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CodeSignal

Feature Selection, Reduction and Streamlining

via CodeSignal

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Overview

Identify impactful features, reduce dimensionality, and streamline datasets for analysis. Learn techniques to enhance model efficiency and performance by focusing on the most relevant data attributes.

Syllabus

  • Unit 1: Data Preparation for Feature Selection
    • Fill Missing Values in Titanic
    • Drop Column With Excessive Missing Values
    • Encode Categorical Data Efficiently
    • Confirm Your Data Preparation Steps
  • Unit 2: Feature Selection with Statistical Tests
    • Defining Features and Target Variable
    • Adjusting Feature Selection Parameters
    • Explore Mutual Information for Feature Selection
    • Selecting Top Features with Chi Square
    • Evaluate Feature Significance with Chi-Square
  • Unit 3: Feature Ranking with Random Forests
    • Adjusting Random Forest Parameters
    • Debug Feature Ranking Code
    • Train and Rank Features
    • Feature Importance with Random Forests
  • Unit 4: Dimensionality Reduction with PCA
    • Explained Variance with PCA Analysis
    • Exploring PCA Without Scaling
    • Enhance Your PCA Skills
    • Creating a DataFrame with PCA
  • Unit 5: Automating Feature Engineering with Pipelines
    • Build a Pipeline in Python
    • Accessing PCA Explained Variance
    • Fix the Pipeline Missing Step
    • Enhance Pipelines with SelectKBest

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