Adventures in data science
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An outline of our recent adventures in data science including some thoughts on how machine learning can help create digital back office services. Delivered to the 1st Touch user conference 28/09/2016.
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Adventures in data science
- 1. Brian MoranWherein our intrepid explorer provides an account of many curious encounters with data... 28 September 2016
- 2. Adapted from http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html Data Science Mathematics and Statistics Research Methods UNICORN Machine Learning Subject Matter Expertise Programming Skills Computer Science What Does it Involve?
- 3. Business Understanding Data Understanding Data PreparationModelling Evaluation Deployment What Does it Do? The CRISP data mining process
- 4. Check email Naive Bayes Internet search PageRank Watch Netflix Boltzman machine Buy lunch Artificial neural net Use Sat-Nav Dijkstra’s algorithm Apply for a loan Decision trees Shop on Amazon Matrix factorization Get a letter k-means clustering Diagrams (but not text) from: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/ Data Science Applications are Everywhere
- 10. Recommend alternative Alert Close 90% Chance of broken appointment: Better alternative: 2:00pm - 5:00pm
- 13. 1. Repair request Delay Staff check Staff input Repair ordered 2. Repair request Repair ordered 3. Repair request Machine Learning Staff check Staff input Repair ordered Repair orderedProblem? N Y Options for Back-office Repairs Process
- 14. Proof of concept 1: Natural language processing with Naive Bayes…
- 16. “Our bathroom is leaking through the ceiling onto the stairs and the ceiling is wet through along with the walls where the taps are mounted. And puddle on the stairs” PLUMBER Check web form Naive Bayes
- 17. Check web form Naive Bayes bathroom leaking ceiling stairsceiling wet walls taps stairs PLUMBER
- 20. -£300,000 -£225,000 -£150,000 -£75,000 £0 £75,000 £150,000 £225,000 £300,000 10% channel shift 20% channel shift 30% channel shift 40% channel shift 50% channel shift 60% channel shift 70% channel shift Netcost/benefitofrepairsself-service 90% accuracy 10% accuracy 50% accuracy ‘Friction Free’ Self Service: Avoiding Pyrrhic Victories
- 21. Proof of concept 2: Clustering with K-means…
- 22. CUSTOMISE SERVICES & INFORMATION Assign to cluster K-means clustering
- 28. Blocked means of escape Alert Close Blocked for one day 56 King Street Leigh WN7 4LJ
- 30. Audit file ready Advice Close Values beginning with Highlighted for attention 11 66
- 31. Proof of concept 3: Identifying unnatural numbers with Benford’s Law…
- 33. Find outliers Benford’s Law 1 2 3 4 5 6 7 8 9
- 36. 6’0” 5’0” 4’0” 3’0” 5’6” 4’6” 3’6” 6’0” 5’0” 4’0” 3’0” 5’6” 4’6” 3’6” I’m going to beat Benford’s Law I’m going to beat Benford’s Law I’m going to beat Benford’s Law I’m going to beat Benford’s Law
- 42. Proof of concept 4: Forecasting arrears with Holt-Winters…
- 43. Forecast time series data Holt-Winters Triple Exponential Smoothing
- 51. Optimal time to sell property Alert Close
- 53. Proof of concept 5: Tenancy survival with Kaplan-Meier…
- 54. Survival Analysis Kaplan-Meier Estimator S(t)=1−F(t) = Prob(T≥t)
- 56. Histogram of Tenancy Length (Current Tenants)
- 59. A Simple Model of Tenancy Lengths Tenancy Length (years)
- 60. Tenancy Length (years) A Simple Model of Tenancy Lengths
- 61. Tenancy Length (years) Unlikely to need component replacements Likely to need component replacements Even Simple Models can Lead to Interesting Thoughts
- 64. RECEPTIONCONTACT CENTRE Getting Technology isn't the Same as Really Getting it
- 66. Digital front of office Digital back of office+ Really Getting Channel Shift Choice