-
Recent Posts
- Evaluation of Time Series Predictability with Kaboudan Metric using Prophet
- Contextual Data Completeness Metric Computation on Spark
- 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
Top Posts
- Combating High Cardinality Features in Supervised Machine Learning
- Pluggable Rule Driven Data Validation with Spark
- Leave One Out Encoding for Categorical Feature Variables on Spark
- Storing Nested Objects in Cassandra with Composite Columns
- Removing Duplicates from Order Data Using Spark
- Anomaly Detection with Robust Zscore
- Hive Plays Well with JSON
- Customer Conversion Prediction with Markov Chain Classifier
- Improving Elastic Search Query Result with Query Expansion using Topic Modeling
- Encoding High Cardinality Categorical Variables with Feature Hashing on Spark
Archives
- January 2020
- December 2019
- November 2019
- October 2019
- September 2019
- August 2019
- July 2019
- June 2019
- May 2019
- April 2019
- March 2019
- February 2019
- January 2019
- December 2018
- November 2018
- October 2018
- September 2018
- August 2018
- July 2018
- June 2018
- May 2018
- April 2018
- March 2018
- February 2018
- January 2018
- December 2017
- November 2017
- October 2017
- September 2017
- August 2017
- July 2017
- June 2017
- May 2017
- April 2017
- March 2017
- February 2017
- January 2017
- December 2016
- November 2016
- October 2016
- September 2016
- August 2016
- July 2016
- June 2016
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- December 2015
- November 2015
- October 2015
- September 2015
- August 2015
- July 2015
- June 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- December 2014
- November 2014
- October 2014
- September 2014
- August 2014
- July 2014
- June 2014
- May 2014
- April 2014
- March 2014
- February 2014
- January 2014
- December 2013
- November 2013
- October 2013
- September 2013
- August 2013
- July 2013
- June 2013
- May 2013
- April 2013
- March 2013
- February 2013
- January 2013
- December 2012
- November 2012
- October 2012
- September 2012
- August 2012
- July 2012
- June 2012
- May 2012
- April 2012
- March 2012
- February 2012
- January 2012
- December 2011
- November 2011
- October 2011
- August 2011
- July 2011
- June 2011
- May 2011
- April 2011
- March 2011
- January 2011
- December 2010
- November 2010
- October 2010
- September 2010
- August 2010
Categories
- AI
- Anomaly Detection
- Approximate Query
- Association Mining
- Big Data
- BPM
- Cassandra
- Cluster Computation
- Collaborative Filtering
- Correlation
- Customer Service
- Data Mining
- Data Model
- Data Profiling
- data quality
- Data Science
- Data Transformation
- Data Warehouse
- Deep Learning
- eCommerce
- elastic search
- ETL
- Fraud Detection
- Hadoop and Map Reduce
- HBase
- Healthcare Analytic
- Hive
- Indexing
- Internet of Things
- Java
- Key Value Store
- Log Analysis
- Machine Learning
- Map Reduce
- Marketing Analytic
- Messaging
- Mobile
- MongoDB
- NLP
- NOSQL
- Optimizatiom
- Optimization
- Outlier Detection
- Performance
- Personalization
- Predictive Analytic
- Programing Language
- Python
- Query
- Real Time Processing
- Recommendation Engine
- Redis
- Reinforcement Learning
- Ruby
- Rule Engine
- Rule Mining
- Scala
- ScikitLearn
- Search
- Search Analytic
- Semantic
- Simulation
- Solr
- Spark
- Spark Streaming
- Statistics
- Storm
- stream processing
- Supervised Learning
- Text Analytic
- Text Mining
- Time Series Analytic
- Topic Modeling
- Uncategorized
- Web
- Web Analytic
- Workflow
Meta
- Anomaly Detection Big Data Cassandra Collaborative Filtering Data Mining Data Science eCommerce ETL Fraud Detection Hadoop and Map Reduce Java Machine Learning Map Reduce Marketing Analytic NOSQL Optimization Predictive Analytic Python Real Time Processing Recommendation Engine Redis Reinforcement Learning Scala Spark Statistics Storm Text Analytic Time Series Analytic Uncategorized Web Analytic
- alarm flooding
- Analytic
- anomaly detection
- API
- big data
- bloat
- Cassandra
- Cassndra
- clustering
- Collaborative filter
- CRM
- customer churn
- customer conversion
- customer loyalty
- customer segmentation
- data lake
- data mining
- Data model
- data quality
- data transformation
- data validation
- decision tree
- eCommerce
- entropy
- ETL
- fraud
- generalization error
- Geo spatial index
- gini index
- Hadoop
- HBase
- HDFS
- high cardinality
- Hive
- Index
- IoT
- JSON
- Mapreduce
- map reduce
- marketing campaign
- markov chain
- mobile
- Mobile Advetisement
- model complexity
- MongoDB
- multi arm bandit
- nearest neighbor
- NOSQL
- outlier
- outlier detection
- Presence data
- programing language
- Query
- real time
- recommendation
- recommendation engine
- retail
- ruby
- Rule Engine
- scikit
- scikit-learn
- seasonality
- Secondary index
- Secondary sort
- similarity
- simulated annealing
- Solr
- spark
- stochastic optimization
- stream processing
- supply chain
- Visitor conversion
- Web click stream analysis
- Web log mining
- Workflow
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
Leave a comment
