we train the '''KNN regressor '''using the '''fit''' method on train data.
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'''y_train_pred = knn.predict(X_train)'''

Press '''Shift''' and '''Enter'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Now, we predict labels for the '''training set.'''
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'''training_mse = mean_squared_error(y_train, y_train_pred)'''

Press''' Shift '''and '''Enter'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We then calculate the '''Mean Squared Error '''for the training set.
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'''def adjusted_r2_score(y_true, y_pred, n, p):'''

| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Then, we define the function for '''Adjusted R squared score '''for regression.

It adjusts for the number of predictors.
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'''n_train = X_train.shape[0]'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | The '''training underscore adj underscore r2''' computes the adjusted R square score.
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| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We print the '''MSE''' and '''Adjusted R squared score''' of the training set.
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'''Training Mean Squared Error: 0.079'''

'''Training Adjusted R^2 Score: 0.973'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Training '''MSE''' of '''0.079''' indicates the model has low error.

It implies good performance.

The training '''Adjusted R squared score''' is '''0.973.'''

It means the model does a great job of predicting the data accurately.
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'''y_pred = knn.predict(X_test) '''

Press''' Shift '''and '''Enter'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Let us predict '''labels''' for the '''test set'''.
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'''print(comparison_df) '''

Press''' Shift '''and '''Enter'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We compare '''actual''' and '''predicted values''' to assess model accuracy.

This helps evaluate how well the model generalizes to new data.
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'''plt.figure(figsize=(6, 4))'''

'''sns.scatterplot(x=y_test, y=y_pred)'''

'''plt.show()'''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Now, we plot a scatter plot which compares '''actual vs. predicted values.'''
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| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | '''Show the output:'''

| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We can observe in the output that most points align with the red dashed line.

The red dashed line represents a perfect prediction match.
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'''test_mse = mean_squared_error(y_test, y_pred)'''

Press''' Shift '''and '''Enter'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Now we calculate '''Mean Squared Error''' of the regression model for the test set.
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'''n_test = X_test.shape[0]'''

'''p_test = X_test.shape[1]'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | Then we calculate the '''Adjusted R squared score''' for the test set.
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'''print("Test Mean Squared Error:", format(test_mse, ".3f")) '''
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | We print the '''MSE '''and '''Adjusted R square score '''of the '''test set'''.
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'''Test Mean Squared Error: 0.105'''

'''Test Adjusted R² Score: 0.964'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | The test '''MSE''' score indicates the model has low error on test data.

It implies good generalization on test data.

We get the '''adjusted R squared score '''of '''0.964 '''for test data.

The model fits the test data well, explaining '''96.4 percent''' of its '''variance'''.
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'''Summary'''

| style="border-top:0.5pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | This brings us to the end of the tutorial. Let us summarize.

In this tutorial, we have learnt about
* <div style="margin-left:1.27cm;margin-right:0cm;">Distance metrics for nearest neighbor identification. </div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Applying '''KNN''' regression to predict the petal length of '''iris''' flowers.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Evaluation using '''MSE''' and '''Adjusted R squared score.'''</div>

|-
| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | Show Slide:

'''Assignment'''

As an assignment, please do the following:

| style="border-top:0.5pt solid #000000;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | As an assignment, please do the following:
* <div style="margin-left:1.27cm;margin-right:0cm;">Let’s use Manhattan distance.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Modify '''knn = KNeighborsRegressor(n_neighbors=5, metric='manhattan')'''</div>

Now observe the change in '''MSE''' and '''Adjusted R squared score'''
|-
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:0.75pt solid #000000;border-right:0.75pt solid #000000;padding:0.106cm;" | Show Slide:''' '''

'''Assignment Solution '''

'''Show Man.JPG'''
| style="border-top:0.75pt solid #000000;border-bottom:0.5pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;color:#000000;" | After completing the assignment, the output should match the expected result.
|- style="border:0.5pt solid #000000;padding:0.106cm;"
|| Show Slide:

'''FOSSEE Forum'''
| style="color:#000000;" | For any general or technical questions on '''Python for Machine Learning''', visit the '''FOSSEE forum''' and post your question.
|-
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'''Thank you'''
| style="border-top:none;border-bottom:0.75pt solid #000000;border-left:none;border-right:0.75pt solid #000000;padding:0.106cm;" | <div style="color:#000000;">This is '''Harini Theiveegan''',a FOSSEE Summer Fellow 2025, IIT Bombay signing off</div>

<div style="color:#000000;">Thanks for joining.</div>
|-
|}

Python-for-Machine-Learning/C2/K-Nearest-Neighbor-Regression/English - Revision history

Nirmala Venkat: Created page with " {| border="1" |- || '''Visual Cue''' || '''Narration''' |- | style="border-top:none;border-bottom:0.75pt solid #0000..."

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- | style="border-top:none;border-bottom:0.75pt solid #0000..."