---
title: "Create Data Pipeline"
url: https://docs.catalyst.zoho.com/en/tutorials/churn-for-bank-customers/create-data-pipeline/
description: "Churn For Bank Customers, Create a powerful ML pipeline that can be used to predict the churn using the Catalyst QuickML service."
last_updated: 2026-08-17T12:16:04Z
service: All Services
type: tutorial
related_links:
  - name: "Data Cleaning"
    url: /en/quickml/help/data-preprocessing/data-cleaning
  - name: "Data Transformation"
    url: /en/quickml/help/data-preprocessing/data-transformation
  - name: "Data Profiler and Viewer"
    url: /en/quickml/help/data-profiler-and-viewer/
---

# Create a data pipeline

Now that we have uploaded the dataset, we will proceed with creating a [data pipeline](https://docs.catalyst.zoho.com/en/quickml/help/create-data-pipeline/) with the dataset.

1. Navigate to the **Datasets** component in the left menu. There are two ways to create a data pipeline:
    - You can click on the dataset and then click `Create Pipeline` in the top-right corner of the page.
    ![create-pipeline](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/churn-for-bank-customers-datasets.webp)

    - You can click on the pen icon located to the left of the dataset name, as shown in the image below.
    ![pen-icon](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/chun-for-bank-customers-datasets.webp)

    Here, we are uploading the **Bank_Customers_Sample_Data** dataset for preprocessing.

2. Name the pipeline "**Churn_Prediction_Data_Pipeline**" and click `Create Pipeline`.
![Pipeline Name](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/churn-for-bank-customers-create-pipeline.webp)

The [pipeline builder interface](https://docs.catalyst.zoho.com/en/quickml/help/pipeline-builder-interface/walkthrough/#pipeline-builder-interface-1) will open as shown in the screenshot below.
![Initial Pipeline](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/initial-pipeline.webp)

We will be performing the following set of data preprocessing operations in order to clean, refine, and transform the datasets, and then execute the data pipeline. Each of these operations involve individual [data nodes](https://docs.catalyst.zoho.com/en/quickml/help/data-preprocessing/data-cleaning/)  that are used to construct the pipeline.

### Data preprocessing with QuickML
1. #### Select/drop columns
    Selecting or dropping columns from a dataset is a common data preprocessing step in data analysis and machine learning. The choice to select or drop columns depends on the specific objectives and requirements of your analysis or modelling task.
    The columns we don't need for our model training from this dataset are "**RowNumber**", "**CustomerId**" and "**Surname**" in the provided datasets. Using QuickML, you may quickly choose the necessary fields from the dataset for model training using the **Select/Drop** [node](https://docs.catalyst.zoho.com/en/quickml/help/data-preprocessing/data-cleaning/#select-or-drop) from the **Data Cleaning** component.
    ![required-field-selection](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/required-field-selection.webp)

2. #### Filling columns in dataset with values
   Using the `Fill Columns` [node](https://docs.catalyst.zoho.com/en/quickml/help/data-preprocessing/data-cleaning/#fill-columns) in **Data Cleaning**, we can easily fill the column values based on any certain condition. We can fill the null values or non-null values based on our requirements. For the columns "**EstimatedSalary,**" and "**Balance**" we are replacing the empty values with a custom value of "**0**". 
   ![Fill Column](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/filling-columns-in-dataset-churn-bank-customers.webp)

3. #### Filter Data
    Filtering a dataset typically involves selecting a subset of rows from a DataFrame that meet certain criteria or conditions. Here we are using the Filter node from the **Data Cleaning** session to filter all the columns "**CreditScore**", "**Geography**", "**Gender**", "**Age**", "**Tenure**" and "**Exited**" that have non-empty values using the `Filter` [node](https://docs.catalyst.zoho.com/en/quickml/help/data-preprocessing/data-cleaning/#filter) from the Data Cleaning session.
    ![Data Filter](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/churn-for-bank-customers-filter-data.webp)

4. #### Save and Execute
    Once all the nodes are connected, click the `Save` button to save the pipeline. Then click on `Execute` button to execute the pipeline.
    ![Completed data pipeline](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/completed-data-pipeline.webp)

You'll be redirected to the page below, which shows the executed pipeline with the execution status. We can see here that the pipeline execution was successful.

![Executed data pipeline](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/executed-pipeline.webp)

Click on `Execution Stats` to access more details regarding the compute usage, as shown below.

![Execution stats for data pipeline](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/execution-stats.webp)

In this part, we've looked at how to process data using QuickML, giving you a variety of effective ways to get your data ready for the creation of machine learning models. This data pipeline can be reused to create multiple ML experiments for varied use cases within your Catalyst project.