Python-for-Machine-Learning/C2/Logistic-Regression-Binary-Classification/English - Revision history

Madhurig at 13:52, 17 July 2025

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Madhurig at 07:28, 14 July 2025

2025-07-14T07:28:56Z

Madhurig at 07:24, 14 July 2025

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Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show sli..."

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Created page with " <div style="margin-left:2.54cm;margin-right: <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show sli..."

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<div style="margin-left:2.54cm;margin-right:

<div style="margin-left:1.27cm;margin-right:0cm;"></div>
{| border="1"
|-
|| '''Visual Cue'''
|| '''Narration'''
|-
|| Show slide:

'''Welcome'''
|| Welcome to the Spoken Tutorial on '''Logistic Regression - Binary Classification.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Learning Objectives'''
|| In this tutorial, we will learn about
* <div style="color:#000000;margin-left:1.905cm;margin-right:0cm;">Logistic Regression</div>
* <div style="color:#000000;margin-left:1.905cm;margin-right:0cm;">Binary Classification</div>
* <div style="color:#000000;margin-left:1.905cm;margin-right:0cm;">Multiclass Classification</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''System Requirements'''
|| To record this tutorial, I am using
* <div style="margin-left:1.905cm;margin-right:0cm;"><span style="color:#000000;">'''Ubuntu Linux '''</span><span style="color:#000000;">OS version</span><span style="color:#000000;">''' 24.04'''</span></div>
* <div style="margin-left:1.905cm;margin-right:0cm;"><span style="color:#000000;">'''Jupyter Notebook '''</span><span style="color:#000000;">IDE</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Prerequisite'''
|| To follow this tutorial,

* <div style="margin-left:1.905cm;margin-right:0cm;"><span style="color:#000000;">The learner must have basic knowledge of </span><span style="color:#000000;">'''Python.'''</span></div>
* <div style="margin-left:1.905cm;margin-right:0cm;"><span style="color:#000000;">For pre-requisite </span><span style="color:#000000;">'''Python'''</span><span style="color:#000000;"> tutorials, please visit this website.</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Code files'''
||
* <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>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Logistic Regression'''
||
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">Logistic regression is a machine learning algorithm used for classification tasks.</div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">The goal is to predict a binary outcome (like yes/no, true/false) based on input features.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Logistic Regression'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">Logistic regression uses the </span><span style="color:#000000;">'''logit'''</span><span style="color:#000000;"> function.</span></div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">This function maps the linear combination of features to probabilities zero and one.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show Slide:'''Types of classification'''
|| There are two types of classification. They are
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Binary Classification'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Multiclass Classification'''</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Binary classification'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">Binary classification is used for modeling a binary target variable.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">The target variable has only two possible outcomes.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Multiclass classification'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">Multiclass classification is an extension of binary classification.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">The target variable can have two or more possible outcomes.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Open the file ads.csv and point to the fields as per narration.
|| To implement the '''Binary classification model, '''we use the '''Ads dot csv '''dataset.

Here, we analyze customers to predict if they will make a purchase in the store.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Point to the '''LR_Binary.ipynb'''
|| '''LR underscore Binary''' '''dot ipynb '''is the python notebook file for this demonstration.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| 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="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Type '''cd Downloads'''

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.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show Jupyter Notebook Home page:

Double Click on '''LR_Binary.ipynb '''file
|| We can see the '''Jupyter Notebook''' '''Home page''' has opened in the web browser.

Click the '''LR underscore Binary dot ipynb '''file to open it.

<div style="color:#000000;">Note that each cell will have the output displayed in this file.</div>
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''import pandas as pd '''

'''import matplotlib.pyplot as plt '''

'''import seaborn as sns '''
|| We have imported the necessary libraries for Binary classification.

Make sure to Press''' Shift '''and''' Enter''' to execute the code in each cell.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| '''Highlight ads = pd.read_csv(r" Ads.csv") '''
'''ads.head() '''
|| We load the dataset to a variable '''ads''' using the method '''pd dot read underscore csv'''.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''Data Exploration'''
|| Let us explore the dataset.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight

'''print(f"Shape of the dataset{ads.shape}") '''

'''print(ads.info())'''
|| First, we display the number of rows and columns of the dataset using '''ads dot shape.'''

Then we summarize the dataset, including rows, columns, and missing values using '''ads dot info'''.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''plt.figure(figsize=(6, 4))'''

'''sns.countplot(x='Purchased', data=ads, palette='viridis')'''
|| Next, we visualize the dataset by plotting the count of the '''Purchased''' attribute.

This attribute represents the target variable.

