Use SPSS to predict customer churn.ipynb

Kernel: watsonx-ai-samples-py-312

Use SPSS to predict customer churn with ibm-watsonx-ai

This notebook contains steps to deploy sample SPSS stream, and start scoring new data.

Some familiarity with bash is helpful. This notebook uses Python 3.12.

Learning goals

The learning goals of this notebook are:

• Working with the watsonx.ai instance

• Online deployment of SPSS model

• Scoring data using deployed model

Contents

This notebook contains the following parts:

1. Setup

2. Upload model

3. Deploy model

4. Scoring

5. Clean up

6. Summary and next steps

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

• Contact with your Cloud Pak for Data administrator and ask them for your account credentials

Install dependencies

Note: ibm-watsonx-ai documentation can be found here.

%pip install -U wget | tail -n 1
%pip install -U ibm-watsonx-ai | tail -n 1

Successfully installed wget-3.2
Successfully installed anyio-4.9.0 certifi-2025.4.26 charset-normalizer-3.4.2 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 ibm-cos-sdk-2.14.1 ibm-cos-sdk-core-2.14.1 ibm-cos-sdk-s3transfer-2.14.1 ibm-watsonx-ai-1.3.18 idna-3.10 jmespath-1.0.1 lomond-0.3.3 numpy-2.2.5 pandas-2.2.3 pytz-2025.2 requests-2.32.2 sniffio-1.3.1 tabulate-0.9.0 typing_extensions-4.13.2 tzdata-2025.2 urllib3-2.4.0

Define credentials

Authenticate the watsonx.ai Runtime service on IBM Cloud Pak for Data. You need to provide the admin's username and the platform url.

username = "PASTE YOUR USERNAME HERE"
url = "PASTE THE PLATFORM URL HERE"

Use the admin's api_key to authenticate watsonx.ai Runtime services:

import getpass
from ibm_watsonx_ai import Credentials

credentials = Credentials(
    username=username,
    api_key=getpass.getpass("Enter your watsonx.ai API key and hit enter: "),
    url=url,
    instance_id="openshift",
    version="5.2",
)

Alternatively you can use the admin's password:

import getpass
from ibm_watsonx_ai import Credentials

if "credentials" not in locals() or not credentials.api_key:
    credentials = Credentials(
        username=username,
        password=getpass.getpass("Enter your watsonx.ai password and hit enter: "),
        url=url,
        instance_id="openshift",
        version="5.2",
    )

Create APIClient instance

from ibm_watsonx_ai import APIClient

client = APIClient(credentials)

Working with spaces

First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use {PLATFORM_URL}/ml-runtime/spaces?context=icp4data to create one.

• Click New Deployment Space

• Create an empty space

• Go to space Settings tab

• Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: Assign space ID below

space_id = "PASTE YOUR SPACE ID HERE"

You can use list method to print all existing spaces.

client.spaces.list(limit=10)

To be able to interact with all resources available in watsonx.ai, you need to set space which you will be using.

client.set.default_space(space_id)

'SUCCESS'

2. Upload model

In this section you will learn how to upload the model to the Cloud.

Action: Download sample SPSS model from Git repository using wget.

import os
import wget

sample_dir = "spss_sample_model"
if not os.path.isdir(sample_dir):
    os.mkdir(sample_dir)

filename = os.path.join(sample_dir, "customer-satisfaction-prediction.str")
if not os.path.isfile(filename):
    filename = wget.download(
        "https://github.com/IBM/watsonx-ai-samples/raw/master/cpd5.2/models/spss/customer_satisfaction/model/customer-satisfaction-prediction.str",
        out=sample_dir,
    )
print(filename)

spss_sample_model/customer-satisfaction-prediction.str

List available software specifications

client.software_specifications.list()

Store SPSS model in your watsonx.ai instance.

sw_spec_id = client.software_specifications.get_id_by_name("spss-modeler_18.2")

model_meta_props = {
    client.repository.ModelMetaNames.NAME: "SPSS customer satisfaction model",
    client.repository.ModelMetaNames.TYPE: "spss-modeler_18.2",
    client.repository.ModelMetaNames.SOFTWARE_SPEC_ID: sw_spec_id,
}

model_details = client.repository.store_model(filename, model_meta_props)

Note: You can see that model is successfully stored in watsonx.ai.

client.repository.list_models()

3. Create online deployment

You can use commands bellow to create online deployment for stored model (web service).

model_id = client.repository.get_model_id(model_details)

deployment = client.deployments.create(
    artifact_id=model_id,
    meta_props={
        client.deployments.ConfigurationMetaNames.NAME: "SPSS deployment",
        client.deployments.ConfigurationMetaNames.ONLINE: {},
    },
)

######################################################################################

Synchronous deployment creation for id: 'a299af8d-73d2-4ac9-ba3d-9a3fc239ba08' started

######################################################################################


initializing
Note: online_url is deprecated and will be removed in a future release. Use serving_urls instead.
.....................................
ready


-----------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_id='bf691aa7-82db-4324-8070-232ac7306de8'
-----------------------------------------------------------------------------------------------

4. Scoring

You can send new scoring records to web-service deployment using score method.

deployment_id = client.deployments.get_id(deployment)

scoring_data = {
    client.deployments.ScoringMetaNames.INPUT_DATA: [
        {
            "fields": [
                "customerID",
                "gender",
                "SeniorCitizen",
                "Partner",
                "Dependents",
                "tenure",
                "PhoneService",
                "MultipleLines",
                "InternetService",
                "OnlineSecurity",
                "OnlineBackup",
                "DeviceProtection",
                "TechSupport",
                "StreamingTV",
                "StreamingMovies",
                "Contract",
                "PaperlessBilling",
                "PaymentMethod",
                "MonthlyCharges",
                "TotalCharges",
                "Churn",
                "SampleWeight",
            ],
            "values": [
                [
                    "3638-WEABW",
                    "Female",
                    0,
                    "Yes",
                    "No",
                    58,
                    "Yes",
                    "Yes",
                    "DSL",
                    "No",
                    "Yes",
                    "No",
                    "Yes",
                    "No",
                    "No",
                    "Two year",
                    "Yes",
                    "Credit card (automatic)",
                    59.9,
                    3505.1,
                    "No",
                    2.768,
                ]
            ],
        }
    ]
}

predictions = client.deployments.score(deployment_id, scoring_data)
print(predictions)

{'predictions': [{'fields': ['customerID', 'Churn', 'Predicted Churn', 'Probability of Churn'], 'values': [['3638-WEABW', 'No', 'No', 0.0526309571556145]]}]}

As we can see this sample telco customer is satisfied ("Predicted Churn", "No").

5. Clean up

If you want to clean up all created assets:

• experiments

• trainings

• pipelines

• model definitions

• models

• functions

• deployments

please follow up this sample notebook.

6. Summary and next steps

You successfully completed this notebook! You learned how to use watsonx.ai for SPSS model deployment and scoring.

Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Author

Lukasz Cmielowski, PhD, is an Automation Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.

Copyright © 2020-2025 IBM. This notebook and its source code are released under the terms of the MIT License.