Dataset research report
Bank research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Bank The Bank dataset from the UCI ML repository. Potential clients are contacted by a bank during a second advertisement campaign. This datasets records the customer, the interaction with the AD campaign, and if they subscribed to a proposed bank plan or not. Configurations and tasks Configuration Task Description encoding Encoding dictionary showing original values of encoded features. subscription Binary classification Has the customer subscribed… See the full description on the dataset page: https://huggingface.co/datasets/mstz/bank.
Research Context
Bank: 500 rows by 13 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.
Data Profile
Chart Evidence
These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.
age vs education_level
age vs education_level, coloured by marital_status.
Open and export this chartCorrelation of numeric columns
Pearson correlation between numeric columns.
Open and export this chartage by marital_status
Spread of age across marital_status groups.
Open and export this chartFollow-Up Queries
Preview Rows
| # | ageinteger | jobtext | marital_statustext | education_levelinteger | has_defaultedboolean | account_balanceinteger | has_housing_loanboolean | has_personal_loanboolean |
|---|---|---|---|---|---|---|---|---|
| 1 | 58 | management | married | 3 | False | 2143 | True | False |
| 2 | 44 | technician | single | 2 | False | 29 | True | False |
| 3 | 33 | entrepreneur | married | 2 | False | 2 | True | True |
| 4 | 47 | blue-collar | married | 0 | False | 1506 | True | False |
| 5 | 33 | unknown | single | 0 | False | 1 | False | False |
| 6 | 35 | management | married | 3 | False | 231 | True | False |
Data Dictionary
- age numeric
- job text
- marital_status categorical
- education_level numeric
- has_defaulted bool
- account_balance numeric
- has_housing_loan bool
- has_personal_loan bool
- month_of_last_contact datetime
- number_of_calls_in_ad_campaign numeric
- days_since_last_contact_of_previous_campaign datetime
- number_of_calls_before_this_campaign numeric
- successful_subscription numeric
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.