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- Combating High Cardinality Features in Supervised Machine Learning
- Customer Conversion Prediction with Markov Chain Classifier
- Six Unsupervised Extractive Text Summarization Techniques Side by Side
- Storing Nested Objects in Cassandra with Composite Columns
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- Bulk Insert, Update and Delete in Hadoop Data Lake
- Leave One Out Encoding for Categorical Feature Variables on Spark
- Realtime Trending Analysis with Approximate Algorithms
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Category Archives: Mobile
Counting Unique Mobile App Users with HyperLogLog
Continuing along the theme of real time analytic with approximate algorithms, the focus this time is approximate cardinality estimation. To put the ideas in a context, the use case we will be working with is for counting number of unique users … Continue reading
Posted in Approximate Query, Big Data, Data Science, Mobile, Real Time Processing, Storm
Tagged cardinality, mobile, unique count
1 Comment
Location and Time Based Service
When I implemented feature similarity based matching engine in my open source Personalization and Recommendation Engine sifarish, it was for addressing the cold start problem. It allowed me to do content or feature based recommendation for users with limited engagement. … Continue reading
Posted in Big Data, Hadoop and Map Reduce, Mobile, Real Time Processing, Recommendation Engine, Search, Spark, Storm
Tagged contextual search, lbs, location based service, mobile
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