!pip install nbconvert
%shell jupyter nbconvert --to html /content/LogisticRegression_prac_01.ipynb

Titan - Train dataset

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

df = pd.read_csv('/content/titanic_train.csv')

df.head(2)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

Exploratory Analysis on basic import of dataset

df.isnull()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	False	False	False	False	False	False	False	False	False	False	True	False
1	False	False	False	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False	False	True	False
3	False	False	False	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False	False	True	False
...	...	...	...	...	...	...	...	...	...	...	...	...
886	False	False	False	False	False	False	False	False	False	False	True	False
887	False	False	False	False	False	False	False	False	False	False	False	False
888	False	False	False	False	False	True	False	False	False	False	True	False
889	False	False	False	False	False	False	False	False	False	False	False	False
890	False	False	False	False	False	False	False	False	False	False	True	False

891 rows × 12 columns

sns.heatmap(df.isnull(), yticklabels= False, cbar = False, cmap = "Blues")

<matplotlib.axes._subplots.AxesSubplot at 0x7f9335ad1c10>

sns.countplot(x='Survived', data = df)

<matplotlib.axes._subplots.AxesSubplot at 0x7f933c3f0750>

sns.countplot(x = 'Survived', hue = "Sex", data = df)

<matplotlib.axes._subplots.AxesSubplot at 0x7f933c2d55d0>

sns.countplot(x='Survived', hue = "Pclass", data = df)

<matplotlib.axes._subplots.AxesSubplot at 0x7f933c032c90>

df.drop('PassengerId', inplace = True, axis = 1)

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x7f933998e890>

df.head(2)

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

sns.boxplot(x = 'Pclass', y = 'Age', data = df)

<matplotlib.axes._subplots.AxesSubplot at 0x7f933620eb50>

def impute_age(cols):
    Age = cols[0]
    Pclass = cols[1]

    if pd.isnull(Age) == True:
        if Pclass == 1:
            return 37
        elif Pclass == 2:
            return 32
        else:
            return 24
    else:
        return Age

df['Age'] = df[['Age', 'Pclass']].apply(impute_age, axis = 1)

sns.heatmap(df.isnull(), cmap = 'Blues')

<matplotlib.axes._subplots.AxesSubplot at 0x7f9335d6c950>

df.head(2)

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C

df.drop(['Name', 'Ticket'], inplace = True, axis = 1)

df.head(2)

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Cabin	Embarked
0	0	3	male	22.0	1	0	7.2500	NaN	S
1	1	1	female	38.0	1	0	71.2833	C85	C

sex_df = pd.get_dummies(df['Sex'], drop_first=True)

Embark_df = pd.get_dummies(df['Embarked'], drop_first = True)

sex_df

	male
0	1
1	0
2	0
3	0
4	1
...	...
886	1
887	0
888	0
889	1
890	1

891 rows × 1 columns

Embark_df

	Q	S
0	0	1
1	0	0
2	0	1
3	0	1
4	0	1
...	...	...
886	0	1
887	0	1
888	0	1
889	0	0
890	1	0

891 rows × 2 columns

df = pd.concat([df, sex_df, Embark_df], axis = 1)

df.drop('Cabin', inplace = True, axis = 1)

logreg = LogisticRegression()

df

	Survived	Pclass	Sex	Age	SibSp	Parch	Fare	Embarked	male	Q	S
0	0	3	male	22.0	1	0	7.2500	S	1	0	1
1	1	1	female	38.0	1	0	71.2833	C	0	0	0
2	1	3	female	26.0	0	0	7.9250	S	0	0	1
3	1	1	female	35.0	1	0	53.1000	S	0	0	1
4	0	3	male	35.0	0	0	8.0500	S	1	0	1
...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	1	0	1
887	1	1	female	19.0	0	0	30.0000	S	0	0	1
888	0	3	female	24.0	1	2	23.4500	S	0	0	1
889	1	1	male	26.0	0	0	30.0000	C	1	0	0
890	0	3	male	32.0	0	0	7.7500	Q	1	1	0

