Customer segmentation – RFM technique on big data using Google Cloud Dataflow

We are quickly becoming big fans of Google Cloud Dataflow.  See our previous posts on this topic here and here.  We are excited to continue using this product and creating analytics solutions for our customers.  In this post we are going to describe how RFM technique can be applied on large data sets – think 100’s …

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Google Cloud Dataflow

Customer segmentation

Consumers, customers, clients or users.  Call them what you will, but businesses exist and grow only when they can serve them well and attract more of them.  While there are several business strategies that one employs, one key methodology is to group or segment similar customers based on past purchasing behavior and geodemographic information.  This helps …

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Customer segmentation

Google Cloud Dataflow

Google Cloud Dataflow has recently entered beta.  It provides a powerful big data processing platform in the cloud.  It’s an end to end solution that comprises compute resources, big data storage, data pipeline development and execution.  Google has open sourced the Cloud Dataflow SDKs that allows users to define data processing jobs.  Here’s a high level …

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Loan data analysis using Azure ML

Here’s my first AzureML model:  The model uses a two class logistic regression algorithm for binary classification. This is based on Python based sample from It leverages various AzureML Studio components as well as custom R code for data cleansing and feature engineering. Here are the various steps I followed to get the model …

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Loan data analysis

Getting Started with Azure ML Studio

Microsoft has released Azure machine learning studio which is a browser based integrated development environment for constructing and operationalizing machine learning workflow on azure.  You can find more details about the tool here - We at springML love programming models in R and Python.  These programming languages along with the myriad of supporting libraries provide …

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Azure ML Studio

ML Tools

When working on a machine learning problem, one follows the following high level steps. Gathering and exploring data Prototype and build models; evaluate and select best model Deploy model so that it’s accessible as a web service Visualization to consume output of models The below table shows a quick grouping of the tools available for each …

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ML Tools