Name: Course Material: Bank Marketing
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Course Material: Bank Marketing

Context

This dataset is used in the second week of our AI course. Students use it to explore structured data, and process it for use with Keras, XGBoost, etc.

Content

The data is a balanced (and processed) version of the UCI Bank Marketing dataset.

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

We downsampled the data to create a 50% success rate. We also processed it to work better with different ML Approaches

Acknowledgements

The original dataset from UCI can be found here
See:
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

Inspiration

This dataset is used week 2 of our AI course. Feel free to explore the connected Kernels.

Tables

Balanced Bank

@kaggle.bletchley_bank_marketing.balanced_bank

170.87 kB
9,280 rows
22 columns

CREATE TABLE balanced_bank (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "age" BIGINT,
  "job" VARCHAR,
  "marital" VARCHAR,
  "education" VARCHAR,
  "default" VARCHAR,
  "housing" VARCHAR,
  "loan" VARCHAR,
  "contact" VARCHAR,
  "month" VARCHAR,
  "day_of_week" VARCHAR,
  "duration" BIGINT,
  "campaign" BIGINT,
  "pdays" BIGINT,
  "previous" BIGINT,
  "poutcome" VARCHAR,
  "emp_var_rate" DOUBLE,
  "cons_price_idx" DOUBLE,
  "cons_conf_idx" DOUBLE,
  "euribor3m" DOUBLE,
  "nr_employed" DOUBLE,
  "y" VARCHAR
);

Processed Bank

@kaggle.bletchley_bank_marketing.processed_bank

211.12 kB
9,280 rows
66 columns

CREATE TABLE processed_bank (
  "unnamed_0" BIGINT  -- Unnamed: 0,
  "campaign" DOUBLE,
  "pdays" DOUBLE,
  "previous" DOUBLE,
  "emp_var_rate" DOUBLE,
  "cons_price_idx" DOUBLE,
  "cons_conf_idx" DOUBLE,
  "euribor3m" DOUBLE,
  "nr_employed" DOUBLE,
  "y" BIGINT,
  "job_admin" BIGINT  -- Job Admin.,
  "job_blue_collar" BIGINT,
  "job_entrepreneur" BIGINT,
  "job_housemaid" BIGINT,
  "job_management" BIGINT,
  "job_retired" BIGINT,
  "job_self_employed" BIGINT,
  "job_services" BIGINT,
  "job_student" BIGINT,
  "job_technician" BIGINT,
  "job_unemployed" BIGINT,
  "job_unknown" BIGINT,
  "age_old" BIGINT,
  "age_mid" BIGINT,
  "age_young" BIGINT,
  "marital_divorced" BIGINT,
  "marital_married" BIGINT,
  "marital_single" BIGINT,
  "marital_unknown" BIGINT,
  "education_basic_4y" BIGINT,
  "education_basic_6y" BIGINT,
  "education_basic_9y" BIGINT,
  "education_high_school" BIGINT,
  "education_illiterate" BIGINT,
  "education_professional_course" BIGINT,
  "education_university_degree" BIGINT,
  "education_unknown" BIGINT,
  "default_no" BIGINT,
  "default_unknown" BIGINT,
  "housing_no" BIGINT,
  "housing_unknown" BIGINT,
  "housing_yes" BIGINT,
  "loan_no" BIGINT,
  "loan_unknown" BIGINT,
  "loan_yes" BIGINT,
  "contact_cellular" BIGINT,
  "contact_telephone" BIGINT,
  "month_apr" BIGINT,
  "month_aug" BIGINT,
  "month_dec" BIGINT,
  "month_jul" BIGINT,
  "month_jun" BIGINT,
  "month_mar" BIGINT,
  "month_may" BIGINT,
  "month_nov" BIGINT,
  "month_oct" BIGINT,
  "month_sep" BIGINT,
  "day_of_week_fri" BIGINT,
  "day_of_week_mon" BIGINT,
  "day_of_week_thu" BIGINT,
  "day_of_week_tue" BIGINT,
  "day_of_week_wed" BIGINT,
  "poutcome_failure" BIGINT,
  "poutcome_nonexistent" BIGINT,
  "poutcome_success" BIGINT,
  "contacted_before" BIGINT
);