891 rows × 11 columns

df.drop('Sex', inplace = True, axis = 1)

df.drop('Embarked', inplace = True, axis = 1)

df

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S
0	0	3	22.0	1	0	7.2500	1	0	1
1	1	1	38.0	1	0	71.2833	0	0	0
2	1	3	26.0	0	0	7.9250	0	0	1
3	1	1	35.0	1	0	53.1000	0	0	1
4	0	3	35.0	0	0	8.0500	1	0	1
...	...	...	...	...	...	...	...	...	...
886	0	2	27.0	0	0	13.0000	1	0	1
887	1	1	19.0	0	0	30.0000	0	0	1
888	0	3	24.0	1	2	23.4500	0	0	1
889	1	1	26.0	0	0	30.0000	1	0	0
890	0	3	32.0	0	0	7.7500	1	1	0

891 rows × 9 columns

from sklearn.model_selection import train_test_split

X = df.drop('Survived', axis = 1)
y = df['Survived']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)

logreg.fit(X_train, y_train)

/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,

LogisticRegression()

predictions = logreg.predict(X_test)

predictions

array([0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,
       1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,
       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,
       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0,
       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1,
       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
       1, 0, 0, 1])

df

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S
0	0	3	22.0	1	0	7.2500	1	0	1
1	1	1	38.0	1	0	71.2833	0	0	0
2	1	3	26.0	0	0	7.9250	0	0	1
3	1	1	35.0	1	0	53.1000	0	0	1
4	0	3	35.0	0	0	8.0500	1	0	1
...	...	...	...	...	...	...	...	...	...
886	0	2	27.0	0	0	13.0000	1	0	1
887	1	1	19.0	0	0	30.0000	0	0	1
888	0	3	24.0	1	2	23.4500	0	0	1
889	1	1	26.0	0	0	30.0000	1	0	0
890	0	3	32.0	0	0	7.7500	1	1	0

891 rows × 9 columns

print(classification_report(y_test, predictions))

              precision    recall  f1-score   support

           0       0.78      0.87      0.82       154
           1       0.79      0.67      0.72       114

    accuracy                           0.78       268
   macro avg       0.79      0.77      0.77       268
weighted avg       0.78      0.78      0.78       268

confusion_matrix(y_test, predictions)

array([[134,  20],
       [ 38,  76]])

sns.heatmap(df, cmap = 'Blues')

<matplotlib.axes._subplots.AxesSubplot at 0x7f9335e6c3d0>

df

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S
0	0	3	22.0	1	0	7.2500	1	0	1
1	1	1	38.0	1	0	71.2833	0	0	0
2	1	3	26.0	0	0	7.9250	0	0	1
3	1	1	35.0	1	0	53.1000	0	0	1
4	0	3	35.0	0	0	8.0500	1	0	1
...	...	...	...	...	...	...	...	...	...
886	0	2	27.0	0	0	13.0000	1	0	1
887	1	1	19.0	0	0	30.0000	0	0	1
888	0	3	24.0	1	2	23.4500	0	0	1
889	1	1	26.0	0	0	30.0000	1	0	0
890	0	3	32.0	0	0	7.7500	1	1	0

891 rows × 9 columns

df.head(2)

	Survived	Pclass	Age	SibSp	Parch	Fare	male	Q	S
0	0	3	22.0	1	0	7.2500	1	0	1
1	1	1	38.0	1	0	71.2833	0	0	0

Task

Convert the current notebook to an HTML file. The notebook will first be saved to /content/LogisticRegression_prac_01.ipynb and then converted to HTML. A file existence check will be performed to ensure the notebook is saved before the conversion.

Fix Javascript filename typo and add file existence check

Subtask:

Ensure the notebook is saved to the specified path by implementing a file existence check with a polling mechanism after triggering the Javascript save function. No filename typo fix is needed as the filename is consistent.

