Welcome! In today's lesson, you'll be diving into the world of data cleaning to learn how to Detect and Handle Outliers using the Diamonds dataset from the seaborn library. Outliers can significantly affect the quality of your data analysis and models, so it's crucial to identify and manage them correctly.

By the end of this lesson, you'll be able to identify outliers using boxplots and remove them using interquartile range (IQR) thresholds.

• Understanding Outliers
• Identifying Outliers using IQR
• Visualizing Outliers with Boxplots
• Removing Outliers from the Dataset
• Verifying the Cleaning Process

First, let's define what an outlier is in the context of data analysis.

Outliers are data points that differ significantly from other observations. These can be errors in data, variability in measurement, or they may indicate a varying characteristic you might need to explore.

Handling outliers is critical because they can distort statistical analyses and models. For example, extreme values can skew the mean and standard deviation of your dataset, leading to inaccurate conclusions and poor model performance.

In simple terms, imagine if you were analyzing the average height of a population and included some incorrect measurements that were twice or half the normal height. Your analysis would be misleading.

Next, we will identify the outliers using the Interquartile Range (IQR) method.