Category Archives: Hadoop and Map Reduce

Combating High Cardinality Features in Supervised Machine Learning

Typical training data set for real world machine learning problems has mixture of different types of data including numerical and categorical. Many machine learning algorithms can not handle categorical variables. Those that can, categorical data can pose a serious problem … Continue reading

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Handling Rare Events and Class Imbalance in Predictive Modeling for Machine Failure

Most supervised Machine Learning algorithms face difficulty when there is class imbalance in the training data i.e., amount of data belonging one class heavily outnumber the other class. However, there are may real life problems where we encounter this situation e.g., … Continue reading

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Processing Missing Values with Hadoop

Missing values are just part of life in the data processing world. In most cases you can not simply ignore the missing values as it may adversely affect whatever analytic processing you are going to do. Broadly speaking, handling missing … 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 , , | 2 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

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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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Simple Sanity Checks for Data Correctness with Spark

Sometimes when running a complex data processing pipeline with Hadoop or Spark, you may encounter data, where most of the data is just grossly invalid. It might save lot of pain and headache, if we could do some simple sanity checks before feeding … Continue reading

Posted in ETL, Hadoop and Map Reduce, Spark | Tagged | 1 Comment