Category Archives: ETL

Leave One Out Encoding for Categorical Feature Variables on Spark

Categorical feature variables is a thorny issue for many supervised Machine Learning algorithms. Many learning algorithms can not handle categorical feature variables. In this post, we will go over an encoding scheme called Leave One Out Encoding, as implemented with … Continue reading

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Handling Categorical Feature Variables in Machine Learning using Spark

Categorical features variables i.e. features variables with fixed set of unique values  appear in the training data set for many real world problems. However, categorical variables pose a serious problem for many Machine Learning algorithms. Some examples of such algorithms … Continue reading

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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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Removing Duplicates from Order Data Using Spark

If you work with data, there is a high probability that you have run into duplicate data in your data set. Removing duplicates in Big Data is a computationally intensive process and parallel cluster processing with Hadoop or Spark becomes … Continue reading

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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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