Category Archives: Machine Learning

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

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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

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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

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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

Supervised Machine Learning Parameter Search and Tuning with Simulated Annealing

The most challenging phase in supervised Machine Learning pipeline is parameter tuning. There are many parameters, each with a range of values. The so called grid search is brute force approach that tries all possible combinations of values for the … Continue reading

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Auto Training and Parameter Tuning for a ScikitLearn based Model for Leads Conversion Prediction

This is a sequel to my last blog on CRM leads conversion prediction using Gradient Boosted Trees as implemented in ScikitLearn. The focus of this blog is automatic training and parameter tuning for the model. The implementation is available in … Continue reading

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Predicting CRM Lead Conversion with Gradient Boosting using ScikitLearn

Sales leads are are generally managed and nurtured in CRM systems. It will be nice if we could predict the likelihood of any lead converting to an actual deal. This could be very beneficial in many ways e.g. proactively  providing … Continue reading

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