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Data Science Internship Prep
·
Module 5
Feature Engineering and Preprocessing Pipelines
1
Encoding Categorical Variables
Encode categorical variables using one-hot, label, and target encoding.
2
Feature Scaling with StandardScaler and MinMaxScaler
Scale numerical features using StandardScaler and MinMaxScaler.
3
Feature Engineering from Existing Data
Construct new features from existing data (e.g., from datetime, text, or domain knowledge).
4
Polynomial Features and Feature Interactions
Create polynomial features and feature interactions to capture non-linear relationships.
5
Handling Imbalanced Datasets
Apply techniques to handle imbalanced datasets, such as SMOTE and class weighting.
6
Feature Selection Techniques
Perform feature selection using methods like correlation analysis and recursive feature elimination.
7
Streamlining Machine Learning with scikit-learn Pipelines
Build reproducible preprocessing and modeling workflows using scikit-learn Pipelines.
Previous module
Machine Learning: Core Concepts and Workflow
Next module
Supervised Learning: Regression and Tree-Based Models