Madhurig at 11:37, 8 July 2025

2025-07-08T11:37:01Z

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;padd..."

2025-06-19T10:20:07Z

Created page with " <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;padd..."

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{| border="1"
|-
|| '''Visual Cue'''
|| '''Narration'''
|-
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show slide:</div>

<div style="color:#000000;">'''Welcome'''</div>
|| Welcome to the Spoken Tutorial on''' Linear Regression'''.

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|| Show Slide:

'''Learning Objectives'''

|| In this tutorial, we will learn about
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Linear Regression'''</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Simple Linear Regression'''</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Multiple Linear Regression'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Evaluation Metrics'''</span></div>

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|| Show Slide:

'''System Requirements'''

|| To Record this tutorial, I am using
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Ubuntu Linux operating system 24.04'''</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;color:#000000;">'''Jupyter Notebook'''</span><span style="background-color:transparent;color:#000000;"> </span><span style="background-color:transparent;color:#000000;">'''IDE'''</span></div>

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|| Show Slide:

'''Prerequisite'''
|| To follow this tutorial,
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;color:#000000;">The learner must have basic knowledge of </span><span style="background-color:transparent;color:#000000;">'''Python.'''</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;">For prerequisite '''Python''' tutorials, please visit this website.</div>
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|| Show Slide:

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

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|| Show Slide:

'''Linear Regression'''

||
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Linear regression''' is a predictive technique used in machine learning. </div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It builds the relationship between a '''dependent''' and '''independent''' variable.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;color:#000000;">Linear regression is categorized into </span><span style="background-color:transparent;color:#000000;">'''Simple'''</span><span style="background-color:transparent;color:#000000;"> and </span><span style="background-color:transparent;color:#000000;">'''Multiple linear regression'''</span><span style="background-color:transparent;color:#000000;">.</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Simple Linear Regression'''

||
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;">'''Simple Linear Regression '''</span><span style="background-color:transparent;">is a way to find </span>relationships<span style="background-color:transparent;"> between two variables.</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It studies how one '''independent variable''' affects one '''dependent variable'''.</div>
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|| Show Slide:

'''Multiple Linear Regression'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;">'''Multiple linear Regression'''</span><span style="background-color:transparent;"> is an extension of simple linear regression.</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It examines how multiple factors influence a single outcome.</div>

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|| Show Slide:

'''Evaluation Metrics'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">To assess the model’s performance, we use '''evaluation metrics'''.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">These metrics indicate how well the '''regression model''' fits the data. </div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;">The two common metrics are </span><span style="background-color:transparent;">'''Mean Absolute Error '''</span><span style="background-color:transparent;">and </span><span style="background-color:transparent;">'''R squared score.'''</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Hover over the files
|| I have created required files for the demonstration of''' Linear Regression.'''
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|| Open the file salaries.csv and point to the fields as per narration.

Open the file salaries_mlr.csv and point to the fields as per narration.
|| To implement '''Simple Linear Regression''', we use the '''salaries dot csv '''dataset.

This dataset contains salaries based on years of experience.

We use '''salaries underscore mlr dot csv''' dataset for '''Multiple Linear Regression.'''

This dataset contains multiple columns as shown.
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|| Point to the '''LinearRegression.ipynb '''
|| '''LinearRegression dot ipynb '''is the python notebook file for this demonstration.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Press '''Ctrl,Alt'''+'''T '''keys

Type '''conda activate ml'''

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

First, we need to activate the machine learning environment.

Run the command '''conda space activate''' '''space ml.'''

Press '''Enter.'''

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Go to the '''Downloads '''folder

Type '''cd Downloads'''

Press '''Enter '''

Type '''jupyter notebook'''

Press '''Enter '''
|| 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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|| Show '''Jupyter Notebook Home page'''

Click on'''LinearRegression.ipynb file'''
|| We see the '''Jupyter Notebook''' Home page.

Click the '''LinearRegression dot ipynb''' file to open 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 numpy as np '''

'''import pandas as pd '''

Press '''Shift+Enter'''

|| We start by importing the required libraries for '''Simple''' '''Linear''' '''Regression'''.

