This dataset represents the activity of a large number of customers who are interested in buying policies from Allianz. Each Each observation corresponds to a potential customer and the feature NEWBUS indicates whether the customer purchased a policy.
The new datasets is taking into account the fact that few factors were self explanatory and were then suppressed
The provided dataset is SAF2 and provide a rich representation of the prospective customer and policy. They include specific coverage information, personal information, property information, and geographic information. Your task is to predict NEWBUS for each Quote in the test set.
File descriptions
train.csv
- the training set
test.csv
- the test set
sample_submission.csv
- a sample submission file in the correct format
Data location
Data are available in the following folder : N:\Resources\Data Science\Kaggle\Conversion_NEW_VERSION
Data fields
UNIQUE_ID
- an anonymous id unique to a given customer
CLUSTER_[CTS/BLD]_
- geographical clusters of classifier per product per risk
QUOTE_PREMIUM
- the quote premium
START_DATE
- Inception date
DIST2
- Distribution channel
CONS_TYPE_DESC
- Construction type
ROOF_TYPE_DESC
- Roof type
OCC_TYPE
- Occupancy type
H_BUILDING_SUMINSURED
- Building sum insured
TOT_SUM_INS
- Total sum insured at policy level
YEAR_CONS
- Building year of construction
GEO_[...]
- Geographical census information
AGE_OWN
- Owner's age
ICA_ZONE
- Geographical zones of Australia used in weather modelling. Equivalent to Cresta zones
AFFORDABILITY
- ratio between premium and average income per geographical zone