Dataset: Insurance Recommendation

About this Dataset

Insurance Recommendation

Description

For insurance markets to work well, insurance companies need to be able to pool and spread risk across a broad customer base. This works best where the population to be insured is diverse and large. In Africa, formal insurance against risk has been hampered by lack of private sector companies offering insurance, with no way to diversify and pool risk across populations.

Understanding the varied insurance needs of a population, and matching them to appropriate products offered by insurance companies, makes insurance more effective and makes insurance companies more successful.

Evaluation

The error metric for this competition is the log loss. For every customer ID in the test set, for each product code, you must submit a prediction between 0 and 1 for likelihood that that customer has that product.

Data

In Train, there is a 1 in the relevant column for each product that a customer has. Test is similar, except that for each customer ONE product has been removed (the 1 replaced with a 0). Your goal is to build a model to predict the missing product.

Tables

Best Replace

@kaggle.mrmorj_insurance_recommendation.best_replace

5.52 KB
62 rows
5 columns


CREATE TABLE best_replace (
  "child_replace" VARCHAR,
  "father_replace" VARCHAR,
  "category" VARCHAR,
  "cv_value" DOUBLE,
  "growth" DOUBLE
);

Samplesubmission

@kaggle.mrmorj_insurance_recommendation.samplesubmission

1017.59 KB
210000 rows
2 columns


CREATE TABLE samplesubmission (
  "id_x_pcode" VARCHAR,
  "label" BIGINT
);

Test

@kaggle.mrmorj_insurance_recommendation.test

174.96 KB
10000 rows
29 columns


CREATE TABLE test (
  "id" VARCHAR,
  "join_date" TIMESTAMP,
  "sex" VARCHAR,
  "marital_status" VARCHAR,
  "birth_year" BIGINT,
  "branch_code" VARCHAR,
  "occupation_code" VARCHAR,
  "occupation_category_code" VARCHAR,
  "p5da" BIGINT,
  "ribp" BIGINT,
  "n_8nn1" BIGINT,
  "n_7pot" BIGINT,
  "n_66fj" BIGINT,
  "gysr" BIGINT,
  "sop4" BIGINT,
  "rvsz" BIGINT,
  "pyuq" BIGINT,
  "ljr9" BIGINT,
  "n2mw" BIGINT,
  "ahxo" BIGINT,
  "bstq" BIGINT,
  "fm3x" BIGINT,
  "k6qo" BIGINT,
  "qbol" BIGINT,
  "jwfn" BIGINT,
  "jz9d" BIGINT,
  "j9jw" BIGINT,
  "ghyx" BIGINT,
  "ecy3" BIGINT
);

Train

@kaggle.mrmorj_insurance_recommendation.train

481.05 KB
29132 rows
29 columns


CREATE TABLE train (
  "id" VARCHAR,
  "join_date" TIMESTAMP,
  "sex" VARCHAR,
  "marital_status" VARCHAR,
  "birth_year" BIGINT,
  "branch_code" VARCHAR,
  "occupation_code" VARCHAR,
  "occupation_category_code" VARCHAR,
  "p5da" BIGINT,
  "ribp" BIGINT,
  "n_8nn1" BIGINT,
  "n_7pot" BIGINT,
  "n_66fj" BIGINT,
  "gysr" BIGINT,
  "sop4" BIGINT,
  "rvsz" BIGINT,
  "pyuq" BIGINT,
  "ljr9" BIGINT,
  "n2mw" BIGINT,
  "ahxo" BIGINT,
  "bstq" BIGINT,
  "fm3x" BIGINT,
  "k6qo" BIGINT,
  "qbol" BIGINT,
  "jwfn" BIGINT,
  "jz9d" BIGINT,
  "j9jw" BIGINT,
  "ghyx" BIGINT,
  "ecy3" BIGINT
);