#Machine Learning #Time Series #Forecasting

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. ...

#Machine Learning #Data Product #Market Basket Analysis

Market Basket Analysis - Part 3 of 3: A Shiny Product Recommender with Improved Collaborative Filtering

My objective for this piece of work is to carry out a Market Basket Analysis as an end-to-end data science project. I have split the output into three parts, of which this is the THIRD and last, that I have organised as follows: In the first chapter, I will source, explore and format a complex dataset suitable for modelling with recommendation algorithms. For the second part, I will apply various machine learning algorithms for Product Recommendation and select the best performing model. ...

#Machine Learning #Market Basket Analysis

Market Basket Analysis - Part 2 of 3: Market Basket Analysis with recommenderlab

My objective for this piece of work is to carry out a Market Basket Analysis as an end-to-end data science project. I have split the output into three parts, of which this is the SECOND, that I have organised as follows: In the first chapter, I will source, explore and format a complex dataset suitable for modelling with recommendation algorithms. For the second part, I will apply various machine learning algorithms for Product Recommendation and select the best performing model. ...