Implementing on Real Dataset
You'll use the mall customers dataset, which contains the following columns:
You should also follow these steps before clustering:
- Load the data: you'll use
pandas
to load the CSV file; - Select relevant features: you'll focus on
'Annual Income (k$)'
and'Spending Score (1-100)'
columns; - Data scaling (important for DBSCAN): since DBSCAN uses distance calculations, it's crucial to scale features to have similar ranges. You can use
StandardScaler
for this purpose.
Interpretation
The code creates 5 clusters in this case. It's important to analyze the resulting clusters to gain insights into customer segmentation. For example, you might find clusters representing:
-
High-income, high-spending customers;
-
High-income, low-spending customers;
-
Low-income, high-spending customers;
-
Low-income, low-spending customers;
-
Middle-income, middle-spending customers.
Concluding Remarks
Thanks for your feedback!
Ask AI
Ask AI
Ask anything or try one of the suggested questions to begin our chat
Awesome!
Completion rate improved to 2.94
Implementing on Real Dataset
Swipe to show menu
You'll use the mall customers dataset, which contains the following columns:
You should also follow these steps before clustering:
- Load the data: you'll use
pandas
to load the CSV file; - Select relevant features: you'll focus on
'Annual Income (k$)'
and'Spending Score (1-100)'
columns; - Data scaling (important for DBSCAN): since DBSCAN uses distance calculations, it's crucial to scale features to have similar ranges. You can use
StandardScaler
for this purpose.
Interpretation
The code creates 5 clusters in this case. It's important to analyze the resulting clusters to gain insights into customer segmentation. For example, you might find clusters representing:
-
High-income, high-spending customers;
-
High-income, low-spending customers;
-
Low-income, high-spending customers;
-
Low-income, low-spending customers;
-
Middle-income, middle-spending customers.
Concluding Remarks
Thanks for your feedback!