To implement Logistic Regression in Python, the `LogisticRegression` class is used:

For now, you can stick with the default parameters. Creating and fitting the model can be done in a single line:

```python
logistic_regression = LogisticRegression().fit(X_train, y_train)
```

The dataset for this chapter comes from a **Portuguese banking institution** and contains information from marketing campaigns conducted via phone calls. The goal is to predict whether a client will **subscribe to a term deposit**, based on their personal, financial, and contact-related details, as well as outcomes of previous marketing interactions.
  
The data is already preprocessed and ready to be fed to the model.

import unittest
import importlib
import numpy as np


def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)


def get_first_differing_index(expected_array, actual_array):
    for i, (val_1, val_2) in enumerate(zip(expected_array, actual_array)):
        if not np.array_equal(val_1, val_2):
            return i, expected_array[i], actual_array[i]

    if len(expected_array) > len(actual_array):
        return len(actual_array), expected_array[len(actual_array)], None
    else:
        return len(expected_array), None, actual_array[len(expected_array)]


class TestUserCode(unittest.TestCase):

    def test_X_train_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'X_train'),
            "The `X_train` variable is declared.",
            "Expected `X_train` to be declared."
        )

    def test_X_test_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'X_test'),
            "The `X_test` variable is declared.",
            "Expected `X_test` to be declared."
        )

    def test_y_train_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'y_train'),
            "The `y_train` variable is declared.",
            "Expected `y_train` to be declared."
        )

    def test_y_test_is_declared(self):
        import user_code
        _dynamic_test(
            self,
            hasattr(user_code, 'y_test'),
            "The `y_test` variable is declared.",
            "Expected `y_test` to be declared."
        )

    def test_X_train_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        expected_value, _, _, _ = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'X_train'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            if actual_value.shape != expected_value.shape:
                failure_message = f"The shape of `{variable}` is incorrect."
            else:
                for i in range(actual_value.shape[0]):
                    if np.array_equal(actual_value[i], expected_value[i]):
                        continue
                    idx, expected_element, actual_element = get_first_differing_index(expected_value[i], actual_value[i])
                    failure_message = f"Expected `{variable}` to contain `{expected_element}` at index [{i}, {idx}], but got `{actual_element}`."
                    break

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_X_test_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        _, expected_value, _, _ = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'X_test'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            if actual_value.shape != expected_value.shape:
                failure_message = f"The shape of `{variable}` is incorrect."
            else:
                for i in range(actual_value.shape[0]):
                    if np.array_equal(actual_value[i], expected_value[i]):
                        continue
                    idx, expected_element, actual_element = get_first_differing_index(expected_value[i],
                                                                                      actual_value[i])
                    failure_message = f"Expected `{variable}` to contain `{expected_element}` at index [{i}, {idx}], but got `{actual_element}`."
                    break

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_y_train_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        _, _, expected_value, _ = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'y_train'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            idx, expected_element, actual_element = get_first_differing_index(expected_value, actual_value)
            failure_message = f"Expected `{variable}` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_y_test_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1).values
        y = df['deposit'].values
        _, _, _, expected_value = train_test_split(X, y, test_size=0.2, random_state=42)

        variable = 'y_test'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif not isinstance(actual_value, np.ndarray):
            failure_message = f"`{variable}` is not a `numpy.ndarray`."
        elif actual_value.size == 0:
            failure_message = f"`{variable}` is empty."
        elif np.array_equal(actual_value, expected_value):
            condition = True
            failure_message = None
        else:
            idx, expected_element, actual_element = get_first_differing_index(expected_value, actual_value)
            failure_message = f"Expected `{variable}` to contain `{expected_element}` at index {idx}, but got `{actual_element}`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` contains the correct data.",
            failure_message
        )

    def test_lr_is_declared(self):
        import user_code

        variable = 'lr'
        _dynamic_test(
            self,
            hasattr(user_code, variable),
            f"The `{variable}` variable is declared.",
            f"Expected `{variable}` to be declared."
        )

    def test_lr_is_correct(self):
        import user_code
        import pandas as pd
        from sklearn.linear_model import LogisticRegression
        from sklearn.model_selection import train_test_split

        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/marketing_bank.csv')
        X = df.drop('deposit', axis=1)
        y = df['deposit']
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

        expected_value = LogisticRegression().fit(X_train, y_train)

        variable = 'lr'
        actual_value = getattr(user_code, variable, None)
        condition = False
        if actual_value is None:
            failure_message = f"The `{variable}` variable is not declared."
        elif isinstance(actual_value, LogisticRegression):
            if np.array_equal(actual_value.coef_, expected_value.coef_):
                condition = True
                failure_message = None
            else:
                idx, expected_element, actual_element = get_first_differing_index(expected_value.coef_[0], actual_value.coef_[0])
                failure_message = f"Expected the coefficients of the model to contain `{expected_element}` at index {idx}, but got `{actual_element}`."
        else:
            failure_message = f"`{variable}` is not a `LogisticRegression`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` is a correctly fitted `LogisticRegression`.",
            failure_message
        )

    def test_test_accuracy_declared(self):
        import user_code

        variable = 'test_accuracy'
        _dynamic_test(
            self,
            hasattr(user_code, variable),
            f"The `{variable}` variable is declared.",
            f"Expected `{variable}` to be declared."
        )

    def test_test_accuracy_is_correct(self):
        import user_code
        expected_value = 0.8083296014330497

        variable = 'test_accuracy'
        actual_value = getattr(user_code, variable, None)
        if actual_value is None:
            condition = False
            failure_message = f"The `{variable}` variable is not declared."
        elif isinstance(actual_value, float):
            condition = actual_value == expected_value
            failure_message = f"Expected `{variable}` to be `{expected_value}`, but got `{actual_value}`."
        else:
            condition = False
            failure_message = f"`{variable}` is not a `float`."

        _dynamic_test(
            self,
            condition,
            f"`{variable}` is correct.",
            failure_message
        )


if __name__ == '__main__':
    unittest.main()

test_main.py

Master the core classification algorithms that power modern machine learning. Explore how models like k-NN, logistic regression, decision trees, and random forests make predictions, evaluate their accuracy, and understand when to use each. Build the skills to compare models and choose the best one for your data.

Discover how the k-nearest neighbors algorithm makes predictions based on similarity. Learn to handle multiple features, tune parameters, and apply cross-validation to improve accuracy.

Understand how logistic regression models probabilities and classifies outcomes. Practice implementing it, interpreting decision boundaries, and applying regularization to prevent overfitting.

Learn how decision trees split data into meaningful groups based on feature values. Explore how parameters like tree depth and minimum samples per leaf affect model performance and generalization.

Explore how random forests combine multiple decision trees to improve accuracy and robustness. Understand the role of randomness and apply this ensemble method to real-world data.

Evaluate models using metrics such as accuracy, precision, recall, and F1-score. Learn to interpret confusion matrices and compare multiple classifiers to identify the best-performing model.

Challenge: Implementing Logistic Regression

Solution

Awesome!

Challenge: Implementing Logistic Regression

Solution