Write predictions to a SaaS app

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.

X = df.drop(['Prediction', 'Churn', 'CustomerName'], axis=1)
# converting categorical columns to numeric
cat_cols = X.select_dtypes('object').columns
X[cat_cols] = X[cat_cols].apply(lambda x: encoder[x.name].transform(x))

# Making prediction
prediction = model.predict(X)
proba = model.predict_proba(X)[0][prediction]
result = pd.DataFrame({'CustomerID':X['CustomerID'], 'Customer Name':df['CustomerName'],'Prediction': prediction, })#'Probability': proba})

st.dataframe(result)

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

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.
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.

Data Apps

Data App Use Cases

Platform and APIs

Import required modules

Load new records

Data Preprocessing & Predicting

Task creation in Salesforce

What’s Next

​Import required modules

​Load new records

​Data Preprocessing & Predicting

​Task creation in Salesforce

​What’s Next

Import required modules

Load new records

Data Preprocessing & Predicting

Task creation in Salesforce

What’s Next