In the output cell, ignore the warning if you get any.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''Data Preprocessing'''
|| Let us preprocess the dataset.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''ads.drop(columns=['User ID'])'''

'''ads.head()'''
|| We delete the''' User ID''' column, as it is not required for the prediction.

Let us display the first few rows of the updated data and verify.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Only narration
|| In the dataset, the column '''Gender''' contains string data type.

The '''fit method''' in '''sklearn''' can't train models with string data.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''l = LabelEncoder()'''
|| So, we use the '''LabelEncoder method''' to convert the string data type into integer.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''ads['Gender'] = l.fit_transform(ads['Gender'])'''

|| Using the '''fit underscore transform '''we encode the '''Gender '''column.

Now,''' female '''is encoded as '''0 '''and '''male '''as '''1.'''

Have a look at the encoded data.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Only narration

Highlight''' x = ads.drop(columns=["Purchased"])'''

'''x.head()'''
|| Now, let us prepare the data for training.

First we remove the target column '''Purchased '''from the ads dataset.''' '''

Then''', '''we''' '''copy the remaining features into the variable '''x.'''

Notice that we have removed the '''Purchased''' column.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight

'''y = ads.Purchased'''

'''y.head()'''
|| Next, let us assign the '''Purchased''' column which is the target feature to '''y'''.

The variable '''y''' has only the class labels as shown.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Only narration

Highlight '''mms = MinMaxScaler()'''

|| Next, we perform '''Feature Scaling''' which is used to normalize the features.

First we create the instance of '''MinMaxScaler''' using the '''MinMaxScaler method.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''scaled_x = pd.DataFrame(mms.fit_transform(x),columns=x.columns)'''
|| '''mms dot fit underscore transform '''method is used for scaling each feature.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''scaled_x.head()'''
|| Now we see the scaled data for the feature '''x.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''Train and Test Split'''
|| Next, we split the data into training and testing sets.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''train_test_split(scaled_x,y,test_size=0.3,random_state=0)'''
|| '''scaled underscore x '''contains the preprocessed features, and y is the target variable.

The''' test underscore size''' equals 0.3 means 30% of the data is allocated for testing.

The remaining 70 percent is used for training.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
||
|| '''x underscore train and y underscore train''' are training features and labels.

Training data is used to train the model.

x underscore test and y underscore test are test features and labels.

Test data is used to evaluate the model performance.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''Model Instantiation of Binary Classification and Model training'''

Highlight '''lr = LogisticRegression()'''
|| Now let’s train the''' Binary Classification Model '''using '''Logistic Regression.'''

We create an instance of '''LogisticRegression''' from the '''sklearn''' library.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''lr.fit(x_train,y_train)'''
|| Now we train the model using the '''fit '''method on the '''training data'''.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''y_train_pred = lr.predict(x_train) '''
|| Next let us calculate the '''training accuracy.'''

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''Training Accuracy: 0.814'''
|| We see the '''training accuracy''' is 0.814 which is pretty good.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''y_train_pred_proba = lr.predict_proba(x_train)[:, 1]'''

|| The trained logistic regression model is used to predict the probabilities.

It predicts the target variable for the training data.

It will return the predicted probabilities for each class.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''roc_auc_train = roc_auc_score(y_train, y_train_pred_proba)'''

|| We calculate the '''ROC-AUC score''' for the model’s performance on the training data.

ROC is '''Receiver Operating Characteristic''', and AUC is '''Area Under the Curve.'''

It measures how well the model distinguishes between the two classes.

A higher score indicates better performance.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''logloss_train = log_loss(y_train, y_train_pred_proba)'''

|| We also calculate the '''cross entropy loss''' for the training data.

It measures how close the predicted probabilities are to the actual class labels.

A lower value indicates better model performance.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''ROC-AUC Score: 0.917'''

'''Cross Entropy Loss: 0.406'''
|| The '''ROC-AUC Score''' is 0.917.

This shows the model effectively distinguishes between the classes.

The '''cross-entropy loss''' is 0.406.

It shows how well the model’s predictions match the actual labels.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''Predictions for Test Data'''
|| Further, we predict labels for the '''x underscore test'''.

For prediction we use the class of '''test underscore data'''.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''test_data = x_test.iloc[10].values.reshape(1, -1)'''
|| Next, we predict the features of the 10th test data point in '''x underscore test'''.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''predicted_class = lr.predict(test_data)'''
|| Then we use the '''Binary classification model''' to predict the classes.

