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Data Science

329 learners

Intro to Data Cleaning and Preprocessing with Diamonds

Learn to clean and preprocess the diamonds dataset, including converting categorical features to ordered types and visualizing data distributions. Gain essential skills in data preparation and visualization techniques, ensuring a solid foundation for deeper data analysis and modeling tasks.

Pandas

Python

Scikit-learn

Seaborn

See path

5 lessons

25 practices

2 hours

Data Cleaning and Preprocessing

Lessons and practices

Lesson 1: Basic Data Cleaning with the Diamonds Dataset

Change Missing Value Column

Common Mistake: Fixing Null Values

Handling Missing Values Efficiently

Handling Missing Values in Diamonds

Write from Scratch: Handle Missing Values

Lesson 2: Converting Categorical Data to Ordered Types in Python

Change Clarity Category Order

Fix Conversion of Categorical Data

Convert Data Types for Diamonds

Categorical Types Conversion Task

Convert Categorical Data to Ordered

Lesson 3: Visualizing Diamond Price Distribution

Adjust the Histogram Parameters

Fix Histogram Visualization Errors

Customize the Histogram

Complete the Histogram Visualization

Visualize Diamond Price Distribution

Lesson 4: Detecting and Handling Outliers in the Diamonds Dataset

Handle Outliers in Carat Column

Fix Issues in Outlier Detection

Detect and Remove Outliers

Flagging Outliers in the Diamonds Dataset

Outliers Handling from Scratch

Lesson 5: Standardizing Numerical Features in the Diamonds Dataset

Selective Standardization Exercise

Fix Bugs in Standardization Code

Standardize Numerical Features Practice

Standardize Specific Features with MinMax

Standardizing Numerical Features from Scratch

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