银行产品推荐竞赛数据【Kaggle竞赛】
| 
179 | 
0 | 
0
Data Description:
In this competition, you are provided with 1.5 years of customers behavior data from Santander bank to predict what new products customers will purchase. The data starts at 2015-01-28 and has monthly records of products a customer has, such as "credit card", "savings account", etc. You will predict what additional products a customer will get in the last month, 2016-06-28, in addition to what they already have at 2016-05-28. These products are the columns named: ind_(xyz)_ult1, which are the columns #25 - #48 in the training data. You will predict what a customer will buy in addition to what they already had at 2016-05-28.
The test and train sets are split by time, and public and private leaderboard sets are split randomly.
Please note: This sample does not include any real Santander Spain customers, and thus it is not representative of Spain's customer base.
train.csv - the training set
test.csv - the test set
sample_submission.csv - a sample submission file in the correct format
| Column Name | Description |
|---|---|
| fecha_dato | The table is partitioned for this column |
| ncodpers | Customer code |
| ind_empleado | Employee index: A active, B ex employed, F filial, N not employee, P pasive |
| pais_residencia | Customer's Country residence |
| sexo | Customer's sex |
| age | Age |
| fecha_alta | The date in which the customer became as the first holder of a contract in the bank |
| ind_nuevo | New customer Index. 1 if the customer registered in the last 6 months. |
| antiguedad | Customer seniority (in months) |
| indrel | 1 (First/Primary), 99 (Primary customer during the month but not at the end of the month) |
| ult_fec_cli_1t | Last date as primary customer (if he isn't at the end of the month) |
| indrel_1mes | Customer type at the beginning of the month ,1 (First/Primary customer), 2 (co-owner ),P (Potential),3 (former primary), 4(former co-owner) |
| tiprel_1mes | Customer relation type at the beginning of the month, A (active), I (inactive), P (former customer),R (Potential) |
| indresi | Residence index (S (Yes) or N (No) if the residence country is the same than the bank country) |
| indext | Foreigner index (S (Yes) or N (No) if the customer's birth country is different than the bank country) |
| conyuemp | Spouse index. 1 if the customer is spouse of an employee |
| canal_entrada | channel used by the customer to join |
| indfall | Deceased index. N/S |
| tipodom | Addres type. 1, primary address |
| cod_prov | Province code (customer's address) |
| nomprov | Province name |
| ind_actividad_cliente | Activity index (1, active customer; 0, inactive customer) |
| renta | Gross income of the household |
| segmento | segmentation: 01 - VIP, 02 - Individuals 03 - college graduated |
| ind_ahor_fin_ult1 | Saving Account |
| ind_aval_fin_ult1 | Guarantees |
| ind_cco_fin_ult1 | Current Accounts |
| ind_cder_fin_ult1 | Derivada Account |
| ind_cno_fin_ult1 | Payroll Account |
| ind_ctju_fin_ult1 | Junior Account |
| ind_ctma_fin_ult1 | Más particular Account |
| ind_ctop_fin_ult1 | particular Account |
| ind_ctpp_fin_ult1 | particular Plus Account |
| ind_deco_fin_ult1 | Short-term deposits |
| ind_deme_fin_ult1 | Medium-term deposits |
| ind_dela_fin_ult1 | Long-term deposits |
| ind_ecue_fin_ult1 | e-account |
| ind_fond_fin_ult1 | Funds |
| ind_hip_fin_ult1 | Mortgage |
| ind_plan_fin_ult1 | Pensions |
| ind_pres_fin_ult1 | Loans |
| ind_reca_fin_ult1 | Taxes |
| ind_tjcr_fin_ult1 | Credit Card |
| ind_valo_fin_ult1 | Securities |
| ind_viv_fin_ult1 | Home Account |
| ind_nomina_ult1 | Payroll |
| ind_nom_pens_ult1 | Pensions |
| ind_recibo_ult1 | Direct Debit |
还没有评论，说两句吧！
热门资源
GRAZ 图像分类数据
GRAZ 图像分类数据
凶杀案报告数据
凶杀案报告数据
猫和狗图像分类数...
Kaggle 上的竞赛数据，用以区分猫和狗两类对象，...
MIT Cars 汽车图像...
MIT Cars 汽车图像数据
Bosch 流水线降低...
数据来自产品在Bosch真实生产线上制造过程中的设备...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
