In this project I go through the various steps needed to build a time series machine learning pipeline and generate a weekly revenue forecast. I carry out a more “traditional” exploratory time series analysis with TSstudio and create a number of predictors using the insight I gather. I then train and validate an array of machine learning models with the open source library H2O, and compare the models’ accuracy using performance metrics and actual vs predicted plots.
How I used RStudio, GitHub and Netlify to create and deploy your own webpage
Steps and considerations to run a successful statistical segmentation with K-means, Principal Components Analysis and Bootstrap Evaluation
This is the minimal coding necessary to assemble the various data feeds and sort out the likes of variables naming & new features creation plus some general housekeeping tasks
Refreshed my CV using the R pagedown package
I am using R tidymodels to create and execute a “tidy” modelling workflow to tackle a classification problem. My aim is to show how easy it is to fit a simple logistic regression in R’s glm and quickly switch to a cross-validated random forest using the ranger engine by changing only a few lines of code.
I use the popular K-Means clustering algorithm to segment customers based on their response to a series of marketing campaigns. Data includes sales promotion data for a fictional wine retailer with details of 32 promotions (including wine variety, minimum purchase quantity, percentage discount, and country of origin) and a list of 100 customers and the promotions they responded to
Third and final part of a Market Basket Analysis project in which I apply an Improved Collaborative Filter implementation to power a Shiny App Product Recommender
Second part of a Market Basket Analysis project in which I apply various machine learning algorithms for Product Recommendation and select the best performing model with the support of the recommenderlab package
First part of a Market Basket Analysis project in which I source, explore and format a complex dataset suitable for modelling with recommendation algorithms.