Category Archives: Machine Learning

Auto Training and Parameter Tuning for a ScikitLearn based Model for Leads Conversion Prediction

This is a sequel to my last blog on CRM leads conversion prediction using Gradient Boosted Trees as implemented in ScikitLearn. The focus of this blog is automatic training and parameter tuning for the model. The implementation is available in … Continue reading

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Predicting CRM Lead Conversion with Gradient Boosting using ScikitLearn

Sales leads are are generally managed and nurtured in CRM systems. It will be nice if we could predict the likelihood of any lead converting to an actual deal. This could be very beneficial in many ways e.g. proactively  providing … Continue reading

Posted in Data Science, Machine Learning, Optimization, Python, ScikitLearn | Tagged , , , | 2 Comments

Data Normalization with Spark

Data normalization is a required data preparation step for many Machine Learning algorithms. These algorithms are sensitive to the relative values of the feature attributes. Data normalization is the process of bringing all the attribute values within some desired range. Unless … Continue reading

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Predicting Call Hangup in Customer Service Calls with Decision Tree and Random Forest

When customers hangup after a long wait in a call, it’s money wasted for the company. Moreover, it leaves the customer with a poor experience. It would have been nice, if we could predict in real time while the customer … Continue reading

Posted in Big Data, Customer Service, Hadoop and Map Reduce, Machine Learning, Predictive Analytic | Tagged , , | 3 Comments

Machine Learning at Scale with Parallel Processing

Machine Learning can leverage modern parallel data processing platforms like Hadoop and Spark in several ways. In this post we will discuss how to have Machine Learning at scale with Hadoop or Spark. We will consider three different ways parallel … Continue reading

Posted in Hadoop and Map Reduce, Machine Learning, Spark | Tagged , , | 4 Comments

Debunking the Myth of Top Ten Machine Learning Algorithms

This kind of broad brush statements about Machine Learning algorithms are made often and there are lot of online content alluding to this simplistic view of Machine Learning. It’s tempting to gravitate towards simplistic views and use recipe like approach while … Continue reading

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Gaining Insight by Mining Simple Rules from Customer Service Call Data

Although the goal for most predictive analytic problem is to make prediction, sometimes we are more interested in the model learnt by the learning algorithm. If the learnt model could be expressed as s set of rules, then those rules … Continue reading

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