#Propensity Modelling #Data Exploration #ML Interpretability #Model Selection #Optimisation #Machine Learning

Propensity Modelling - Using h2o and DALEX to Estimate Likelihood to Purchase a Financial Product - Abridged Version

In this day and age, a business that leverages data to understand the drivers of customers’ behaviour has a true competitive advantage. Organisations can dramatically improve their performance in the market by analysing customer level data in an effective way and focus their efforts towards those that are more likely to engage. One trialled and tested approach to tease this type of insight out of data is Propensity Modelling, which combines information such as a customers’ demographics (age, race, religion, gender, family size, ethnicity, income, education level), psycho-graphic (social class, lifestyle and personality characteristics), engagement (emails opened, emails clicked, searches on mobile app, webpage dwell time, etc. ...

#Clustering #t-SNE #Customer Segmentation #Machine Learning

Segmenting with Mixed Type Data - A Case Study Using K-Medoids on Subscription Data

With the new year, I started to look for new employment opportunities and even managed to land a handful of final stage interviews before it all grounded to a halt following the corona-virus pandemic. Invariably, as part of the selection process I was asked to analyse a set of data and compile a number of data driven-recommendations to present in my final meeting. In this post I retrace the steps I took for one of the take home analysis I was tasked with and revisit clustering, one of my favourite analytic methods. ...

#Propensity Modelling #Machine Learning #Optimisation

Propensity Modelling - Using h2o and DALEX to Estimate the Likelihood of Purchasing a Financial Product - Optimise Profit With the Expected Value Framework

In this day and age, a business that leverages data to understand the drivers of its customers’ behaviour has a true competitive advantage. Organisations can dramatically improve their performance in the market by analysing customer level data in an effective way and focus their efforts towards those that are more likely to engage. One trialled and tested approach to tease out this type of insight is Propensity Modelling, which combines information such as a customers’ demographics (age, race, religion, gender, family size, ethnicity, income, education level), psycho-graphic (social class, lifestyle and personality characteristics), engagement (emails opened, emails clicked, searches on mobile app, webpage dwell time, etc. ...

#Machine Learning #Propensity Modelling #ML Interpretability #Model Selection

Propensity Modelling - Using h2o and DALEX to Estimate the Likelihood of Purchasing a Financial Product - Estimate Several Models and Compare Their Performance Using a Model-agnostic Methodology

In this day and age, a business that leverages data to understand the drivers of its customers’ behaviour has a true competitive advantage. Organisations can dramatically improve their performance in the market by analysing customer level data in an effective way and focus their efforts towards those that are more likely to engage. One trialled and tested approach to tease out this type of insight is Propensity Modelling, which combines information such as a customers’ demographics (age, race, religion, gender, family size, ethnicity, income, education level), psycho-graphic (social class, lifestyle and personality characteristics), engagement (emails opened, emails clicked, searches on mobile app, webpage dwell time, etc. ...

#Machine Learning #Time Series #Forecasting #Data Exploration

Time Series Machine Learning Analysis and Demand Forecasting with H2O & TSstudio

Traditional approaches to time series analysis and forecasting, like Linear Regression, Holt-Winters Exponential Smoothing, ARMA/ARIMA/SARIMA and ARCH/GARCH, have been well-established for decades and find applications in fields as varied as business and finance (e.g. predict stock prices and analyse trends in financial markets), the energy sector (e.g. forecast electricity consumption) and academia (e.g. measure socio-political phenomena). In more recent times, the popularisation and wider availability of open source frameworks like Keras, TensorFlow and scikit-learn helped machine learning approaches like Random Forest, Extreme Gradient Boosting, Time Delay Neural Network and Recurrent Neural Network to gain momentum in time series applications. ...

#Machine Learning #Tidyverse #Classification

Modelling with Tidymodels and Parsnip - A Tidy Approach to a Classification Problem

Recently I have completed the online course Business Analysis With R focused on applied data and business science with R, which introduced me to a couple of new modelling concepts and approaches. One that especially captured my attention is parsnip and its attempt to implement a unified modelling and analysis interface (similar to python’s scikit-learn) to seamlessly access several modelling platforms in R. parsnip is the brainchild of RStudio’s Max Khun (of caret fame) and Davis Vaughan and forms part of tidymodels, a growing ensemble of tools to explore and iterate modelling tasks that shares a common philosophy (and a few libraries) with the tidyverse. ...