Predictive Analytics
What we do
We build ML pipelines that deliver forecasts stakeholders trust and act on. Our approach emphasizes model interpretability, probability calibration, and operational reliability across the full lifecycle from training through production monitoring. Our team has built predictive systems for sports outcome modeling, demand forecasting in logistics, churn prediction in SaaS, credit risk scoring in financial services, and clinical outcome prediction.
Time-Series Forecasting
Models that predict what happens next - demand volumes, revenue trajectories, capacity needs, pricing shifts, or any metric that moves through time. We work with Prophet, ARIMA, DeepAR, and Temporal Fusion Transformers, handling seasonality, holiday effects, promotional impacts, and external regressors to produce forecasts your operations team can plan around.
Classification & Scoring Models
Binary and multi-class prediction models for any decision your business needs to make at scale - churn risk, lead scoring, conversion likelihood, lifetime value estimation, content relevance, or outcome probability. We combine behavioural signals, usage patterns, and domain features into calibrated scores with practical thresholds your team can act on.
Governed & Regulated Models
Prediction models built for industries where transparency and compliance are non-negotiable - credit decisioning, fraud detection, clinical risk, insurance underwriting. We implement model governance frameworks, audit trails, adverse action explanations, and bias monitoring so your models satisfy regulatory requirements while maintaining predictive performance.
Anomaly Detection & Alerting
Real-time and batch systems that surface unusual patterns in operational data, financial transactions, sensor readings, or infrastructure metrics. We use isolation forests, autoencoders, and statistical process control methods calibrated to minimize false positives while catching genuine anomalies before they become incidents.
How we work together
Data Assessment & Feature Engineering
We audit your data sources for quality, completeness, temporal consistency, and predictive signal before committing to a modeling approach. We identify and engineer features using domain knowledge, assess data leakage risks, and establish train-validation-test splits that reflect real-world deployment conditions. Data preparation and feature engineering consistently determine prediction quality more than model architecture.
Model Development & Validation
Iterative model development using scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow depending on the problem characteristics. We benchmark multiple approaches against business-relevant metrics including cost-weighted accuracy, calibration plots, and decision-threshold analysis alongside standard measures like RMSE and AUC. Every model undergoes bias and fairness evaluation before deployment.
Production Deployment
Model serving infrastructure using MLflow, BentoML, or custom FastAPI services with monitoring for prediction drift, feature drift, and performance degradation. We implement automated retraining pipelines triggered by performance thresholds or scheduled intervals, with A/B testing frameworks that validate new models against production baselines before full rollout.
Stakeholder Integration
Dashboards built in Streamlit, Retool, or embedded BI tools that put predictions directly into the hands of the people who need them. We include confidence intervals, feature importance explanations via SHAP or LIME, and alerting thresholds so decision-makers understand what the model predicts, why, and how certain it is.
View Work MMA Betting Intelligence
Gradient boosting and deep learning ensemble across four data sources with profit-gated deployment delivering 10-20% ROI.
View Work NBA Betting Engine
Processing 3,000+ features with sub-minute refresh cycles to generate profitable NBA game predictions.
View Work NCAA Betting System
Predictive modeling across 350+ college basketball programs with tournament auto-scaling and real-time inference.
Read Article Feature engineering decides Machine Learning outcomes
Feature engineering shapes how models understand signals and it determines whether they perform well once deployed. This article explains why well structured features drive model accuracy and reliability in machine learning systems.
Read Article Hyperparameter optimization to maximize ML performance
Hyperparameter optimization can lift ML model accuracy by 2 to 10%. Learn which models benefit most, which tuning techniques to use, and when the compute cost isn't justified.