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Label encoding

Categorical data is converted to numeric values with LabelEncoder.

LabelEncoder

Models work with numbers; text labels must be encoded as integers. LabelEncoder gives each distinct value 0, 1, 2... in alphabetical order:

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(["kedi", "köpek", "kedi", "kuş"])
print(y)                 # [0 1 0 2]
print(le.classes_)       # ['kedi' 'köpek' 'kuş']
print(le.inverse_transform([2]))  # ['kuş']

LabelEncoder is meant for labels (y).

One-hot encoding

Unordered categorical features are expanded into a separate 0/1 column for each value:

from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder()
X = enc.fit_transform([["kırmızı"], ["mavi"], ["kırmızı"]]).toarray()
print(X)   # [[1. 0.] [0. 1.] [1. 0.]]
print(enc.categories_)

In pandas, pd.get_dummies(df, columns=["renk"]) does the same. For ordered categories (small < medium < large) OrdinalEncoder is suitable.

Practice Encode the labels

Encode the renk list with LabelEncoder and print the result. Then print the length of classes_.

Starter code

model.py
from sklearn.preprocessing import LabelEncoder

renk = ["yeşil", "mavi", "yeşil", "sarı", "mavi"]

# buraya yaz

Sample solution

model.py
from sklearn.preprocessing import LabelEncoder

renk = ["yeşil", "mavi", "yeşil", "sarı", "mavi"]

le = LabelEncoder()
print(le.fit_transform(renk))
print(len(le.classes_))

Expected output

[2 0 2 1 0]
3

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 Why can applying LabelEncoder to a feature column (X) be a problem?

  • It isn't a problem
  • The numbers imply a false order (0 &lt; 1 &lt; 2) Correct answer
  • It is very slow
  • It doesn't accept text

Explanation: Linear models treat "kuş" (2) as greater than "kedi" (0); one-hot is used for unordered categories.

2 Turn the encoded numbers back into text.

Question 
adlar = le.____([0, 2])
Answer 
adlar = le.inverse_transform([0, 2])

Explanation: inverse_transform turns the model's predictions into readable labels.

3 Match the encoders.

Term Meaning
LabelEncoder Labels to integers
OneHotEncoder A 0/1 column per value
OrdinalEncoder Ordered categories
get_dummies One-hot with pandas

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4