We predict the classes based on test underscore data.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''actual_class = y_test[10]'''
|| '''actual underscore class''' has the actual class of the test data point.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''print(f"Predicted class:'''
|| Finally, we print the predicted and actual class.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''Predicted class: 0, Actual class: 0'''

|| We get the output, '''predicted value''' as '''0''' and the '''actual value''' as '''0'''.

In the output cell, ignore the warning if you get any.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''y_pred = lr.predict(x_test)'''
|| '''y underscore pred''' predicts the target variables.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''print("Binary classification - Actual vs Predicted:")'''

|| We print actual and predicted class labels.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''test_accuracy = accuracy_score(y_test, y_pred)'''

|| Then we also calculate the '''test accuracy.'''

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight: '''Test Accuracy: 0.833'''
|| The '''test accuracy''' is approximately 0.833, which is also pretty good.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight

'''y_test_pred_proba = lr.predict_proba(x_test)[:, 1]'''

|| We calculate the '''ROC-AUC score''' and '''cross-entropy loss''' for the test data.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight '''ROC-AUC Score: 0.949'''

'''Cross Entropy Loss: 0.355'''
|| The '''ROC-AUC''' score is 0.949.

The model demonstrates excellent performance in distinguishing between classes.

A Cross Entropy Loss of '''0.355''' shows fairly accurate predictions.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Highlight

'''conf_matrix = confusion_matrix(y_test, y_pred)'''

|| We can also visualize the '''confusion matrix''' of the model’s performance.

It shows how well the model is correctly classifying the instances.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show output plot
|| The '''confusion matrix''' shows '''76 non-buyers''' are correctly predicted.

However, '''three non-buyers''' are incorrectly classified as '''buyers'''.

Similarly,''' 24 actual buyers''' are correctly identified.

But '''17''' were '''misclassified''' as '''non-buyers'''.

A '''higher numbe'''r in the '''diagonal''' indicated '''better accuracy'''.

This means the model performs well but struggles with some misclassifications.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Only narration
|| We conclude our implementation of '''Binary classification'''.

We have successfully predicted if a given user will make a purchase in the store.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Summary'''

Only Narration

|| In this tutorial, we have learnt about
* <div style="margin-left:1.27cm;margin-right:0cm;">Logistic Regression</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Binary Classification</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Multiclass Classification</div>

In the next tutorial, we’ll learn how to implement Multiclass classification for Logistic Regression.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Assignment '''

<span style="background-color:#ffffff;">As an assignment, please do the following:</span>
|| As an assignment,

<span style="background-color:#ffffff;">Replace the </span><span style="background-color:#ffffff;">'''y underscore pred '''</span><span style="background-color:#ffffff;">code with the code as shown here.</span>

Observe the changes in Training and Testing accuracy.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide image:

'''Assignment Solution'''

'''binary.png'''
|| After completing the assignment, the output should match the expected result.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| <div style="color:#252525;">Show Slide:</div>

<div style="color:#252525;">'''FOSSEE Forum'''</div>
|| <span style="background-color:#ffffff;color:#000000;">For any general or technical questions on </span><span style="color:#000000;">'''Python for'''</span>

<span style="color:#000000;">'''Machine Learning'''</span><span style="background-color:#ffffff;color:#000000;">, visit the</span><span style="background-color:#ffffff;color:#000000;">''' FOSSEE forum'''</span><span style="background-color:#ffffff;color:#000000;"> and post your question.</span>
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| <div style="color:#000000;">Show slide:</div>

<div style="color:#000000;">'''Thank You'''</div>
|| <span style="background-color:#ffffff;color:#000000;">This is </span><span style="background-color:#ffffff;color:#000000;">'''Anvita Thadavoose Manjummel'''</span><span style="background-color:#ffffff;color:#000000;">, a FOSSEE Summer Fellow 2025, IIT Bombay signing off.</span>

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

@@ Line 61: / Line 61: @@
 |-
 || Show Slide:'''Types of classification'''
-|| There are two types of classification. They are
+|| There are two types of classification.
 * '''Binary Classification'''
 * '''Multiclass Classification'''
@@ Line 88: / Line 90: @@
 |-
 || Point to the '''LR_Binary.ipynb'''
-|| '''LR underscore Binary''' '''dot ipynb '''is the python notebook file for this demonstration.
+|| '''LR underscore Binary dot ipynb ''' is the python notebook file for this demonstration.
 |-
 || 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.
+|| Let us open the '''Linux''' terminal by pressing '''Ctrl,Alt''' and '''T '''keys together.
 Activate the machine learning environment as shown.
@@ Line 111: / Line 113: @@
 Double Click on '''LR_Binary.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 '''LR underscore Binary dot ipynb '''file to open it.
@@ Line 184: / Line 186: @@
 || Now, let us prepare the data for training.
-First we remove the target column '''Purchased '''from the ads dataset'''.
+First we remove the target column '''Purchased ''' from the '''ads dataset'''.
-Then ,  we copy the remaining features into the variable '''x.'''
+Then,  we copy the remaining features into the variable '''x.'''
 Notice that we have removed the '''Purchased''' column.
@@ Line 368: / Line 370: @@
 '''Summary'''
 * Logistic Regression
 * Binary Classification
 * Multiclass Classification
-In the next tutorial, we’ll learn how to implement Multiclass classification for Logistic Regression.
+|| In this tutorial, we have learnt about
 |-
 || Show slide:

