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ONS Data Science Pathfinder Programme: Better Outcomes through Predictive Analytics

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ONS Data Science Pathfinder Programme: Better Outcomes through Predictive Analytics
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ONS Data Science Pathfinder Programme: Better Outcomes through Predictive Analytics

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  1. ONS Data Science Pathfinder Programme: Better Outcomes through Predictive Analytics Jamie Stainer, Department for the Economy (NI)

  2. Nice to meet you! Youth Training Statistics and Research Branch: • Part of Analytical Services Division • Division aims to foster and facilitate evidence based policy development and service delivery • Publish TfS/AppsNI statistical bulletins on biannual basis • Data Linkage, Data Science, & Research

  3. Agenda • What we wanted to do and why we wanted to do it • An overview of the Training for Success Programme and the aim of the project • What we did and what challenges we faced • Outcomes • Lessons learned and reflections

  4. What is the Data Science Pathfinder Programme?

  5. ONS Data Science Pathfinder Programme “…a capability-building programme which gives analysts from across the public sector the opportunity to develop their data science skills.”

  6. Why did we want to do it? Develop new skills in Modelling Learn best practice from experts Share knowledge with colleagues Identify ways we can benefit our customers Gain new insights from the data Long Term:

  7. Predicting success on the Training for Success programme

  8. What is Training for Success? • Training for Success (TfS) is a programme designed for young people aged 16-17, with extended age eligibility for young people with a disability up to age 22 and up to age 24 for those from an in-care background. • The programme aims to provide young people with the qualifications and skills they need to progress into an apprenticeship, further education, or employment. • It is delivered across 4 strands, with training delivered across the following areas: • Personal and social development • Employability skills • Professional and technical skills • Essential skills in Literacy, Numeracy, and ICT

  9. What was the aim of the project? Potential benefits: • Efficient resource allocation (getting support to those who need it most) • New insights to inform policy makers • Better outcomes for young people who have left school with low or no qualifications.

  10. What did we do?

  11. What did we do? • 12 week programme over Summer 2018 • Week 0 - Initial visit to Data Science Campus in Newport to scope project with ONS Mentor • Weekly phonecalls to discuss progress and emails to share information/learning • 2nd Newport visit in week 12 to get 1-on-1 coaching through the modelling phase. • Comprehensive technical notes and guidance provided by mentor. • Ideally, project time would all be spent at data science campus – travel and technology were significant blockers.

  12. Our Modelling Process • Understand what we want to achieve • Define success (or targets) 1. Scope • Understand • Audit (summary statistics, dimensions, missing data) • Identifying correlated fields 2. Data Audit • Aggregation / disaggregation, merging new datasets • New variable derivation • Dimensionality reduction 3. Data Munging • Training set and test set 4. Sampling • Select variables to include or exclude • Fewer attributes reduce complexity 5. Feature Selection • Stepwise logistic regression • Random forest • Adaptive Boosting 6. Modelling • Performance (e.g. accuracy from Confusion Matrix) • Variable Importance 7. Assessment Future: Deployment • Performance (e.g. accuracy from Confusion Matrix) • Variable Importance

  13. Scope • Tasks • What are we trying to achieve? (and what is achievable?) • What data do we have available? • What is our target variable? • Challenges • Internally driven rather than by customer • Multiple definitions of success

  14. Data Audit and Data Munging • Challenges • Dealing with missing data • Technical challenges with CHAID library in R • Interpretation of CHAID output • Most pairs of variables were not independent of one another • Audit • Understand the data • Calculate summary statistics (dimensions, missing data, variable types) • Identify correlated / non-independent fields • Munging • Dimensionality reduction (removing near zero variance variables, linear combinations) • Reducing number of categories in categorical variables (using CHAID) • Merging other data sets

  15. Sampling Tasks Balance the outcomes (via down-sampling) Split data into training and test sets Why? Classification algorithms can misclassify minority class more frequently if there is a large difference in proportions Test set is used for validation purposes to ensure your model is not over fit (i.e works brilliantly on the data it has seen, but would be useless with unseen data) ?

