Category Archives: Machine Learning

Supervised Machine Learning Parameter Search and Tuning with Simulated Annealing

The most challenging phase in supervised Machine Learning pipeline is parameter tuning. There are many parameters, each with a range of values. The so called grid search is brute force approach that tries all possible combinations of values for the … Continue reading

Posted in Machine Learning, Python, ScikitLearn, Supervised Learning | Tagged , , | Leave a comment

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

Posted in Data Science, Machine Learning, Python, ScikitLearn, Supervised Learning | Tagged , , , | Leave a comment

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 , , , | 7 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

Posted in Big Data, Data Science, ETL, Machine Learning, Spark | Tagged , , | Leave a comment

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

Posted in Machine Learning | Tagged | Leave a comment