Category Archives: Big Data

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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Optimizing Discount Price for Perishable Products with Thompson Sampling using Spark

For retailers, stocking perishable products is a risky business. If a product doesn’t sell completely by the expiry date, then the remaining inventory has to be discarded and loss be taken for those items. Retailers will do whatever is necessary … Continue reading

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Data Type Auto Discovery with Spark

In the life of a Data Scientist, it’s not uncommon to run into a data set with no knowledge or very little knowledge about the data. You may be interested in learning about such data with missing meta data  through … 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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