When working with the
diamonds dataset in Python, which contains information about various diamond characteristics, there are common mistakes that users may make. Let’s explore some of the most common ones:
1. Neglecting the importance of data exploration:
Neglecting to explore the dataset before analysis can lead to incorrect assumptions or modeling choices. It’s essential to understand the structure and characteristics of the dataset before proceeding to analysis as it can help you get to know about your data and its structure.
2. Using inappropriate visualizations for the dataset:
Many individuals make the mistake of using inappropriate and incorrect visuals to visualize the data which causes misinterpretation and misleading results. Therefore, always keep in mind which visual is used for which types of features and use them appropriately.
In the example code below, a scatter plot is created between the
carat feature and the
price target variable. A line plot is not used because line plots are used for time-series data and a scatter plot is used to visualize relationships between 2 or more numeric variables.
3. Overplotting the data:
A common mistake occurs when we plot all the data points of the dataset and overcomplicate the plots. For example, when using the
plt.scatter() function to plot
price, overplotting can occur if there are many data points, and they overlap, making it challenging to distinguish individual points and observe patterns.
To address overplotting, you can use techniques such as downsampling, alpha blending, or using different marker sizes. Here’s an example code that uses alpha bending which reduces the transparency of the data points to tackle this issue: