Integrate predictive ML models with SaaS apps and update fields in your business applications.

The Peliqan platform allows you to write predictions to a SaaS application such as Salesforce. Use Writeback to update fields or create new records (e.g. Tasks) to automate actions based on predictions.

This tutorial is tailored for a Churn Prediction App. We will create Tasks in Salesforce, you can modify it to work with other models as well.

Import required modules

from sklearn.feature_extraction.text import TfidfVectorizer
from joblib import load
import pandas as pd

# Loading models to make predictions
model = load('/data_app/model_churn_prediction')
encoder = load('/data_app/encoder_churn_prediction')

Load new records

Load new records that do not have a prediction yet (prediction is null). Make sure you create a “Prediction” column first in the spreadsheet view of the table:

dbconn = pq.dbconnect(pq.DW_NAME)
my_query = 'select * from customer_churn where prediction is null limit 5'
df = dbconn.fetch(pq.DW_NAME, query = my_query, df = True)

Data Preprocessing & Predicting

For predicting, we have to prepare the data in the same format as it was trained on.

Note: It's recommended to add Try/Except statement to capture the error when there are no records to update.

Task creation in Salesforce

for _, record in results.iterrows()
	if record['Prediction'] == 1:
		# create a Task in Salesforce for follow up by an Sales Account Manager
		Salesforce.add("task", title = "Reach out to " + record["name"])

Click here to learn more about write-back to SaaS apps.

What’s Next

1. You can make real-time predictions on new incoming data and send alerts to slack if the model makes a prediction above a certain threshold. Learn more about sending critical notifications to slack.
2. Using Peliqan you can create an app for business users to consume the model you have made in a simple and intuitive UI. Learn more about creating apps for business users to consume your model.