Extract Notebook Content and Save

Subtask:

Extract the current notebook's content using Javascript, convert it to a JSON string, and save it to the specified file path.

Reasoning: The subtask requires extracting the notebook content and saving it to a file. This code block imports the json module, defines the notebook path, retrieves the current notebook content as a Python dictionary using Javascript, converts it to a JSON string, and then writes the JSON string to the specified file path.

import json
from google.colab import output
import time

notebook_path = '/content/LogisticRegression_prac_01.ipynb'

JS_CODE = '''
    (function() {
        // Try window.colab.global.notebook.toJSON() first, then fallback to window.colab.shell.toJSON()
        if (typeof window.colab !== 'undefined' && typeof window.colab.global !== 'undefined' && typeof window.colab.global.notebook !== 'undefined') {
            return JSON.stringify(window.colab.global.notebook.toJSON());
        } else if (typeof window.colab !== 'undefined' && typeof window.colab.shell !== 'undefined') {
            return JSON.stringify(window.colab.shell.toJSON());
        } else {
            return '{}'; // Return an empty JSON object if colab components are not available
        }
    })();
'''

max_retries = 5
retry_delay_seconds = 2
notebook_content = '{}'

for i in range(max_retries):
    print(f"Attempt {i + 1}/{max_retries}: Retrieving notebook content...")
    content = output.eval_js(JS_CODE)
    # Check if the content is not just an empty JSON string
    if content and content != '{}' and 'cells' in json.loads(content):
        notebook_content = content
        print("Notebook content successfully retrieved.")
        break
    else:
        print(f"Failed to retrieve complete notebook content. Retrying in {retry_delay_seconds} seconds...")
        time.sleep(retry_delay_seconds)

if notebook_content == '{}' or 'cells' not in json.loads(notebook_content):
    print("Error: Could not retrieve valid notebook content after multiple attempts.")
else:
    # Write the JSON string to the specified notebook path
    with open(notebook_path, 'w') as f:
        f.write(notebook_content)

    print(f"Notebook content saved to {notebook_path}")

Attempt 1/5: Retrieving notebook content...
Failed to retrieve complete notebook content. Retrying in 2 seconds...
Attempt 2/5: Retrieving notebook content...
Failed to retrieve complete notebook content. Retrying in 2 seconds...
Attempt 3/5: Retrieving notebook content...
Failed to retrieve complete notebook content. Retrying in 2 seconds...
Attempt 4/5: Retrieving notebook content...
Failed to retrieve complete notebook content. Retrying in 2 seconds...
Attempt 5/5: Retrieving notebook content...
Failed to retrieve complete notebook content. Retrying in 2 seconds...
Error: Could not retrieve valid notebook content after multiple attempts.

Task

Convert the current notebook to an HTML file. The notebook will first be saved to /content/LogisticRegression_prac_01.ipynb and then converted to HTML. A file existence check will be performed to ensure the notebook is saved before the conversion.

Safely Retrieve Notebook Content

Subtask:

Execute JavaScript to safely retrieve the current notebook's content as a JSON string, including checks for the existence of window.colab and window.colab.shell to prevent errors.

Verify Notebook File Creation

Subtask:

Perform a file existence check to confirm that /content/LogisticRegression_prac_01.ipynb has been successfully created before proceeding with the HTML conversion.

Reasoning: To perform the file existence check with a timeout, I need to import the os and time modules, define the notebook path and timeout, and then implement a polling loop. This code block will encapsulate all these requirements.

import os
import time

notebook_path = '/content/LogisticRegression_prac_01.ipynb'
timeout_seconds = 60 # Set an appropriate timeout, e.g., 60 seconds
start_time = time.time()
file_saved = False

while not file_saved and (time.time() - start_time < timeout_seconds):
    if os.path.exists(notebook_path):
        file_saved = True
        print(f"Notebook found at {notebook_path}")
    else:
        print(f"Notebook not yet found at {notebook_path}. Waiting...")
        time.sleep(1) # Wait for 1 second before checking again

if not file_saved:
    print(f"Error: Notebook not found at {notebook_path} within {timeout_seconds} seconds.")

