Dave Cote, M.Sc.
8+ years of experience in data science, business intelligence and delivering actionnable data-driven solutions that address business challenges. Numerous achievements in predictive modeling and Machine Learning with Python.
Currently Data Scientist in the insurance industry.
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Survival Analysis
Customer Churn
Project description: For new submissions and renewals, estimate the conditional time to event (churn) to help actuarial decisions.
1. Data
- Batch ETL pipeline to refresh customer features, events and durations.
2. Model
- Forcasting: Ensemble of CoxRegression, DeepLearning and Gradient Boosting models.
- Predict the survival function for each customer.
- Calculate the conditional time to event (Predicted Duration)
- Calculate the survival probabilities at critical times for each customer and compare them with the probability distribution of their respective segment to compute individual risk scores.