# Marketing Dataset — Data

来源：https://www.kaggle.com/competitions/data-marketing/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

<h2>File descriptions</h2>
<ul>
<li><strong>train.csv</strong> - the training set</li>
<li><strong>test.csv</strong> - the test set</li>
<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>
</ul>
<h2>Data fields</h2>
<h4>Input variables:</h4>
<p><br /># bank client data:</p>
<p>1 - age (numeric)</p>
<p>2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')</p>
<p>3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)</p>
<p>4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')</p>
<p>5 - default: has credit in default? (categorical: 'no','yes','unknown')</p>
<p>6 - housing: has housing loan? (categorical: 'no','yes','unknown')</p>
<p>7 - loan: has personal loan? (categorical: 'no','yes','unknown')</p>
<p># related with the last contact of the current campaign:</p>
<p>8 - contact: contact communication type (categorical: 'cellular','telephone')</p>
<p>9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')</p>
<p>10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')</p>
<p>11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.</p>
<p><br /># other attributes:</p>
<p>12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)</p>
<p>13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)</p>
<p>14 - previous: number of contacts performed before this campaign and for this client (numeric)</p>
<p>15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')</p>
<p><br /># social and economic context attributes</p>
<p>16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)</p>
<p>17 - cons.price.idx: consumer price index - monthly indicator (numeric)</p>
<p>18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)</p>
<p>19 - euribor3m: euribor 3 month rate - daily indicator (numeric)</p>
<p>20 - nr.employed: number of employees - quarterly indicator (numeric)</p>
<h4>Output variable (desired target):</h4>
<h6><br />21 - y - has the client subscribed a term deposit? (binary: 'yes','no')</h6>