Variables

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What is a Variable?

A variable is a name that refers to a value stored in memory. Think of it as a box with a label that stores data. In machine learning, variables help us manage data, features, models, and results.

1. Declaring Variables

x = 5
name = "Mr Nerd"
price = 19.99
is_active = True

Practical Notes:

• x is an integer

• name is a string

• price is a float

• is_active is a boolean

No need to declare the type in Python — it figures it out!

2. Variable Naming Rules (Very Important in ML Projects)

# Valid
data = [1, 2, 3]
learning_rate = 0.01
model_name = "LinearRegression"

# Invalid
# 1model = "wrong"   ❌ Starts with a number
# learning-rate = 0.01  ❌ Hyphen not allowed

Best Practices (PEP8 Style Guide for ML Code):

• Use lowercase with underscores: train_data, test_accuracy

• Be descriptive: input_vector instead of iv

• Avoid keywords: class, def, etc.

3. Updating Variables

accuracy = 0.85
accuracy = accuracy + 0.05  # Now it's 0.90

Shortcuts:

accuracy += 0.05  # same as above

4. Data Types in Machine Learning

# Common data types
integer_example = 10           # int
float_example = 3.14           # float
string_example = "NeuralNet"   # str
boolean_example = False        # bool
list_example = [1, 2, 3]       # list
tuple_example = (1, 2)         # tuple
dict_example = {"lr": 0.01}    # dict

5. Practical Use in ML:

features = [5.1, 3.5, 1.4, 0.2]   # input features (list of floats)
label = "Iris-setosa"            # target label (string)
params = {"learning_rate": 0.01, "epochs": 100}  # dictionary for config

6. Multiple Assignments

x, y, z = 1, 2, 3

# Use this for splitting ML data
train_data, test_data = [1, 2], [3, 4]

7. Constants (Not Built-in, But You Can Use UPPERCASE)

LEARNING_RATE = 0.01
EPOCHS = 100

Tip: Use ALL_CAPS to indicate values that should not change.

8. Type Checking (Important in Debugging ML Code)

data = [1, 2, 3]
print(type(data))  # <class 'list'>

9. Variable Scope (Global vs Local)

score = 90  # global

def update_score():
    score = 100  # local to this function
    print("Inside function:", score)

update_score()
print("Outside function:", score)

Output:

Inside function: 100
Outside function: 90

Variables inside functions do not change the global ones unless specified.

10. Dynamic Typing

Python is dynamically typed — you can reassign variables to different types.

x = 10      # x is int
x = "hi"    # now x is str

Be careful in ML code! Accidentally changing data types can cause bugs.

11. Example in a Mini Machine Learning Context

# Step 1: Define hyperparameters
LEARNING_RATE = 0.01
EPOCHS = 100

# Step 2: Data
features = [5.1, 3.5, 1.4, 0.2]
label = "Iris-setosa"

# Step 3: Simulate a training log
accuracy = 0.75
accuracy += 0.05  # New accuracy
print(f"Final accuracy: {accuracy}")

12. Using Variables in NumPy (Essential for ML)

import numpy as np

x = np.array([1, 2, 3])
w = np.array([0.4, 0.3, 0.2])
bias = 0.5

# Linear combination
y = np.dot(x, w) + bias
print("Output:", y)

Output:

Output: 2.1

Summary Table

Feature	Example	Notes
Basic variable	x = 5	Auto-detects type
Multiple assign	x, y = 1, 2	Split values
Type check	type(x)	Debugging help
Update value	x += 1	Shortcut for updating
Store parameters	params = {"lr": 0.01}	Common in ML projects
Constants	EPOCHS = 100	Use ALL_CAPS

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