kokul/bank-marketing-propensity
Introduction This project explores several classification techniques as applied to a bank's marketing campaign data. The classification goal is to predict whether the client will subscribe a term deposit (variable y). Source: https://archive.ics.uci.edu/ml/datasets/bank+marketing It's recommended that the viewer read the Jupyter Notebook in NBViewer: https://nbviewer.jupyter.org/github/sgus1318/marketing_propensity/blob/master/Bank_DirectMarketing_Propensity.ipynb… See the full description on the dataset page: https://huggingface.co/datasets/kokul/bank-marketing-propensity.
Introduction
This project explores several classification techniques as applied to a bank's marketing campaign data. The classification goal is to predict whether the client will subscribe a term deposit (variable y). <br> Source: https://archive.ics.uci.edu/ml/datasets/bank+marketing <br> <br> It's recommended that the viewer read the Jupyter Notebook in NBViewer: https://nbviewer.jupyter.org/github/sgus1318/marketingpropensity/blob/master/BankDirectMarketing_Propensity.ipynb <br><br>
Data Dictionary:
Bank client data:
1 - age (numeric) <br> 2 - job : type of job (categorical: 'admin.', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown') <br> 3 - marital : marital status (categorical: 'divorced', 'married', 'single', 'unknown'; note: 'divorced' means divorced or widowed) <br> 4 - education (categorical: 'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown') <br> 5 - default: has credit in default? (categorical: 'no','yes','unknown') <br> 6 - housing: has housing loan? (categorical: 'no','yes','unknown') <br> 7 - loan: has personal loan? (categorical: 'no','yes','unknown') <br>
Campaign Data:
8 - contact: contact communication type (categorical: 'cellular','telephone') <br> 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') <br> 10 - dayofweek: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') <br> 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. <br>
Other:
12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) <br> 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) <br> 14 - previous: number of contacts performed before this campaign and for this client (numeric) <br> 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') <br>
Macroeconomic variables:
16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) <br> 17 - cons.price.idx: consumer price index - monthly indicator (numeric) <br> 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) <br> 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) <br> 20 - nr.employed: number of employees - quarterly indicator (numeric) <br>
Created Variables: <br>
21 - prevcampaigncontact - whether or not an individual was contacted during a previous marketing campaign <br> 22 - prevcall - whether or no an individual was contacted already during this campaign <br> 23 - white collar - whether an individual's occupation falls into the category 'entrepreneaur, management, or admin" <br> 24 - agesq - individual's age squared <br> 25 - agesqrt - square root of the individual's age <br> 26 - ageln - the natural log of the individual's age <br> 27 - agesq - individual's age squared <br> 28 - emp.var.ratesq - employment variation rate squared <br> 29 - emp.var.ratesqroot - square root of employment variation rate <br> 30 - emp.var.raterecip - reciprocal of employment variation rate <br> 31 - emp.var.rateln - natural log of employment variation rate <br> 32 - cons.price.idxsq - consumer price index squared <br> 33 - cons.price.idxsqroot - square root of consumer price index <br> 34 - cons.price.idxrecip - reciprocal of consumer price index <br> 35 - cons.price.idxln - natural log of consumer price index <br> 36 - cons.conf.idxsq - consumer confidence index squared <br> 37 - cons.conf.idxsqroot - square root of consumer confidence index <br> 38 - cons.conf.idxrecip - reciprocal of consumer confidence index <br> 39 - cons.conf.idxln - natural log of consumer confidence index <br> 40 - euribor3msq - euribor 3 month rate squared <br> 41 - euribor3msqroot - square root of euribor 3 month rate <br> 42 - euribor3mrecip - reciprocal of euribor 3 month rate <br> 43 - euribor3mln - natural log of euribor 3 month rate <br> 44 - nr.employedsq - number of employees squared <br> 45 - nr.employedsqroot - square root of number of employees <br> 46 - nr.employedrecip - reciprocal of number of employees <br> 47 - nr.employed_ln - natural log of number of employees <br>
Output variable (target):
Made_Deposit - has the client subscribed a term deposit? (binary: 1:yes, 0:no) <br>