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

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'''df_salary=pd.read_csv("salaries.csv") '''

|| Let us load the dataset into a variable called '''df underscore salary.'''
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'''df_salary.head()'''

|| Next, we display the '''first few rows''' of the data.
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'''df_salary.describe()'''

|| Now, we generate '''summary statistics''' for the numerical columns.
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'''sns.heatmap(df_salary.corr(), annot=True, cmap="coolwarm") '''

'''plt.show() '''

|| '''Correlation heatmap''' shows how attributes in the dataset are related.
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|| Narration:
|| '''Correlation''' measures how two variables are related to each other.

'''Correlation''' measures the relationship between two variables

<div style="color:#000000;">The '''correlation''' '''values range from -1 to 1'''.</div>

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|| Show the Correlation matrix output 4.47

|| <div style="color:#000000;">Here, experience and income have a correlation of '''0.97.'''This means that as '''experience increases''', '''income also increases''' strongly.</div>

Let us understand the correlation value ranges.
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|| Show Slide:

'''Correlation Matrix'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">A value of '''1''' means a '''perfect''' '''positive correlation'''.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">A value of '''-1''' means a '''perfect negative correlation'''.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">A value of '''0 '''means '''no correlation'''</div>

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|| Highlight the lines:

'''plt.figure(figsize=(6,4)) '''

|| Now we create a '''boxplot''' to visualize the income distribution.
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|| Show the output

|| This image is a boxplot of income '''before removing outliers.'''

'''Outliers''' are extreme values that differ significantly from other data points.

They are the small circles on the right side of the boxplot.

Here, incomes around 60,000 to 65,000 are considered as outliers.

The line inside the box is the '''median'''.

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'''<nowiki>Q1 = df_salary[['experience', </nowiki>'income']].quantile(0.25)  '''

'''<nowiki>Q3 = df_salary[['experience', 'income']].</nowiki>quantile(0.75)  '''

'''IQR = Q3 - Q1 '''

|| Next, we will '''remove these outliers''' using the '''Interquartile Range''' method.

We calculate '''first quartile Q1''' and '''third quartile Q3''' for experience and income.

Then, we compute the''' IQR '''and remove the outliers.

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|| Highlight the lines:

'''plt.figure(figsize=(6,4)) '''

|| Now, we plot the income distribution after '''removing outliers'''.

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|| Show the output

|| Observe that the small circles are gone, showing outliers were removed.

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'''x=df_salary['experience'] '''

'''y=df_salary['income'] '''
|| Now, we define '''x''' as '''experience''' and '''y''' as '''income''' from the dataset.
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|| Then, we split the data into '''training''' and '''testing''' '''sets'''.

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'''x_train=np.array(x_train).reshape(-1,1) '''

'''x_test=np.array(x_test).reshape(-1,1)'''
|| We then reshape the '''x underscore train''' lists into '''2D array.'''

The same is done for '''x underscore test''' for '''compatibility.'''

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'''lr=LinearRegression() '''

'''lr.fit(x_train,y_train)'''

|| Now, we initialize a '''Linear Regression model''' and train it using training data.
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'''print("Intercept (W0):", lr.intercept_)'''

'''print("Coefficient (W1):", lr.coef_)'''

|| Then, we print the '''intercept W0''' and '''coefficient W1''' of the model.

These define the model’s '''slope and relationship''' between experience and income.

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'''y_pred_train = lr.predict(x_train) '''

'''y_pred_train = y_pred_train.round().astype(int) '''

'''y_pred_train '''
|| Now, we use the trained model to make '''predictions on the training data.'''

We round the predictions to whole numbers for better readability.

Then, we display the rounded predictions.

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'''mae_train = mean_absolute_error(y_train, y_pred_train) '''

'''print("MAE (Training):", mae_train)'''

|| Next, we calculate the '''Mean Absolute Error''' on the training data.

'''Mean Absolute Error''' measures '''prediction accuracy.'''

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'''r2_score(y_pred_train, y_train) '''
|| Then, we compute the '''R squared score''' to evaluate the model’s performance.

