---
title: "Create ML Pipeline"
url: https://docs.catalyst.zoho.com/en/tutorials/churn-for-bank-customers/create-ml-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: "ML Algorithms in QuickML"
    url: /en/quickml/help/ml-algorithms/classification-algorithms
  - name: "Operations in QuickML"
    url: /en/quickml/help/operations-in-quickml/encoding
---

# Create an ML pipeline

To build the prediction model, we will use the preprocessed dataset in the [ML Pipeline Builder](https://docs.catalyst.zoho.com/en/quickml/help/create-ml-pipeline/). The initial step in building the ML Pipeline involves selecting the **target column**, which is the column that we are trying to predict.

To create an ML pipeline, first Navigate to the **Pipelines** component and click on the `Create Pipeline` option.
![ML Pipeline Creation 1](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/ml-pipeline-creation-1.webp)

In the pop-up that appears, select **Prediction** as pipeline type and provide the pipeline name, we'll name the pipeline as **Churn_Prediction_ML_Pipeline** and the model **Churn_Prediction_ML_Pipeline Model** in the Create Pipeline pop-up. Then, select the appropriate dataset and the column name of the target.
![ML Pipeline Creation Meta](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/create-pipeline-churn-for-bank-cust.webp)

We need to select the source dataset that is chosen for building the data pipeline, as the preprocessed data is reflected in the source dataset. In our case, we will be importing the **Bank_Customers_Sample_Data** dataset, as we have selected it for preprocessing and cleaning, and our target is the column named **Exited**.

1. ### Imputers
      Imputers are used in various fields, such as data analysis, statistics, and machine learning to handle missing or incomplete data. Here, we are using mean imputer by importing it from **ML operations > Imputers > Mean Imputer** for imputing the missing values in the dataset.
      Mean Imputing & Mode Imputing refers to a data imputation technique where missing values are filled based on some mean or mode of selected columns. 
      ![Mean Imputer](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/mean-imputer.webp)

      Here, the columns should not contain empty values for best model predictions are "**CreditScore**","**Age**","**Tenure**","**Balance**","**NumOfProducts**","**HasCrCard**","**IsActiveMember**","**EstimatedSalary**" imputed by its mean values and the few columns that are imputed by their mode are "**Gender**","**Geography**".
      ![Mode Imputer](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/mode-imputer.webp)

2. ### Encoding
      Encoders are used in various data preprocessing and machine learning tasks to convert categorical or non-numeric data into a numerical format that machine learning algorithms can work with effectively.

      #### Ordinal encoding
      Here, we are using ordinal encoding to encode the following categorical features: "gender". It assigns integers to the categories based on their order, making it possible for machine learning algorithms to capture the ordinal nature of the data. We'll use the [Ordinal Encoder](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/encoding/#ordinal-encoder) node by navigating to ML operations, clicking the ->**Encoding component**, and choosing -> **Ordinal Encoder** in QuickML to turn the selected category columns into numerical columns. 
      ![Ordinal Encoder](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/ordinal-encoder.webp)

      #### Ordinal Encoder
      [Ordinal Encoding](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/encoding/) involves mapping each unique label to an integer value. This type of encoding is really only appropriate if there is a known relationship between the categories. If the data is ordered, we can use ordinal encoding.

      Here we aare using `Ordinal Encoder` node to encode the **Gender** column. We can use the `Ordinal Encoder` node from **ML Operations** > **Encoding** > **Ordinal Encoder** in QuickML to turn the category columns into numerical columns. Here, we are converting all categorical columns to numerical format while retaining the columns’ original order and data for model training.

3. ### One-hot encoding
      [One-hot encoding](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/encoding/#one-hot-encoding) is typically applied to categorical columns in a dataset, where each category represents a distinct class or group. This method typically increases the dimensionality of the dataset because it creates a new binary column for each unique category. The number of binary columns is equal to the number of unique categories minus one, as you can infer the presence of the last category from the absence of all others.

      Here, we are using `One-Hot Encoder` node to encode the following column: "**Geography**". We'll use the One-Hot Encoder node by navigating to  **ML operations**, selecting the -> **Encoding** component and choosing -> **One-Hot Encoder** in QuickML to turn the selected category columns into numerical columns.
    ![One Hot Encoding](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/one-hot-encoding.webp)

4. ### Normalize the columns
      Navigate to **ML operations-> Normalization**. Drag and drop the **Min-Max Normalization** [node](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/normalization/#min-max-normalization) to the ML pipeline builder interface. In the configuration box on the right panel, choose all the columns except **Exited** which is the target and click Save.
      ![Normalization](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/normalization.webp)

5. ### Feature Engineering:
    [Feature selection](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/feature-engineering/#feature-selection) is the process of choosing a subset of the most relevant and important features (variables or columns) from the dataset to use in model training and analysis. The goal of feature selection is to improve the performance, efficiency, and interpretability of machine learning models. Feature selection is particularly crucial when dealing with high-dimensional datasets, as it can help reduce overfitting, reduce computation time, and enhance model interpretability.

    Here we are using the **PCA** [feature](http://localhost:1313/en/quickml/help/operations-in-quickml/feature-engineering/#feature-reduction) selection technique to generate the features. Select **PCA** node by navigating to **ML operations**, clicking ->**Feature Engineering**, and choosing -**>Feature Reduction**.
    ![Feature Selection](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/PCA.webp)

6. ### ML Algorithm:
	The next step in ML pipeline building is selecting the appropriate algorithm for training the preprocessed data. Here we'll use the [Random-Forest Classification](https://docs.catalyst.zoho.com/en/quickml/help/ml-algorithms/classification-algorithms/#random-forest-classification) to train the data.

  In order to make sure the model is optimized for our particular dataset, we may also adjust the tuning parameters; in our instance, we can just stick with the default settings. Select **Random-Forest Classification** node by navigating to **ML operations**, clicking ->**Algorithms**, and choosing ->**Classification**. When everything is configured, we may save the pipeline for further testing and deployment.
  ![random-forest-classification](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/random-forest-classification.webp)

Once we drag-and-drop the algorithm node, its end node will be automatically connected to the destination node. Click `Save` to save the pipeline and execute the pipeline by clicking the `Execute` button at the top-right corner of the pipeline builder page.
This will redirect you to the page below which shows the executed pipeline with execution status. We can clearly see here that the pipeline execution is successful.
![executed-ml-pipeline](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/executed-ml-pipeline.webp)

Click `Execution Stats` to view more compute details about each stage of the model execution in detail.
![execution-stats-ml-pipeline](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/execution-stats-ml-pipeline.webp)

The prediction model is created and can be examined under the Model section(click on **Churn_Prediction_ML_Pipeline Model**) following the successful completion of the ML workflow. 
![Model name](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/model-name.webp)

This offers useful perceptions into the efficiency and performance of the model while making predictions based on the data.
![Model-Metrics](https://catalyst-docs-development.zohostratus.in/images/tutorials/churn-for-bank-customers/model-metrics.webp)