Predictive analysis with machine learning
We transform your historical data into operational forecasts. We build supervised and unsupervised machine learning models to anticipate demand, reduce churn, identify anomalies, and optimize business decisions with a measurable confidence level.
Machine learning models to anticipate market trends, user behavior, and reduce operational risks.
Use cases
- Sales forecasting and retail stock management
- Churn prediction for telco and SaaS
- Industrial predictive maintenance
- Anomaly detection on financial transactions
- Credit scoring and anti-fraud
Measurable benefits
- 30-50% reduction in forecasting errors
- Identification of churners before they cancel
- Measurable data-driven decisions
- Traceable ROI for every model in production
Technical details
Models and algorithms
- Time series: ARIMA, Prophet, NeuralProphet
- Gradient boosting: XGBoost, LightGBM, CatBoost
- Deep learning: LSTM, Transformer
- Clustering and PCA for segmentation
Data pipelines
- Automated feature engineering
- Dataset versioning with DVC
- Validation with temporal cross-validation
- A/B testing of models in production
Deploy and monitoring
- Real-time or batch model serving
- Automatic drift detection
- Scheduled retraining
- Explainability with SHAP/LIME
Visualization
- Custom dashboards with React + D3
- Power BI, Tableau, Looker integration
- Email/Slack alerts on thresholds
- Executive reporting PDF export
FAQ
How much data is needed for a predictive model?
It depends on the problem. For sales forecasting, at least 18-24 months of history are needed. For classification, even a few thousand labeled examples.
Are the models explainable?
Yes. We use explainable AI techniques (SHAP, LIME) to show which variables influence the prediction — essential in regulated environments.
How do you handle data quality?
We build data quality pipelines with automated validation, anomaly alerting, and dataset versioning.