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Recent Posts
- Machine Learning Model Interpretation and Prescriptive Analytic with Lime
- Automated Machine Learning with Hyperopt and Scikitlearn without Writing Python Code
- Time Series Trend and Seasonality Component Decomposition with STL on Spark
- Missing Value Imputation with Restricted Boltzmann Machine Neural Network
- Encoding High Cardinality Categorical Variables with Feature Hashing on Spark
Top Posts
- Combating High Cardinality Features in Supervised Machine Learning
- Leave One Out Encoding for Categorical Feature Variables on Spark
- Data Normalization with Spark
- Improving Elastic Search Query Result with Query Expansion using Topic Modeling
- Customer Conversion Prediction with Markov Chain Classifier
- Bandits Know the Best Product Price
- Removing Duplicates from Order Data Using Spark
- Encoding High Cardinality Categorical Variables with Feature Hashing on Spark
- Time Series Sequence Anomaly Detection with Markov Chain on Spark
- Six Unsupervised Extractive Text Summarization Techniques Side by Side
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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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