'''R squared score''' measures how well the model explains the '''variance''' in the data.

A '''value closer to''' '''1''' indicates a '''stronger fit.'''

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'''y_pred_test = lr.predict(x_test) '''

'''y_pred_test = y_pred_test.round().astype(int) '''

'''y_pred_test '''

|| Now, we make predictions on the test data.
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'''plt.scatter(x_test,y_test) '''

|| To visualize performance, we create a '''scatter plot of actual vs predicted values'''.

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|| Show the output
|| In the output we can see that most points are close to the line.

It shows a '''positive correlation.'''

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'''mean_absolute_error(y_test,y_pred_test) '''

|| Now, compute the '''Mean Absolute Error '''on the test data.

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'''r2_score(y_pred_test, y_test) '''
|| Then, we calculate and display the '''R squared score'''.
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|| Narration

|| The model has a '''Mean Absolute Error of 1626.41''', indicating prediction errors.

The '''R-squared score of 0.87''' shows the model explains most of the variance.

Overall, the model performs well but has some prediction errors.

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||
|| Now let us see the implementation of '''Multiple Linear Regression'''.

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'''df_salaries = pd.read_csv(r"salaries_mlr.csv") '''

|| First, load the dataset for '''Multiple Linear Regression'''.

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'''df_salaries.tail()'''

|| Then, we display the '''last five rows.'''
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'''df_salaries.dtypes'''

|| Next, we check the '''data types''' of each column in the dataset.
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'''df_salaries.isnull().sum()'''
|| We also check for any '''missing values''' in the dataset by summing them.

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'''df_salaries['gender'] = df_salaries['gender'].map({'m': 1, 'f': 0}) '''

|| Now, we convert '''gender column''' to numeric values, '''1 for Male''' and '''0 for Female'''.
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'''X = df_salaries.drop(columns='income')'''

'''y = df_salaries['income']'''

|| Then, we separate the '''features X''' and the '''target variable y''' for prediction.
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'''X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) '''

|| Now, we split the data into '''training and testing sets.'''
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'''model = LinearRegression()'''

'''model.fit(X_train, y_train)'''

|| We initialize a''' Linear Regression model''' and train it using the training data.
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'''coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_}) '''

|| Next, we print the model's '''coefficients and intercept'''.

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'''y_train_pred = model.predict(X_train) '''

'''y_train_pred = y_train_pred.round().astype(int) '''

'''y_train_pred'''
|| Now, we make '''predictions on the training data.'''
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'''mae_train = mean_absolute_error(y_train, y_train_pred) print(f'Training data MAE: {mae_train}') '''

|| Next, we compute the '''Mean Absolute Error for training data'''.
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'''r2_train = r2_score(y_train, y_train_pred) '''

'''n_train = len(y_train'''
|| Then, we computethe '''R squared score''' to measure the model performance

After that, we compute and print the '''adjusted R squared '''score.

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'''y_test_pred = model.predict(X_test) '''

'''y_test_pred = y_test_pred.round().astype(int) '''

|| Moving forward, we make '''predictions on the test data.'''
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'''plt.scatter(y_test, y_test_pred, color='red', label='Predicted') '''

'''plt.scatter(y_test, y_test, color='blue', alpha=0.5, label='Actual') '''
|| We compare '''actual vs predicted income''' using a '''scatter plot.'''
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'''mae_test = mean_absolute_error(y_test, y_test_pred) print(f'Testing data MAE: {mae_test}')'''

|| Then, we compute the '''Mean Absolute Error''' for the test data.
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'''r2_test = r2_score(y_test, y_test_pred) '''

'''n_test = len(y_test) '''

'''k_test = X_test.shape[1] '''
|| Next, we calculate the '''R squared score '''for the test data.
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|| Narration
|| The model has an '''MAE''' of '''1700.15''', showing the average prediction error in income.The '''Adjusted R squared score''' is '''0.921'''.

It indicates the model explains '''92.1 percent''' of income variance.

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'''residuals = y_test - y_test_pred '''

'''plt.show()'''

|| Now, we analyse the '''residuals''' to check model errors.

