---
title: "Create an ML Pipeline"
url: https://docs.catalyst.zoho.com/en/tutorials/crm-deal-prediction/create-ml-pipeline/
description: "Create a powerful ML pipeline that analyzed historic ZOHO CRM data and meaningfully predicts the likeliness of a successful deal using the Catalyst QuickML components."
last_updated: 2026-10-01T07:03:46Z
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

In this section, we will be building a prediction ML model using the preprocessed dataset from the previous section. This dataset will be the input to the **[ML pipeline builder](https://docs.catalyst.zoho.com/en/quickml/help/create-ml-pipeline/)**, which enables you to define the model's architecture and select a target column for prediction.

**To create an ML pipeline, make sure to follow the steps below:**

1. Navigate to the *Pipelines* component in the left menu and click **Create Pipeline**.

   ![crm-deal-25.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-25.webp)

   ![crm-deal-26.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-26.webp)

2. In the pop-up that appears, select "**Prediction**" as the pipeline type, name the pipeline as "**Deal Prediction ML Pipeline**", and choose the input dataset as **Zoho_CRM_Deal_Prediction_Sampl**e. In our case, the target column should be "**Stage**". The model name will be auto-populated based on the pipeline name. Click **Create**.

   ![crm-deal-27.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-27.webp)

3. The *pipeline Details* page will be displayed as shown in the screenshot below.

   ![crm-deal-28.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-28.webp)

Now that we have created our ML pipeline, we will proceed to configure the pipeline by defining the nodes in the ML pipeline builder interface.

## Encoding categorical columns

Because our target column "**Stage**", contains categorical data of type String, we will encode them for further ML training standards. Please make sure to follow the below steps to encode the columns.

1. In the Operations menu, navigate to *ML tab > ML operations > Encoding > Label Encoder*. Drag and drop the **[Label Encoder](https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/encoding/#ordinal-encoder)** node to the ML pipeline builder interface. 

   ![crm-deal-29.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-29.webp)

2. Navigate to *ML tab > ML operations > Encoding > One-Hot Encoder*. Drag and drop the **One-Hot encoder node** to the ML pipeline builder interface. In the configuration section on the right panel, choose the columns as "**Pipeline**" and click **Save**.

   ![crm-deal-30.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-30.webp)

These encoding operations will convert the column values of type String to Integer, while maintaining the order and preserving data accuracy.

## Normalize the columns

Because the values of all the features are in various ranges, we will use the **Mean-Std Normalization** component to scale down the values of the features to a common range, typically between 0 and 1.

Navigate to *ML tab > ML operations > Normalization*. Drag and drop the **Mean-Std Normalization node** to the ML pipeline builder interface. In the configuration box on the right panel, choose "**Amount**" and click **Save**.

Once normalization is applied, the ML pipeline builder page will be displayed as below:

   ![crm-deal-31.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-31.webp)

## ML algorithm and hyperparameter tuning

For any ML model, it's necessary to implement an ML algorithm based on which the model will be trained. In this tutorial, we will be implementing the **Random-Forest Classification** algorithm to configure the tuning parameters for the ML model to ensure it is optimized for our pre-processed dataset.

1. In the Operations menu, navigate to* ML tab > ML operations > Algorithm > Classification*. Drag and drop the **Random-Forest Classification node** into the pipeline builder. The node will be automatically connected to the **Destination** node. Make an input connection with the **Mean-Std Normalization** and the **Random-Forest Classification** node.

    ![crm-deal-32.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-32.webp)

2. For the *Random-Forest Classification* node, we will go with the default configuration and click **Save**.

Now, we have completed making the required node connections and configurations. Proceed to execute the pipeline by clicking **Execute** for further evaluation and deployment.

  ![crm-deal-33.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-33.webp)

Click **Execution Stats** to view more details about each stage of the execution in detail.

![crm-deal-34.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-34.webp)

Upon successful execution of the ML pipeline, the Deal Prediction model is created and will be displayed under the Models section.

You can view the details of the model on the model's Details page by clicking on the model name.

![crm-deal-35.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/crm-deal-prediction/crm-deal-35.webp)