Schedule of Actuarial Data Scientist Program: Fourth edition - Module 1 (16 CPD)

• Conditional mean estimation E[Y|X] and the iris problem.
• Introduction to Classification problems in machine learning: Linear Discriminant Analysis.
• Introduction to Regression Problem: Linear models and the OLS estimator (with mixed data types: e.g. mix of continuous and discrete data).

• Introducing the trainers and the training environment, including a first introduction to git and the GitHub repo’s dedicated to the training, the notebooks, data sets available, the ways to execute the Python code
• Introducing the data sets that will be analyzed in the course: MTPL claim frequency and severity data, Ames Housing data set, caravan insurance data set (for a classification problem with class imbalance), data set with characteristics of vehicles (for clustering).
• Getting to know these data sets: basic data explorations, some plotting, data manipulation and calculating summary statistics [Numpy, pandas and Matplotlib].
• Target and feature engineering steps, including (among others) centering, scaling, dealing with NAs, class imbalance, filter out near zero variance [scikit-learn: sklearn.preprocessing].
• Data splitting and resampling methods: training vs validation vs test, k-fold cross validation [scikit-learn: sklearn.model_selection].
• Introduction to parameter tuning, simple example with e.g. K-nearest-neighbour. [scikit-learn: sklearn.model_selection].

• the GLMs (Logistic, Poisson, Gamma)

• Introducing the Python package statsmodels via [statsmodels.api] and the support for formulas via [statsmodels.formula.api].
• Linear regression: describe the models, fit and summarize linear regression models on the Ames Housing data: model fit and model inspection, prediction, variable and model selection tools [statsmodels: statsmodels.regression.linear_model]
• Generalized linear regression models: fitting Poisson and gamma regression models on the MTPL data set: inspecting model fit, building predictions, evaluating model fit [statsmodels: statsmodels.genmod.generalized_linear_model].
• We gradually build up the Poisson GLM: introducing exposure (offset), how to handle multiple types of variables (numeric, categorical).
• Combine frequency and severity GLMs into a technical tariff. Construct technical prices for selected risk profiles.

Introduction of

• the LASSO
• Ridge
• ElasticNet
• Relation with Support Vector Machine

• Fitting regularized (G)LMs: basic set up with [statsmodels or pyglmnet], handling different types of covariates in the regularized fit, automatic feature selection.
• Working on a classification problem with class imbalance: the caravan insurance data set. Going from regression to classification problems: model formulation, model fit (with and without regularization), model evaluation tools (e.g. AUC).