Data Science
592 learners
Data Preprocessing For Machine Learning
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.
Pandas
Python
Scikit-learn
5 lessons
23 practices
3 hours
Data Cleaning and Preprocessing
Lessons and practices
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
Encoding Car Brands and Colors
Encoding Car Brands and Colors
Changing OneHotEncoder to LabelEncoder
Encoding Car Brands with OneHotEncoder
Encode Car Brands and Colors
Scaling Recipe Ingredients
Standardize Ingredient Quantities
Feature Scaling for Recipe Measurements
Recipe Data Scaling
Adjusting Test Set Size
Debug the Fruit Dataset Split
Splitting the Fruit Dataset
Splitting the Iris 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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