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Summary - Feature engineering

In this group you learned feature selection, extraction, and transformation.

Summary: feature engineering

In this group:

  • Features are the only information the model sees; a good feature relates to the target and is known at prediction time.
  • Simple selection is done with a variance threshold and correlation.
  • Extraction from dates and text: month, day, word counts.
  • Categorical features: one-hot (unordered), ordinal (ordered), handle_unknown="ignore".
  • Numeric transformations: log1p, binning, polynomials.

Every transformation must be learned on training data and applied unchanged to test data; a pipeline is the safest way.

Practice Features from a date

Build a list containing the month and the weekend flag (1 if weekday() >= 5, else 0) for each date and print it as an np.array.

Starter code

model.py
import numpy as np
from datetime import date

tarihler = [date(2024, 1, 6), date(2024, 1, 8), date(2024, 7, 14)]

# buraya yaz

Sample solution

model.py
import numpy as np
from datetime import date

tarihler = [date(2024, 1, 6), date(2024, 1, 8), date(2024, 7, 14)]

ozet = [[t.month, int(t.weekday() >= 5)] for t in tarihler]
print(np.array(ozet))

Expected output

[[1 1]
 [1 0]
 [7 1]]

Questions and answers

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

1 Which carries a leakage risk for a credit model?

  • Income at application time
  • The applicant's age
  • The amount requested
  • The number of late payments computed after the loan closed Correct answer

Explanation: This information doesn't exist at prediction time; it is derived from the target itself.

2 Compress the skewed value with a logarithm.

Question 
x_log = np.____(gelir)
Answer 
x_log = np.log1p(gelir)

Explanation: log1p is defined at 0 too.

3 Match the tools.

Term Meaning
VarianceThreshold Drops constant columns
CountVectorizer Counts words
OneHotEncoder Category columns
PolynomialFeatures Squares and products

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