CRM Deal Prediction

This tutorial will guide you through building a machine learning model using Catalyst QuickML to predict potential deals by analyzing data from Zoho CRM. A sample dataset is provided, which can be used as the data source for the model.

Stage 1: Preprocess the Dataset

Start by cleaning and preparing the dataset to ensure it is suitable for training. This involves handling missing values, encoding categorical fields, and applying any necessary data transformations.

Stage 2: Build the Data Pipeline

Create a data pipeline using Catalyst QuickML. Use node operations to manage tasks such as data cleaning, encoding, and feature engineering. This reusable pipeline ensures consistent preprocessing for all future model training.

Stage 3: Build the ML Pipeline

Construct the ML pipeline by connecting the processed data from the data pipeline. Select an appropriate ML algorithms, configure its parameters, and initiate the training process. QuickML automatically evaluates the model after training to validate its performance.

Stage 4: Deploy the Model via Endpoint

Once trained, generate an endpoint URL within QuickML. This endpoint allows external applications to send input data and receive real-time predictions from the CRM Deal Prediction model.

The final output, after creating all of the required data and ML pipelines in the Catalyst console, will look like this: