---
title: "Pipeline Configuration"
url: https://docs.catalyst.zoho.com/en/tutorials/customer-segmentation-analysis/pipeline-configuration/
description: "Create and configure a data and ML pipeline with a readily available dataset to analyze customer segmentation, using the Catalyst QuickML service."
last_updated: 2026-11-04T10:10:44Z
service: All Services
type: tutorial
related_links:
  - name: "ML Algorithms in QuickML"
    url: /en/quickml/help/ml-algorithms/classification-algorithms
  - name: "Operations in QuickML"
    url: /en/quickml/help/operations-in-quickml/encoding
---

To build any ML model,  the  pipeline should involve data cleaning,  preprocessing  stages ,  and choosing the right model .  

## 1. Fill columns 

From the dataset profile generated during the upload stage, it's clear that there are missing values  in the dataset ,  and they need to be handled .   

The fill columns stage is used in the pipeline to fill in  cells  that  are empty or null. 

We’ll drag and drop the   fill columns   node  by navigating to  **Data Cleaning** then **Fill Columns** and connecting  it to  the  source node. 

![pipeline-config-1](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-1.webp)

From the dataset profile page, we  found  the missing values  in   **minimum_payments** .   In the fill columns configuration page,  we'll substitute them with  **Mean**  values. This will update missing values with the column mean.   

Click  **Save**  to save the pipeline.   

## 2. Select/Drop  

Now we have to drop  the   **cust_id**  column, since  ID  columns aren't useful during the clustering model training process.    

We’ll drag and drop the  _Select/Drop_ node by navigating to **Data Cleaning** and **Select/Drop** in the pipeline builder. Connect it to the previous node to open the node configuration panel. 

![pipeline-config-2](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-2.webp)  

Choose  **cust_id**  in the columns dropdown as shown above, select  **drop**  as the operation to execute ,  and click  **Save**   to save the stage. The selected column has been dropped from the training dataset ,  which you can see in the preview section.  

![pipeline-config-3](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-3.webp)  

## 3. Mean normalization  

Now we have the full training dataset ready with  the  required columns and without any missing values.  Check  the value ranges from all the columns :  each column has values across various ranges.  We'll  use  the normalization technique to bring all the  data  to a common scale.    
Learn more about normalization in [our documentation](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/normalization/) . 

This  keeps  the model's training process  from  getting  dominated by  data  with  a  higher magnitude. For our case, we'll use mean normalization.

Let's drag and drop the _Mean Normalisation_ node by navigating to **ML Operations**, **Normalisation**, and **Mean Normalisation** in the pipeline builder. 

![pipeline-config-4](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-4.webp)  

Connect the node to  the  previous stage to open the configuration panel. Select all the available columns using the  **Select all**  button and click  **Save** .

With this, the preprocessing of the dataset is completed, and the dataset is ready.  

## 4. K\-means algorithm

The final step in the pipeline building process is to select the appropriate algorithm, configure it with the required number of clusters, and execute it.

Let's use the K-means algorithm. We'll provide the number of clusters in the algorithm configuration panel,  so the model must create only  **K**  clusters using our training dataset.   

Let's drag and drop the algorithm node by navigating to  **ML Operations** , **Algorithms**, and **KMeans** in the pipeline builder.

![pipeline-config-5](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-5.webp)  

Connect it to the previous stage to open the configuration panel, and the other end will be  automatically connected to destination. This finishes the pipeline building.

> **Note:** We're using 3 clusters as a starting point for this tutorial. In practice, you can determine the optimal number of clusters by running the model with different K values and comparing the evaluation metrics to indicate better-defined clusters. 

Update the  **Number of clusters**  parameter to  **3**   and click  **Save** .     

![pipeline-config-6](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-6.webp)  

Now that the entire clustering pipeline is built and saved,   click the  **Execute**  button   at the top-right corner of the pipeline builder page   to start executing the pipeline.    

This will redirect you to the pipeline details page shown below, which provides details about the executed pipeline with execution status. 

You can see that the pipeline execution is successful. 

![pipeline-config-7](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-7.webp) 

Click  **Execution Stats**  to view compute usage  details about each stage of the model execution.   

![pipeline-config-8](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-8.webp)  

Now the model   **Credit Card customer clustering model**  is generated after training ,  and it's ready for evaluation.    

![pipeline-config-9](https://catalyst-docs-development.zohostratus.in/images/tutorials/customer-segmentation-analysis/pipeline-config-9.webp)  

Click  the model hyperlink   **Credit Card customer clustering model**  to open  the model details page  and see  necessary information about model details, evaluation metrics,  clustering visualizations ,  and versioning details.