  16. Feature Selection, Modelling and Assessment

  17. Feature Selection, Modelling and Assessment • Leave variables as they are • Likely to have highest accuracy on training set • Many inputs; likely to lead to overfitting and unnecessary computational resource • Data driven • Using direct output from CHAID groupings as is • Could be hard to explain • Domain Driven • Using domain knowledge to reduce numbers of categories • Most explainable but sacrificing accuracy

  18. Feature Selection, Modelling and Assessment Once features have been selected, transform categorical variables using one-hot encoding. This is not necessary for all algorithms, but some cannot handle categorical variables. Start Modelling! E.g. One-hot encode

  19. Feature Selection, Modelling and Assessment We used 3 different algorithms: Logistic regression (and stepwise logistic regression) Random forests Adaptive boosting We built many models with each algorithm, tweaking parameters and including/excluding variables as we went along to try to improve performance as we went along. Criteria for choosing the best model: Explainability Complexity Accuracy

  20. Feature Selection, Modelling and Assessment • Example of the relationship between number of model features and model performance

  21. Feature Selection, Modelling and Assessment • Logistic regression (and stepwise logistic regression) • model_stepwise <- glm(ANY_QUAL_ACHIEVED ~ . , • family = binomial, • data = df.dmy_train) %>% • stepAIC(direction = “both”, trace = FALSE) Random Forests • rf_model <- randomForest(ANY_QUAL_ACHIEVED ~ . , • data = df.dmy_train, • importance = TRUE, • mtry = 4) Adaptive Boosting • model_adaboost <- ada(ANY_QUAL_ACHIEVED ~., • data = df.dmy_train)

  22. Feature Selection, Modelling and Assessment

  23. Feature Selection, Modelling and Assessment The model is used on the test data set, and the Accuracy is calculated using a Confusion Matrix. This metric was used as it is comparable over different algorithms (there is no AIC in a Random Forest) Accuracy

  24. Outcomes

  25. Outcomes Due to the limitations of the dataset (size, variable quality etc.) there were negligible difference between the classic Logistic Regression and the Machine Learning models used. However, we did see the best* results from the Adaptive Boosting Model * Lowest number of inputs with least accuracy sacrifice. Despite classifying this as the “best”, the differences in accuracy for most of the models were not huge. “Art not science”

  26. Outcomes • Important variables from Adaboost Model: start month, rejoins, provider, deprivation, disability • Weakness of Adaboost model – can’t see the detailed impact of each variable on final model – positive/negative – some may cancel each other out • Can look at the original data and make some assumptions about the likely impact of each variable – room for error 10 most important variables for final Adaboost model

  27. Outcomes 10 Variables with largest (absolute) regression coefficients • Important variables from Regression model: qualification, strand, supplier, provider • From baseline model, can see if impact is positive or negative • Easy to interpret & explain to customers • Other info may be lost Qualification A Strand Provider Supplier 1 Qualification B Supplier 2 Supplier 3 Qualification C Supplier 4 Supplier 5

  28. Future Deployment • Main aim of the project was to develop the team’s skills and knowledge of the modelling process • However the models built do provide some valuable insight into the factors influencing success on TfS programmes - e.g. start month – programme is designed for flexibility • Potential for future modelling with additional data – e.g. quality measures, attendance records – to measure ‘churn’ & ‘retention’ • Potential to ‘risk score’ in future – to identify individuals who would benefit from targeted support/ intervention – leading to better success rates and better outcomes for individuals

  29. Lessons Learnt

  30. Lessons Learnt • ‘An Art not a Science’ • Research questions should be customer led rather than driven by statisticians • Have a clear definition of ‘success’ at the outset of modelling process • Data acquisition and data munging will take the longest time, be prepared to go back to the start several times as new issues emerge • Data quality is always an issue: importance of domain specific/expert knowledge – and buy-in from customers – to understand the data fully • Unexplainable factors – individual qualities that will not be captured in data.

  31. Reflections on working with ONS

  32. Reflections on working with ONS • A really valuable experience, with knowledgeable, professional mentorship from ONS • IT infrastructure was a significant blocker • Would have benefited from more face to face time rather than phone calls – perhaps an intensive course over a week based entirely at the Data Science campus would be better in future • Scheduling over the summer holidays was difficult • Ability to share data with ONS mentor would have been useful • Team gained valuable knowledge and experience, and significantly developed their data science and modelling skills to take on additional projects in the future.

  33. Any Questions?