Feature Engineering is the process of selecting, creating, and transforming input variables (features) to improve the performance of a Machine Learning model.
Good features help the model understand data better and make more accurate predictions.
Improves model accuracy
Reduces overfitting
Makes patterns clearer for algorithms
Enhances training speed and efficiency
Choosing the most relevant features and removing unnecessary ones.
Removes noise
Reduces complexity
Techniques:
Correlation analysis
Feature importance
Chi-square test
Creating new features from existing data.
Combining features
Extracting information
Example:
Creating Age from Date of Birth
Changing feature values into a suitable format.
Common methods:
Normalization
Standardization
Log transformation
Converting non-numeric data into numeric form.
Techniques:
Label Encoding
One-Hot Encoding
Dealing with incomplete data.
Remove missing records
Replace with mean, median, or mode
Ensures all features are on the same scale.
Important for distance-based algorithms
Methods:
Min-Max Scaling
Standard Scaling
In a house price dataset:
Original features: Size, Location
Engineered feature: Price per square foot
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