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porto-seguro

porto-seguro

in_preparation ARFF Publicly available Visibility: public Uploaded 06-11-2018 by Florian Pargent
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Training dataset of the 'Porto Seguros Safe Driver Prediction' Kaggle challenge [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction]. The goal was to predict whether a driver will file an insurance claim next year. The official rules of the challenge explicitely state that the data may be used for 'academic research and education, and other non-commercial purposes' [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/rules]. For a description of all variables checkout the Kaggle dataset repository [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/data]. It states that numeric features with integer values that do not contain 'bin' or 'cat' in their variable names are in fact ordinal features which could be treated as ordinal factors in R. For further information on effective preprocessing and feature engineering checkout the 'Kernels' section of the Kaggle challenge website [https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/kernels]. Note that many Kagglers removed all 'calc' variables as they do not seem to carry much information.

58 features

target (target)nominal2 unique values
0 missing
id (row identifier)numeric595212 unique values
0 missing
ps_ind_01numeric8 unique values
0 missing
ps_ind_02_catnominal4 unique values
216 missing
ps_ind_03numeric12 unique values
0 missing
ps_ind_04_catnominal2 unique values
83 missing
ps_ind_05_catnominal7 unique values
5809 missing
ps_ind_06_binnominal2 unique values
0 missing
ps_ind_07_binnominal2 unique values
0 missing
ps_ind_08_binnominal2 unique values
0 missing
ps_ind_09_binnominal2 unique values
0 missing
ps_ind_10_binnominal2 unique values
0 missing
ps_ind_11_binnominal2 unique values
0 missing
ps_ind_12_binnominal2 unique values
0 missing
ps_ind_13_binnominal2 unique values
0 missing
ps_ind_14numeric5 unique values
0 missing
ps_ind_15numeric14 unique values
0 missing
ps_ind_16_binnominal2 unique values
0 missing
ps_ind_17_binnominal2 unique values
0 missing
ps_ind_18_binnominal2 unique values
0 missing
ps_reg_01numeric10 unique values
0 missing
ps_reg_02numeric19 unique values
0 missing
ps_reg_03numeric5012 unique values
107772 missing
ps_car_01_catnominal12 unique values
107 missing
ps_car_02_catnominal2 unique values
5 missing
ps_car_03_catnominal2 unique values
411231 missing
ps_car_04_catnominal10 unique values
0 missing
ps_car_05_catnominal2 unique values
266551 missing
ps_car_06_catnominal18 unique values
0 missing
ps_car_07_catnominal2 unique values
11489 missing
ps_car_08_catnominal2 unique values
0 missing
ps_car_09_catnominal5 unique values
569 missing
ps_car_10_catnominal3 unique values
0 missing
ps_car_11_catnominal104 unique values
0 missing
ps_car_11numeric4 unique values
5 missing
ps_car_12numeric183 unique values
1 missing
ps_car_13numeric70482 unique values
0 missing
ps_car_14numeric849 unique values
42620 missing
ps_car_15numeric15 unique values
0 missing
ps_calc_01numeric10 unique values
0 missing
ps_calc_02numeric10 unique values
0 missing
ps_calc_03numeric10 unique values
0 missing
ps_calc_04numeric6 unique values
0 missing
ps_calc_05numeric7 unique values
0 missing
ps_calc_06numeric11 unique values
0 missing
ps_calc_07numeric10 unique values
0 missing
ps_calc_08numeric11 unique values
0 missing
ps_calc_09numeric8 unique values
0 missing
ps_calc_10numeric26 unique values
0 missing
ps_calc_11numeric20 unique values
0 missing
ps_calc_12numeric11 unique values
0 missing
ps_calc_13numeric14 unique values
0 missing
ps_calc_14numeric24 unique values
0 missing
ps_calc_15_binnominal2 unique values
0 missing
ps_calc_16_binnominal2 unique values
0 missing
ps_calc_17_binnominal2 unique values
0 missing
ps_calc_18_binnominal2 unique values
0 missing
ps_calc_19_binnominal2 unique values
0 missing
ps_calc_20_binnominal2 unique values
0 missing

62 properties

595212
Number of instances (rows) of the dataset.
58
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
846458
Number of missing values in the dataset.
470281
Number of instances with at least one value missing.
26
Number of numeric attributes.
32
Number of nominal attributes.
4.68
Third quartile of means among attributes of the numeric type.
0
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
44.83
Percentage of numeric attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
104
The maximum number of distinct values among attributes of the nominal type.
-2.22
Minimum skewness among attributes of the numeric type.
55.17
Percentage of nominal attributes.
0.86
Third quartile of skewness among attributes of the numeric type.
12.21
Maximum skewness among attributes of the numeric type.
0.05
Minimum standard deviation of attributes of the numeric type.
0.54
First quartile of entropy among attributes.
1.77
Third quartile of standard deviation of attributes of the numeric type.
3.55
Maximum standard deviation of attributes of the numeric type.
3.64
Percentage of instances belonging to the least frequent class.
-0.47
First quartile of kurtosis among attributes of the numeric type.
18.13
Standard deviation of the number of distinct values among attributes of the nominal type.
0.98
Average entropy of the attributes.
21694
Number of instances belonging to the least frequent class.
0.45
First quartile of means among attributes of the numeric type.
7.53
Mean kurtosis among attributes of the numeric type.
24
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
2.93
Mean of means among attributes of the numeric type.
-0.07
First quartile of skewness among attributes of the numeric type.
0.93
Average class difference between consecutive instances.
0
Average mutual information between the nominal attributes and the target attribute.
0.29
First quartile of standard deviation of attributes of the numeric type.
0.23
Entropy of the target attribute values.
2652.93
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.71
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
6.59
Average number of distinct values among the attributes of the nominal type.
0.04
Second quartile (Median) of kurtosis among attributes of the numeric type.
609.92
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.68
Mean skewness among attributes of the numeric type.
2.12
Second quartile (Median) of means among attributes of the numeric type.
96.36
Percentage of instances belonging to the most frequent class.
1.18
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
573518
Number of instances belonging to the most frequent class.
0
Minimal entropy among attributes.
0.34
Second quartile (Median) of skewness among attributes of the numeric type.
6.01
Maximum entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
41.38
Percentage of binary attributes.
1.13
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.98
Third quartile of entropy among attributes.
180.7
Maximum kurtosis among attributes of the numeric type.
0.01
Minimum of means among attributes of the numeric type.
79.01
Percentage of instances having missing values.
0.96
Third quartile of kurtosis among attributes of the numeric type.
9.23
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2.45
Percentage of missing values.

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