Data Normalization
Standardization and scaling techniques.
Data Normalization is a free Learn AI with Python lesson on CoddyKit — lesson 3 of 5. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Learn AI with Python learning path, one of 5 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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Data Normalization
Data normalization ensures that all features in a dataset have similar scales. This is crucial for algorithms like gradient descent and distance-based models. In this lesson, we’ll explore standardization and scaling techniques.
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Why Normalize Data?
Normalization ensures that all features contribute equally to the model. Without normalization, features with larger scales can dominate the learning process.
Example:
- Age (0-100) and income (0-100,000) should be scaled to comparable ranges.
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Standardization
Standardization transforms data to have a mean of 0 and a standard deviation of 1. It is calculated as:
z = (x - mean) / std_dev
Standardization is ideal for algorithms like logistic regression and SVM.
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Min-Max Scaling
Min-max scaling scales data to a fixed range, typically [0, 1]. It is calculated as:
x_scaled = (x - min) / (max - min)
This technique is used when data needs to be bounded.
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Normalization in Python (Standardization)
Use StandardScaler from sklearn.preprocessing to standardize data.
Example:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)6
Normalization in Python (Min-Max Scaling)
Use MinMaxScaler from sklearn.preprocessing to apply min-max scaling.
Example:
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)7
Choosing a Normalization Technique
Consider the following when selecting a technique:
- Standardization: Use when data contains outliers.
- Min-Max Scaling: Use when features need to be bounded.
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Recap
In this lesson, you learned about:
- Standardization: Scaling data to a mean of 0 and standard deviation of 1.
- Min-Max Scaling: Transforming data to a fixed range.
- Using
StandardScalerandMinMaxScalerin Python.
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Congratulations!
You’ve completed the lesson on Data Normalization. Continue to the next lesson to learn about Data Merging in Python.
Frequently asked questions
Is the “Data Normalization” lesson free?
Yes — the full text of “Data Normalization” is free to read here on the web, and the Learn AI with Python course includes 5 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Learn AI with Python course, upgrade to CoddyKit PRO.
What will I learn in “Data Normalization”?
Standardization and scaling techniques. You practise Learn AI with Python with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Learn AI with Python?
No prior experience is required. Learn AI with Python on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 5, so you can start here or from the beginning and move at your own pace.
How long does the “Data Normalization” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Learn AI with Python lesson?
Yes. Every Learn AI with Python lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.