-
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
- Bandits Know the Best Product Price
- Customer Conversion Prediction with Markov Chain Classifier
- Improving Elastic Search Query Result with Query Expansion using Topic Modeling
- Removing Duplicates from Order Data Using Spark
- Pluggable Rule Driven Data Validation with Spark
- Hive Plays Well with JSON
- Six Unsupervised Extractive Text Summarization Techniques Side by Side
Archives
- 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 Hive 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 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 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
- map reduce
- Mapreduce
- marketing campaign
- markov chain
- mobile
- Mobile Advetisement
- model complexity
- MongoDB
- Monitoring
- 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: Spark
Time Series Trend and Seasonality Component Decomposition with STL on Spark
You may be interested in decomposing a time series into level, trend, seasonality and remainder components to gain more insight into your time series. You may also be interested in decomposition to separate out the remainder component for anomaly detection. … Continue reading
Posted in Anomaly Detection, Big Data, Data Science, ETL, Spark, Time Series Analytic
Tagged seasonal cycle, STL, time series decomposition, trend
Leave a comment
Encoding High Cardinality Categorical Variables with Feature Hashing on Spark
Categorical variables are ubiquitous in data. They pose a serious problem in many Data Science analysis processes. For example, many supervised Machine Learning algorithms work only with numerical data. With high cardinality categorical variables, popular encoding solutions like One Hot … Continue reading
Posted in Big Data, Data Science, ETL, Scala, Spark
Tagged categorical feature, feature hashing, high cardinality
Leave a comment
Time Series Sequence Anomaly Detection with Markov Chain on Spark
There are many techniques for time series anomaly detection. In this post, the focus is on sequence based anomaly detection of time series data with Markov Chain. The technique will be elucidated with a use case involving data from a … Continue reading
Elastic Search or Solr Search Result Quality Evaluation with NCDG Metric on Spark
You have built an enterprise search engine with Elastic Search or Solr. You have tweaked all the knobs in the search engine to get the best possible quality for the search results. But how do you know how well your … Continue reading
Posted in Big Data, Data Science, elastic search, Log Analysis, Scala, Search Analytic, Solr, Spark
Tagged enterprise search, NCDG, relevance feedback, search performance
Leave a comment
Plugin Framework Based Data Transformation on Spark
Data transformation is one of the key components in most ETL process. It is well known, that in most data projects, more than 50% of the time in spent in data pre processing. In my earlier blog, a Hadoop based … Continue reading
Posted in Big Data, Data Science, ETL, Scala, Spark
Tagged data transformation, plugin framework
2 Comments
Normal Distribution Fitness Test with Chi Square on Spark
Many Machine Learning models is based on certain assumptions made about the data. For example, in ZScore based anomaly detection, it is assumed that the data has normal distribution. Your Machine Learning model will be as good as how those … Continue reading
Posted in Anomaly Detection, Big Data, Data Science, Spark, Statistics
Tagged chi square fitness, normal distribution fitness
Leave a comment
Time Series Seasonal Cycle Detection with Auto Correlation on Spark
There are may benefits of auto correlation analysis on time series data, as we will be alluding to in detail later. It allows us to gain important insights on the nature of the time series data. Cycle detection is one … Continue reading
Posted in Big Data, Correlation, Spark, Statistics, Time Series Analytic
Tagged auto correlation, cycle, seasonality
3 Comments