Notebook found at /content/LogisticRegression_prac_01.ipynb

Convert Notebook to HTML

Subtask:

Use the %shell jupyter nbconvert --to html command to convert the saved notebook (/content/LogisticRegression_prac_01.ipynb) into an HTML file.

Reasoning: The subtask requires converting the notebook to an HTML file using the jupyter nbconvert shell command. This code block will execute that command.

!pip install nbconvert
%shell jupyter nbconvert --to html /content/LogisticRegression_prac_01.ipynb

Requirement already satisfied: nbconvert in /usr/local/lib/python3.12/dist-packages (7.17.0)
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[NbConvertApp] Converting notebook /content/LogisticRegression_prac_01.ipynb to html
Traceback (most recent call last):
  File "/usr/local/bin/jupyter-nbconvert", line 10, in <module>
    sys.exit(main())
             ^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/jupyter_core/application.py", line 284, in launch_instance
    super().launch_instance(argv=argv, **kwargs)
  File "/usr/local/lib/python3.12/dist-packages/traitlets/config/application.py", line 992, in launch_instance
    app.start()
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/nbconvertapp.py", line 420, in start
    self.convert_notebooks()
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/nbconvertapp.py", line 597, in convert_notebooks
    self.convert_single_notebook(notebook_filename)
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/nbconvertapp.py", line 563, in convert_single_notebook
    output, resources = self.export_single_notebook(
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/nbconvertapp.py", line 487, in export_single_notebook
    output, resources = self.exporter.from_filename(
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/exporters/templateexporter.py", line 390, in from_filename
    return super().from_filename(filename, resources, **kw)  # type:ignore[return-value]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/exporters/exporter.py", line 201, in from_filename
    return self.from_file(f, resources=resources, **kw)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/exporters/templateexporter.py", line 396, in from_file
    return super().from_file(file_stream, resources, **kw)  # type:ignore[return-value]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbconvert/exporters/exporter.py", line 221, in from_file
    nbformat.read(file_stream, as_version=4), resources=resources, **kw
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbformat/__init__.py", line 174, in read
    return reads(buf, as_version, capture_validation_error, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbformat/__init__.py", line 94, in reads
    nb = convert(nb, as_version)
         ^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/nbformat/converter.py", line 63, in convert
    raise ValidationError(msg) from None
jsonschema.exceptions.ValidationError: Notebook could not be converted from version 1 to version 2 because it's missing a key: cells

---------------------------------------------------------------------------
CalledProcessError                        Traceback (most recent call last)
/tmp/ipykernel_2810/232916469.py in <cell line: 0>()
      1 get_ipython().system('pip install nbconvert')
----> 2 get_ipython().run_line_magic('shell', 'jupyter nbconvert --to html /content/LogisticRegression_prac_01.ipynb')

/usr/local/lib/python3.12/dist-packages/IPython/core/interactiveshell.py in run_line_magic(self, magic_name, line, _stack_depth)
   2416                 kwargs['local_ns'] = self.get_local_scope(stack_depth)
   2417             with self.builtin_trap:
-> 2418                 result = fn(*args, **kwargs)
   2419             return result
   2420 

/usr/local/lib/python3.12/dist-packages/google/colab/_system_commands.py in _shell_line_magic(line)
     65   """
     66   result = _run_command(line, clear_streamed_output=False)
---> 67   result.check_returncode()
     68   return result
     69 

/usr/local/lib/python3.12/dist-packages/google/colab/_system_commands.py in check_returncode(self)
    135   def check_returncode(self):
    136     if self.returncode:
--> 137       raise subprocess.CalledProcessError(
    138           returncode=self.returncode, cmd=self.args, output=self.output
    139       )

CalledProcessError: Command 'jupyter nbconvert --to html /content/LogisticRegression_prac_01.ipynb' returned non-zero exit status 1.