OUR END-CLIENT PROJECTS

CASE STUDY 1

 Parkinson's UK project

Objectives

  • To validate the Parkinson's disease phenotype research results by using a range of enhanced data science techniques

  • To understand how these techniques could be used to further Parkinson’s research with these cohorts

Deliverables achieved

  • Successfully validated previously identified Parkinson's diseases sub-types

  • Devised a disease phenotype mapping technique to monitor patient's morbidity status

Click on the video below to see 3D visualization of generated Parkinson's disease classes

 

CASE STUDY 2
TOUCHNOTE CUSTOMER VALUE PROJECT

Objectives

  • Understand customer spend and the frequency of purchases

  • Develop and implement Machine Learning model to predict customer value and purchase frequency

Deliverables Achieved

  • Developed model for forecasted customer value and forecasted frequency of purchase using a range of variables. These two models are used to categorise customers based on their financial importance

  • Model predictions were measured as accurate to within £1.30 of actual customer spend

  • Established an automated weekly process involving model training/learning, validation, prediction and writing the results to a database for business use

  • Built model front end in the form of RShiny app and Google Sheet to allow business users to review model performance and results

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RShiny App developed as pert of Touchnote customer lifetime value project

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CASE STUDY 4
CHURN FORECAST PROJECT

Objectives

  • Understand factors which lead to customer churn (customer stops buying your product/service)

  • Use Machine Learning to predict the probability of a customer “churning”

Deliverables Achieved

  • Built a model that predicts the likelihood of a customer leaving based on a range of input variables

  • Developed an end-to-end automated process to collect data, train and validate the machine learning model on a subset of the data, then apply it to the customer dataset. Finally, write the results to a database

  • Built a spreadsheet front end so users could review predictions and outputs

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CASE STUDY 5
PARKING EYE ROI-BY-SITE PROJECT

Objectives

  • Identify geo-urban features that correlate with revenue generated from fines

  • Develop and implement model that forecasts the number of fines issued using a post-code as one of model inputs

Deliverables Achieved

  • Built a model that predicts number of parking fines based geo-urban features

  • Used these features to generate a score for each postal area to help Parking Eye identify opportunities for new sites which would yield the best return on investment

  • Built Rshiny app for business users to review predictions and sort results, and Power BI dashboard to locate and visualise high opportunity areas

 

POWER BI DASHBOARD FOR CASE STUDY 4: PARKINGEYE PROJECT

Allows users to see the different features and metrics to aid decision making, with colour coded visuals to show relativity between different postal areas for various metrics, and performance of local vs the regional. Aims to facilitate the exploration of various geographical features using a postal area code search. Feel free to play around the dashboard yourself

Designed by Brainbox Data Science LTD & based on non-proprietary public data

 

CASE STUDY 6
NEWS UK – VARIOUS DATA SCIENCE PROJECTS

Deliverables Achieved

  • Web Traffic Index full model production – time series analysis to assess and predict web traffic to aid resource management

  • Article Sentiment analysis – used text analysis program to identify and prove a negative correlation between news article sentiment, and reader engagement

  • Built a Comment Toxicity model to classify and identify site user comments that breach a threshold of unpleasantness or obscenity

  • Built a neural network exploring the customer journey from free user to premium subscription

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CASE STUDY 7
PWC – DOCUMENT CLASSIFICATION

Objectives

  • Develop and implement Machine Learning model that classifies Documents

Deliverables Achieved

  • Developed and presented a range of solutions capable of extracting textual information from documents and predicting document category. For example based on document type, content, author