Project Introduction

Project : French Motor Claims
Author : Bhishan Poudel, Ph.D Physics
Goal : Implement Frequency modelling, Severity modelling and Pure Premium Modelling
Tools : pandas, scikit-learn, xgboost,pygam

References:

• https://www.kaggle.com/floser/french-motor-claims-datasets-fremtpl2freq
• https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html

Project Notebooks

Notebook	Rendered	Description	Author
a01_data_cleaning.ipynb	ipynb, rendered	ohe, kbin, logscaling	Bhishan Poudel
b01_freq_modelling.ipynb	ipynb, rendered	Poisson	Bhishan Poudel
b02_severity_modelling.ipynb	ipynb, rendered	Gamma	Bhishan Poudel
b03_pure_premium_modelling.ipynb	ipynb, rendered	Poisson*Gamma and Tweedie	Bhishan Poudel
b04_tweedie_vs_freqSev.ipynb	ipynb, rendered	comparison	Bhishan Poudel
b05_lorentz_curves_comparison.ipynb	ipynb, rendered	Lorentz Curve	Bhishan Poudel
c01_xgboost_tweedie.ipynb	ipynb, rendered	'objective':'reg:tweedie'	Bhishan Poudel
d01_gam_linear.ipynb	ipynb, rendered	n_splies=10, grid_search	Bhishan Poudel

Data

• openml french motor freq (Multiple features.)
• openml french motor severity (Two features Id policy and Claim Amount.)

Data Cleaning

Some of the features are chosen for modelling.

one hot encoding = ["VehBrand", "VehPower", "VehGas", "Region", "Area"]
kbins discretizer = ["VehAge", "DrivAge"]
log and scaling = ["Density"]
pass through =  ["BonusMalus"]

Results

Module	Distribution	y_train	sample_weight	train D2	test D2	train MAE	test MAE	train MSE	test MSE
sklearn	Frequency Modelling (Poisson Distribution)	df_train['Frequency']	df_train['Exposure']	0.051384	0.048138	0.232085	0.224547	4.738399	2.407906
sklearn	Severity Modelling (Gamma Distribution)	df_train.loc[mask_train, 'AvgClaimAmount']	df_train.loc[mask_train, 'ClaimNb']	-	3.638157e-03	-4.747382e-04	1.859814e+03	1.856312e+03	4.959565e+06
sklearn	Pure Premium Modelling (TweedieRegressor)	df_train['PurePremium']	df_train['Exposure']	2.018645e-02	1.353285e-02	6.580440e+02	4.927505e+02	1.478259e+09	1.622053e+08
xgboost	Xgboost Tweedie Regression	dtrain.set_base_margin(np.log(df_train['Exposure'].to_numpy())	dtest.set_base_margin(np.log(df_test['Exposure'].to_numpy()))	-	-	1.760538e+03	1.588351e+03	1.481952e+09	1.659363e+08
pygam	GAM Linear Model	df_train["AvgClaimAmount"].values	N/A	-	-	1.686438e+02	1.655408e+02	1.785332e+06	1.647533e+06