Category Archives: Python

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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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

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Synthetic Training Data Generation for Machine Learning Classification Problems using Ancestral Sampling

Access to good training data set is a serious impediment to building supervised Machine Learning models. Such data is scarce and when available, the quality of the data set may be questionable. Even if good quality data set is available, … Continue reading

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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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Improving Elastic Search Query Result with Query Expansion using Topic Modeling

Query expansion is a process of reformulating a query to improve query results and to be more specific to improve the recall for a query. Topic modeling is an Natural Language Processing (NLP) technique to discover hidden topics or concepts … Continue reading

Posted in elastic search, NLP, Python, Solr, Text Analytic, Text Mining, Topic Modeling | Tagged , , , | 1 Comment

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