Embarking on this course allows you to deeply understand and apply data cleaning and preprocessing techniques. It systematically covers the concepts of data cleaning, handling missing values, normalization, binning, encoding, and more, aiming to equip you with practical skills for preparing data for analysis or machine learning tasks.
Applying Missing Values Handling to Clients' Personal Information Dataset
Replacing Mean with Median
Filling the Missing Values in the Clients Dataset
Addressing Missing Values in Client Data
Identifying Duplicates and Outliers in Height Dataset
Cleaning Duplicates from the Dataset
Cleaning Up School Data: Handling Duplicates and Outliers
Removing Duplicates and Handling Outliers in Student Data
Clean School Data: Handling Duplicates and Outliers
Exploring Normalization of Planet Orbit Speeds
Normalization on Planet Diameter
Planetary Orbital Speed Normalization Fix
Applying Min-Max Normalization to Planetary Distances
Normalization of Planetary Orbits
Encoding Clothing Categories and Sizes
Changing One-Hot Encoding to Label Encoding for Clothing Type Data
Fix the Clothing Store Inventory Management System
Mapping Clothing Sizes to Numerical Values
Applying Categorical Data Encoding in Clothing Store Inventory Management
Binning Student Ages into Grade Levels
Altering Labels in Data Binning
Categorizing Student Ages with Data Binning
Implementing Binning Technique in Data Preparation
Categorizing Ages into Groups