Author Archives: Pranab

About Pranab

I am Pranab Ghosh, a software professional in the San Francisco Bay area. I manipulate bits and bytes for the good of living beings and the planet. I have worked with myriad of technologies and platforms in various business domains for early stage startups, large corporations and anything in between. I am an active blogger and open source project owner. I am passionate about technology and green and sustainable living. My technical interest areas are Big Data, Distributed Processing, NOSQL databases, Machine Learning and Programming languages. I am fascinated by problems that don't have neat closed form solution.

Machine Learning Model Interpretation and Prescriptive Analytic with Lime

Machine learning model interpretablity is the degree to which a human can comprehend the reasons behind the prediction made by a model. Interpretablity may be required for various reasons e.g. meeting compliance requirements or gaining insight for high stakes situation … Continue reading

Posted in Data Science, Machine Learning, Python | Tagged , , | Leave a comment

Automated Machine Learning with Hyperopt and Scikitlearn without Writing Python Code

The most challenging part of building supervised machine learning model is optimization for algorithm selection, feature selection and algorithm specific hyper parameter value selection that yields the best performing model. Undertaking such a task manually is not feasible, unless the … Continue reading

Posted in Data Science, Machine Learning, Python, ScikitLearn, Supervised Learning | Tagged , , , | 1 Comment

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 , , , | Leave a comment

Missing Value Imputation with Restricted Boltzmann Machine Neural Network

Missing value is a common problem in many real world data set. There are various techniques for imputing missing values. We will use a kind of Neural Network called RBM for imputing missing values. Restricted Boltzmann Machine (RBM) are stochastic … Continue reading

Posted in Data Science, Deep Learning, ETL, Machine Learning, Python | Tagged , , , | 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 , , | 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

Posted in Anomaly Detection, Big Data, Data Science, Machine Learning, Outlier Detection, Scala, Spark | Tagged , , , , | 1 Comment

Six Unsupervised Extractive Text Summarization Techniques Side by Side

In text summarization, we create a summary of the original content that is coherent and captures the salient points in the original content. There are various important usages of text summarization. Something we face almost every day is the text … Continue reading

Posted in Data Science, NLP, Python, Text Analytic, Text Mining | Tagged , | Leave a comment