How to Add Comments in Python Programs for Machine Learning

# This is an example of a comment
x = 5  # Another comment explaining the value of x

"""
This is an example of a multi-line comment.
It can be used to explain complex logic or provide context.
You can include multiple lines here, and it will be treated as one block.
"""

# Here's how you would use the function explained above
def add_numbers(x, y):
    result = x + y  # This is where the magic happens!
    return result

numbers_to_add = 5  # You get the idea...
result = add_numbers(numbers_to_add, 10)
print(result)  # Outputs: 15

# This is an example of how to add a comment explaining what this block does
x = 5  # Another comment here
y = 10  # And another one here
result = x + y  # The final result!
print(result)  # Outputs: 15

"""
This is an example of how to use a multi-line comment.
You can explain what this function does, and even include some examples!
"""

def add_numbers(x, y):
    result = x + y  # This is where the magic happens!
    return result

numbers_to_add = 5
result = add_numbers(numbers_to_add, 10)
print(result)  # Outputs: 15

# Don't do this!
x = 5  # The value of x is being set here.
y = 10  # This is where we're setting y.
result = x + y  # And finally, we're calculating the result.
print(result)  # Outputs: 15

# Instead, do this:
x = 5
y = 10
result = x + y
print(result)  # Outputs: 15

"""
This is an example of how not to ignore comments!
You might think it's unnecessary, but trust us, it's crucial.
"""

x = 5  # This is where we're setting x.
y = 10  # And this is where y gets set.
result = x + y  # Finally, the result!

print(result)  # Outputs: 15

"""
This is an example of how to use comments to explain an algorithm.
You can break down complex logic into smaller, more manageable steps!
"""

def find_prime_numbers(numbers):
    prime_numbers = []
    for number in numbers:
        if number > 1:  # A prime number is greater than 1
            is_prime = True
            for i in range(2, int(number ** 0.5) + 1):  # Check divisibility from 2 to sqrt(n)
                if number % i == 0:
                    is_prime = False
                    break
            if is_prime:
                prime_numbers.append(number)
    return prime_numbers

numbers = [12, 15, 20, 25]
prime_numbers = find_prime_numbers(numbers)
print(prime_numbers)  # Outputs: []

"""
This is an example of how to use comments for data preprocessing.
You can explain what each step does and why it's essential!
"""

import pandas as pd

# Load the dataset
data = pd.read_csv('data.csv')

# Check for missing values
print(data.isnull().sum())  # Output: NaN count for each column

# Handle missing values (mean or median imputation)
data['age'] = data['age'].fillna(data['age'].mean())
data['income'] = data['income'].fillna(data['income'].median())

# Print the cleaned dataset
print(data.head())  # Outputs: Cleaned dataset with handled missing values

"""
This is an example of how to use comments for model selection.
You can justify your choice and provide context!
"""

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data.drop('target', axis=1), data['target'], test_size=0.2, random_state=42)

# Choose a model (Logistic Regression or Random Forest)
model = LogisticRegression()
# model = RandomForestClassifier()

# Train the model
model.fit(X_train, y_train)

# Print the trained model
print(model)  # Outputs: Trained model with coefficients and features

"""
This is an example of how to use comments for hyperparameter tuning.
You can justify your choices and provide context!
"""

from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression

# Define the hyperparameter grid
param_grid = {
    'C': [0.1, 1, 10],
    'penalty': ['l1', 'l2']
}

# Perform hyperparameter tuning using Grid Search
grid_search = GridSearchCV(LogisticRegression(), param_grid, cv=5)
grid_search.fit(X_train, y_train)

# Print the best parameters and score
print(grid_search.best_params_)  # Outputs: Best parameters and score