Source: https://daphnislabs.com/services/predictive-analytics-machine-learning # Predictive analytics and machine learning Evaluate whether a model can improve a real choice, with the data, capacity and error trade-offs made visible. [Assess My Predictive Use Case](https://daphnislabs.com/contact?service=predictive-analytics-machine-learning) [Try the example](https://daphnislabs.com/services/predictive-analytics-machine-learning#worked-example) Waiting time Repeat contacts Issue history **Risk ranking** **Review queue** Capacity + cost of missed cases ## Tools & technologies - Python - scikit-learn - PyTorch - TensorFlow - SQL - Model evaluation and monitoring ## First, establish whether a model is worth using. ### A decision Name the action a prediction could change and the person who owns it. ### A baseline Compare the current rule or manual approach before adding model complexity. ### An outcome Check that historical labels reflect what will be known at the actual decision time. ## More cases reviewed. Fewer cases missed? Move the review threshold on a small fictional evaluation set and see the trade-off. Interactive example Support review threshold ### Set the review threshold Eight fictional historical cases with fixed ranking scores and known outcomes. No model runs here. Review scores at or above 0.65 0.30 0.90 **Sent to review** 4 **Escalations found** 2 **Escalations missed** 2 **Unneeded reviews** 2 Case / score Observed outcome - **T-01** 0.92 Escalated Review - **T-02** 0.80 Did not escalate Review - **T-03** 0.74 Escalated Review - **T-04** 0.65 Did not escalate Review - **T-05** 0.52 Escalated Not reviewed - **T-06** 0.30 Did not escalate Not reviewed - **T-07** 0.20 Escalated Not reviewed - **T-08** 0.10 Did not escalate Not reviewed 4 cases sent to review: 2 escalations found, 2 missed and 2 unneeded reviews. Fictional example. Nothing is sent or saved outside this page. ## A useful evaluation needs an operating plan. ### Before release Test against a held-out period, relevant groups and the agreed baseline. ### During use Track data changes, outcomes and review capacity alongside prediction quality. ### When it degrades Define a fallback and a decision owner before changing or retraining the model. ## Evidence and the means to reproduce it. - ### Decision & baseline definition - ### Dataset and feature assessment - ### Reproducible evaluation - ### Threshold & review policy - ### Model integration package - ### Monitoring and retraining plan [Need to establish the data foundation first?](https://daphnislabs.com/services/data-engineering) ## Customer data and AI analysis [View all case studies](https://daphnislabs.com/case-studies) ![PA — official Shopify app listing image](https://daphnislabs.com/images/projects/pa-product-collection-sort/listing-1.webp) ### [PA: Product & Collection Sort](https://daphnislabs.com/case-studies/pa-product-collection-sort) Sales signals, stock levels and merchant rules help decide which products shoppers encounter first. View project ![Z08 — the published analytics dashboard](https://daphnislabs.com/images/projects/z08-heatmap-and-recording/published-1-depth.webp) ### [Z08 Heatmap & Recording](https://daphnislabs.com/case-studies/z08-heatmap-and-recording) A Shopify app combining heatmaps, session recording and AI-assisted behavioural insight. View project 01 / 02 ## A few practical questions. ### Can you guarantee model accuracy? No. Feasibility depends on the data, outcome definition and changing conditions. Evaluation should compare an agreed baseline and report relevant errors, not promise a universal accuracy figure. ### How much historical data do we need? There is no useful universal minimum. We assess coverage, outcome labels, missing cases, leakage and how closely the history matches the intended use. ### Does a prediction automatically trigger action? Only if that is explicitly designed and accepted. A score can instead prioritise a review queue, with human decisions and outcomes recorded for evaluation. ### What happens when performance changes? We define monitoring, review ownership and fallback conditions. Retraining, threshold changes and release approval are governed by the agreed operating plan. ## What would a prediction change? Bring the decision, examples of past outcomes and the cost of a missed or unnecessary action. [Assess My Predictive Use Case](https://daphnislabs.com/contact?service=predictive-analytics-machine-learning) [Predictive Analytics Services](https://daphnislabs.com/services/predictive-analytics)