@@ Line 1: / Line 1: @@
 {| border="1"
+|-
 || '''Visual Cue'''
 || '''Narration'''
@@ Line 171: / Line 171: @@
 |-
 || Highlight '''ads['Gender'] = l.fit_transform(ads['Gender'])'''
 || Using the '''fit underscore transform '''we encode the '''Gender '''column.
-Now,''' female '''is encoded as '''0 '''and '''male '''as '''1.'''
+Now,''' female '''is encoded as '''0 ''' and '''male '''as '''1.'''
 Have a look at the encoded data.
@@ Line 185: / Line 184: @@
 || Now, let us prepare the data for training.
-First we remove the target column '''Purchased '''from the ads dataset.''' '''
+First we remove the target column '''Purchased '''from the ads dataset'''.
-Then''', '''we''' '''copy the remaining features into the variable '''x.'''
+Then ,  we copy the remaining features into the variable '''x.'''
 Notice that we have removed the '''Purchased''' column.
@@ Line 209: / Line 208: @@
 |-
 || Highlight '''scaled_x = pd.DataFrame(mms.fit_transform(x),columns=x.columns)'''
-|| '''mms dot fit underscore transform '''method is used for scaling each feature.
+|| '''mms dot fit underscore transform ''' method is used for scaling each feature.
 |-
 || Highlight '''scaled_x.head()'''
-|| Now we see the scaled data for the feature '''x.'''
+|| Now we see the scaled data for the feature '''x'''.
 |-
 || Highlight '''Train and Test Split'''
@@ Line 218: / Line 217: @@
 |-
 || Highlight '''train_test_split(scaled_x,y,test_size=0.3,random_state=0)'''
-|| '''scaled underscore x '''contains the preprocessed features, and y is the target variable.
+|| '''scaled underscore x '''contains the preprocessed features, and '''y''' is the target variable.
-The''' test underscore size''' equals 0.3 means 30% of the data is allocated for testing.
+The ''' test underscore size''' equals 0.3 means 30% of the data is allocated for testing.
 The remaining 70 percent is used for training.
@@ Line 236: / Line 235: @@
 Highlight '''lr = LogisticRegression()'''
-|| Now let’s train the''' Binary Classification Model '''using '''Logistic Regression.'''
+|| Now let’s train the ''' Binary Classification Model ''' using '''Logistic Regression'''.
 We create an instance of '''LogisticRegression''' from the '''sklearn''' library.
@@ Line 259: / Line 258: @@
 |-
 || Highlight '''roc_auc_train = roc_auc_score(y_train, y_train_pred_proba)'''
 || We calculate the '''ROC-AUC score''' for the model’s performance on the training data.
@@ Line 356: / Line 354: @@
 But '''17''' were '''misclassified''' as '''non-buyers'''.
-A '''higher numbe'''r in the '''diagonal''' indicated '''better accuracy'''.
+A '''higher number'''  in the '''diagonal''' indicated '''better accuracy'''.
 This means the model performs well but struggles with some misclassifications.
@@ Line 383: / Line 381: @@
 '''Assignment '''
- As an assignment, please do the following:
+As an assignment, please do the following:
 || As an assignment,
- Replace the  '''y underscore pred ''' code with the code as shown here.
+Replace the  '''y underscore pred ''' code with the code as shown here.
 Observe the changes in Training and Testing accuracy.
@@ Line 399: / Line 397: @@
 ||  Show Slide:
- '''FOSSEE Forum'''
+'''FOSSEE Forum'''
 ||  For any general or technical questions on '''Python for'''

Python-for-Machine-Learning/C2/Logistic-Regression-Binary-Classification/English - Revision history

Madhurig at 13:52, 17 July 2025

Madhurig at 07:28, 14 July 2025

Madhurig at 07:24, 14 July 2025

Nirmala Venkat: Created page with " {| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show sli..."

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show sli..."