Task description

One of the most valuable sources of customer information is bank transaction data. In this competition, participants are asked to answer the question: is it possible to predict the gender of a client using information about outcome transactions and income on a bank card? And if so, what is the accuracy of such a prediction?

File descriptions

transactions.csv – transactional data on banking operations.
train set.csv – training set with client gender marking (0/1 - client gender).
codes.csv – the table contains description of MCC transaction codes.
types.csv – table contains description of transactions.
test set.csv – data set with 2656 client id that is independent of the training data set, but that follows the same probability distribution.

Data fields

transactions.csv

client_id – client is id;
datetime – transaction date (format - ordered day number hh:mm:ss - 421 06:33:15);
code – MCC transaction code;
type – transaction type;
sum – sum of transactions: "+" — accrual of funds to the client (incoming transaction), "-" — write-off of funds (outcoming transaction);

train_set.csv

client_id – client is id;
target – client gender;

codes.csv

code – MCC transaction code;
code_description – MCC transaction code description;

types.csv

type – transaction type;
type_description – transaction type description;

test_set.csv

client_id – client is id;

Tables

Codes

@kaggle.nenriki_sberbank_gender_prediction.codes

10.4 KB
184 rows
2 columns


CREATE TABLE codes (
  "code" BIGINT,
  "code_description" VARCHAR
);

Test Set

@kaggle.nenriki_sberbank_gender_prediction.test_set

21.5 KB
2656 rows
3 columns


CREATE TABLE test_set (
  "cl" BIGINT,
  "ent" VARCHAR,
  "d" VARCHAR
);

Train Set

@kaggle.nenriki_sberbank_gender_prediction.train_set

46.76 KB
6000 rows
2 columns


CREATE TABLE train_set (
  "client_id" BIGINT,
  "target" BIGINT
);

Transactions

@kaggle.nenriki_sberbank_gender_prediction.transactions

2.04 MB
130039 rows
5 columns


CREATE TABLE transactions (
  "client_id" BIGINT,
  "datetime" VARCHAR,
  "code" BIGINT,
  "type" BIGINT,
  "sum" DOUBLE
);

Types

@kaggle.nenriki_sberbank_gender_prediction.types

6.89 KB
155 rows
2 columns


CREATE TABLE types (
  "type" BIGINT,
  "type_description" VARCHAR
);