We create a '''scatter plot''' of '''predicted values versus residuals.'''
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|| Highlight the output

|| This is a '''residual plot''' for the regression model.

The '''red dashed line''' represents '''zero residual.'''

Points above the line mean predictions are lower than actual values.

Points below the line mean predictions are higher than actual values.

Most '''residuals''' are '''close to zero''', meaning predictions are fairly accurate.

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|| Narration
|| Thus, we successfully implemented '''Multiple Linear Regression'''.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show slide:

'''Summary'''
|| 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;">'''Linear Regression'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Simple Linear Regression'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Multiple Linear Regression'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;color:#000000;">'''Evaluation Metrics'''</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Assignment'''

In Multiple Linear Regression code,
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="background-color:transparent;color:#000000;">Replace the test_size parameter as shown here.</span></div>

<div style="margin-left:1.27cm;margin-right:0cm;"></div>
|| In Multiple Linear Regression code,

* <div style="margin-left:1.27cm;margin-right:0cm;">R<span style="background-color:transparent;color:#000000;">eplace the </span><span style="background-color:transparent;color:#000000;">'''test_size parameter'''</span><span style="background-color:transparent;color:#000000;"> as shown here.</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Ob<span style="background-color:transparent;color:#000000;">serve the change in </span><span style="background-color:transparent;color:#000000;">'''MAE '''</span><span style="background-color:transparent;color:#000000;">and </span><span style="background-color:transparent;color:#000000;">'''Adjusted R squared score'''</span><span style="background-color:transparent;color:#000000;">.</span></div>

<div style="color:#000000;"></div>
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Assignment Solution'''

Show s1 img file
|| After completing the assignment, the output should match the expected result.
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|| Show Slide:

'''FOSSEE Forum'''
|| For any general or technical questions on <span style="background-color:#ffffff;">'''Python</span><span style="background-color:#ffffff;"> for Machine Learning'''</span>, visit the''' FOSSEE forum''' and post your question.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.191cm;padding-right:0.191cm;"
|| Show Slide:

'''Thank you'''
|| <div style="color:#000000;">This is '''Harini Theiveegan''', a FOSSEE Summer Fellow 2025, IIT Bombay signing off</div>

Thanks for joining.

|-
|}

← Older revision		Revision as of 10:28, 4 July 2025
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Python-for-Machine-Learning/C2/Linear-Regression/English - Revision history

Madhurig at 11:37, 8 July 2025

Madhurig at 10:28, 7 July 2025

Madhurig at 10:21, 7 July 2025

Madhurig at 10:28, 4 July 2025

Madhurig at 10:18, 4 July 2025

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;padd..."

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 |-
 || Show the Correlation matrix output 4.47
-|| Here, experience and income have a correlation of '''0.97.'''This means that as '''experience increases''', '''income also increases''' strongly.
+This means that as experience increases, income also increases strongly.
 Let us understand the correlation value ranges.
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 |-
 || Highlight the lines:
 || Then, we split the data into '''training''' and '''testing''' '''sets'''.
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 |-
 || Narration
-|| The model has an '''MAE''' of '''1700.15''', showing the average prediction error in income.The '''Adjusted R squared score''' is '''0.921'''.
+|| The model has an '''MAE''' of '''1700.15''', showing the average prediction error in income.
 It indicates the model explains '''92.1 percent''' of income variance.

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 {| border="1"
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 || Show slide:
 '''Welcome'''
-|| Welcome to the Spoken Tutorial on '''Linear Regression'''.
+|| Welcome to the Spoken Tutorial on''' Linear Regression'''.
 |-

Python-for-Machine-Learning/C2/Linear-Regression/English - Revision history

Madhurig at 11:37, 8 July 2025

Madhurig at 10:28, 7 July 2025

Madhurig at 10:21, 7 July 2025

Madhurig at 10:28, 4 July 2025

Madhurig at 10:18, 4 July 2025

Nirmala Venkat: Created page with " {| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;padd..."

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- |- style="border:0.5pt solid #000000;padding-top:0cm;padd..."