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CodeSignal

Data Preprocessing For Machine Learning

via CodeSignal

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Overview

This course covers essential data preprocessing techniques such as handling missing values, encoding categorical features, feature scaling, and splitting the dataset for training and testing.

Syllabus

  • Unit 1: Handling Missing Values
    • Impute Missing Values Using Median Strategy
    • Fixing Missing Data in House Prices Dataset
    • Handle Missing Values in House Prices Dataset
    • Handling Missing Values in House Prices Dataset
    • Handling Missing Values for House Prices and Features
  • Unit 2: Encoding Categorical Features
    • Encoding Car Brands and Colors
    • Encoding Car Brands and Colors
    • Changing OneHotEncoder to LabelEncoder
    • Encoding Car Brands with OneHotEncoder
    • Encode Car Brands and Colors
  • Unit 3: Feature Scaling
    • Scaling Recipe Ingredients
    • Standardize Ingredient Quantities
    • Feature Scaling for Recipe Measurements
    • Recipe Data Scaling
  • Unit 4: Train-Test Split
    • Adjusting Test Set Size
    • Debug the Fruit Dataset Split
    • Splitting the Fruit Dataset
    • Splitting the Iris Dataset
  • Unit 5: Building Full Preprocessing Pipeline for the Titanic Dataset
    • Drop Unwanted Titanic Columns
    • Handle Missing Values in Titanic Dataset
    • Encode Categorical Features and Concatenate
    • Handle Missing Values and Feature Scaling
    • Final Titanic Preprocessing Challenge

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