Category Archives: Machine Learning

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

Posted in Data Science, Machine Learning, Optimization, Python, ScikitLearn | Tagged , , , | 7 Comments

Data Normalization with Spark

Data normalization is a required data preparation step for many Machine Learning algorithms. These algorithms are sensitive to the relative values of the feature attributes. Data normalization is the process of bringing all the attribute values within some desired range. Unless … Continue reading

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Predicting Call Hangup in Customer Service Calls with Decision Tree and Random Forest

When customers hangup after a long wait in a call, it’s money wasted for the company. Moreover, it leaves the customer with a poor experience. It would have been nice, if we could predict in real time while the customer … Continue reading

Posted in Big Data, Customer Service, Hadoop and Map Reduce, Machine Learning, Predictive Analytic | Tagged , , | 3 Comments