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Updated bank-marketing dataset
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@ -23,3 +23,4 @@ data-collection/*/preprocessed/*
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# markdown outputs
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*.html
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.Rproj.user
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43
README.md
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README.md
@ -3,7 +3,7 @@ Andrzej Wójtowicz
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Document generation date: 2016-06-23 11:44:00.
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Document generation date: 2016-07-13 13:45:45.
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@ -30,7 +30,7 @@ Document generation date: 2016-06-23 11:44:00.
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**Source data files**:
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* [bank-additional.zip](https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip)
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* [bank.zip](https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip)
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**Cite**:
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```nohighlight
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@ -40,24 +40,22 @@ S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of
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**Dataset**:
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```nohighlight
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'data.frame': 38227 obs. of 18 variables:
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$ age : int 56 57 37 40 56 45 59 24 25 25 ...
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$ job : Factor w/ 11 levels "admin","blue.collar",..: 4 8 8 1 8 8 1 10 8 8 ...
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$ marital : Factor w/ 3 levels "divorced","married",..: 2 2 2 2 2 2 2 3 3 3 ...
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$ education : Ord.factor w/ 6 levels "basic.4y"<"basic.6y"<..: 1 4 4 2 4 3 5 5 4 4 ...
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$ housing : Factor w/ 2 levels "no","yes": 1 1 2 1 1 1 1 2 2 2 ...
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$ loan : Factor w/ 2 levels "no","yes": 1 1 1 1 2 1 1 1 1 1 ...
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$ contact : Factor w/ 2 levels "cellular","telephone": 2 2 2 2 2 2 2 2 2 2 ...
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'data.frame': 43193 obs. of 16 variables:
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$ age : int 58 44 33 35 28 42 58 43 41 29 ...
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$ job : Factor w/ 11 levels "admin","blue.collar",..: 5 10 3 5 5 3 6 10 1 1 ...
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$ marital : Factor w/ 3 levels "divorced","married",..: 2 3 2 2 3 1 2 3 1 3 ...
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$ education: Ord.factor w/ 3 levels "primary"<"secondary"<..: 3 2 2 3 3 3 1 2 2 2 ...
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$ balance : int 2143 29 2 231 447 2 121 593 270 390 ...
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$ housing : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
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$ loan : Factor w/ 2 levels "no","yes": 1 1 2 1 2 1 1 1 1 1 ...
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$ contact : Factor w/ 3 levels "cellular","telephone",..: 3 3 3 3 3 3 3 3 3 3 ...
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$ day : int 5 5 5 5 5 5 5 5 5 5 ...
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$ month : Ord.factor w/ 12 levels "jan"<"feb"<"mar"<..: 5 5 5 5 5 5 5 5 5 5 ...
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$ day.of.week : Ord.factor w/ 5 levels "mon"<"tue"<"wed"<..: 1 1 1 1 1 1 1 1 1 1 ...
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$ campaign : int 1 1 1 1 1 1 1 1 1 1 ...
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$ pdays : int 999 999 999 999 999 999 999 999 999 999 ...
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$ pdays.bin: Factor w/ 2 levels "successful","never": 2 2 2 2 2 2 2 2 2 2 ...
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$ previous : int 0 0 0 0 0 0 0 0 0 0 ...
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$ poutcome : Factor w/ 3 levels "failure","nonexistent",..: 2 2 2 2 2 2 2 2 2 2 ...
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$ emp.var.rate : num 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ...
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$ cons.price.idx: num 94 94 94 94 94 ...
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$ cons.conf.idx : num -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 ...
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$ euribor3m : num 4.86 4.86 4.86 4.86 4.86 ...
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$ nr.employed : num 5191 5191 5191 5191 5191 ...
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$ poutcome : Factor w/ 4 levels "failure","other",..: 4 4 4 4 4 4 4 4 4 4 ...
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$ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
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```
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@ -66,17 +64,16 @@ S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of
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|Type | Frequency|
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|:--------------|---------:|
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|factor | 6|
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|integer | 3|
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|numeric | 5|
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|ordered factor | 3|
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|factor | 7|
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|integer | 6|
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|ordered factor | 2|
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**Class imbalance**:
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| class A | class B |
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|:-------:|:--------:|
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| 11 % | 89 % |
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| 4254 | 33973 |
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| 12 % | 88 % |
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| 5021 | 38172 |
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---
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@ -4,7 +4,7 @@ name: Bank Marketing
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info: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing
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urls:
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- https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip
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- https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip
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cite: >
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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
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@ -4,8 +4,8 @@ preprocessDataset = function()
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temp.dir = tempdir()
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zip.file = "bank-additional.zip"
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zip.dataset.path = "bank-additional/bank-additional-full.csv"
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zip.file = "bank.zip"
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zip.dataset.path = "bank-full.csv"
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flog.debug(paste("Unzipping", zip.file))
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@ -20,31 +20,25 @@ preprocessDataset = function()
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flog.debug("Preprocessing loaded dataset")
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dataset = dataset %>%
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select(-c(duration, pdays, default)) %>%
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select(-c(duration, default)) %>%
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filter(job != "unknown" & marital != "unknown" & education != "unknown" &
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education != "illiterate" & housing != "unknown" & loan != "unknown") %>%
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education != "unknown" & housing != "unknown" & loan != "unknown") %>%
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droplevels()
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#dataset.yes = dataset %>% filter(y == "yes")
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#dataset.no = dataset %>% filter(y == "no") %>% sample_n(nrow(dataset.yes))
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#
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#dataset = rbind(dataset.yes, dataset.no)
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dataset = dataset %>% mutate(
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education=factor(education, levels=c("basic.4y", "basic.6y",
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"basic.9y", "high.school",
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"professional.course",
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"university.degree"),
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dataset = dataset %>%
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mutate(
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education=factor(education, levels=c("primary", "secondary",
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"tertiary"),
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ordered=TRUE),
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month=factor(month, levels=c("jan", "feb", "mar",
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"apr", "may", "jun",
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"jul", "aug", "sep",
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"oct", "nov", "dec"),
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ordered=TRUE),
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day_of_week=factor(day_of_week, levels=c("mon", "tue", "wed",
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"thu", "fri"),
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ordered=TRUE)
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)
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pdays.bin=revalue(factor(pdays==-1),
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c("TRUE"="never", "FALSE"="successful")),
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pdays=as.integer(replace(pdays, pdays==-1, 999))) %>%
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select(age:pdays, pdays.bin, previous:y)
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return(dataset)
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}
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@ -3,6 +3,7 @@ rm(list=ls())
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source("config.R")
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source("utils.R")
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library(plyr)
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library(dplyr)
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library(foreign)
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library(XLConnect)
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