Assignment1 D622
Jose Fuentes
2025-03-02
Assignment 1
This assignment focuses on one of the most important aspects of data science, Exploratory Data Analysis (EDA). Many surveys show that data scientists spend 60-80% of their time on data preparation. EDA allows you to identify data gaps & data imbalances, improve data quality, create better features and gain a deep understanding of your data before doing model training - and that ultimately helps train better models. In machine learning, there is a saying - “better data beats better algorithms” - meaning that it is more productive to spend time improving data quality than improving the code to train the model.
This will be an exploratory exercise, so feel free to show errors and warnings that arise during the analysis. Test the code with both datasets selected and compare the results.
Dataset
A Portuguese bank conducted a marketing campaign (phone calls) to predict if a client will subscribe to a term deposit The records of their efforts are available in the form of a dataset. The objective here is to apply machine learning techniques to analyze the dataset and figure out most effective tactics that will help the bank in next campaign to persuade more customers to subscribe to the bank’s term deposit. Download the Bank Marketing Dataset from: https://archive.ics.uci.edu/dataset/222/bank+marketing
# Load required libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.2## Warning: package 'ggplot2' was built under R version 4.4.2## Warning: package 'tidyr' was built under R version 4.4.2## Warning: package 'readr' was built under R version 4.4.2## Warning: package 'dplyr' was built under R version 4.4.2## Warning: package 'stringr' was built under R version 4.4.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(corrplot)## Warning: package 'corrplot' was built under R version 4.4.2## corrplot 0.95 loadedlibrary(caret)## Warning: package 'caret' was built under R version 4.4.2## Loading required package: lattice
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## Attaching package: 'caret'
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## The following object is masked from 'package:purrr':
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## liftlibrary(GGally)## Warning: package 'GGally' was built under R version 4.4.2## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(ggplot2)
# Load the dataset (adjust the path to your local file)
bank_data <- read.csv("C:/Users/Dell/Downloads/bank-full.csv", sep = ";")
# View the structure of the dataset
str(bank_data)## 'data.frame': 45211 obs. of 17 variables:
## $ age : int 58 44 33 47 33 35 28 42 58 43 ...
## $ job : chr "management" "technician" "entrepreneur" "blue-collar" ...
## $ marital : chr "married" "single" "married" "married" ...
## $ education: chr "tertiary" "secondary" "secondary" "unknown" ...
## $ default : chr "no" "no" "no" "no" ...
## $ balance : int 2143 29 2 1506 1 231 447 2 121 593 ...
## $ housing : chr "yes" "yes" "yes" "yes" ...
## $ loan : chr "no" "no" "yes" "no" ...
## $ contact : chr "unknown" "unknown" "unknown" "unknown" ...
## $ day : int 5 5 5 5 5 5 5 5 5 5 ...
## $ month : chr "may" "may" "may" "may" ...
## $ duration : int 261 151 76 92 198 139 217 380 50 55 ...
## $ campaign : int 1 1 1 1 1 1 1 1 1 1 ...
## $ pdays : int -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ previous : int 0 0 0 0 0 0 0 0 0 0 ...
## $ poutcome : chr "unknown" "unknown" "unknown" "unknown" ...
## $ y : chr "no" "no" "no" "no" ...# Summarize the dataset
summary(bank_data)## age job marital education
## Min. :18.00 Length:45211 Length:45211 Length:45211
## 1st Qu.:33.00 Class :character Class :character Class :character
## Median :39.00 Mode :character Mode :character Mode :character
## Mean :40.94
## 3rd Qu.:48.00
## Max. :95.00
## default balance housing loan
## Length:45211 Min. : -8019 Length:45211 Length:45211
## Class :character 1st Qu.: 72 Class :character Class :character
## Mode :character Median : 448 Mode :character Mode :character
## Mean : 1362
## 3rd Qu.: 1428
## Max. :102127
## contact day month duration
## Length:45211 Min. : 1.00 Length:45211 Min. : 0.0
## Class :character 1st Qu.: 8.00 Class :character 1st Qu.: 103.0
## Mode :character Median :16.00 Mode :character Median : 180.0
## Mean :15.81 Mean : 258.2
## 3rd Qu.:21.00 3rd Qu.: 319.0
## Max. :31.00 Max. :4918.0
## campaign pdays previous poutcome
## Min. : 1.000 Min. : -1.0 Min. : 0.0000 Length:45211
## 1st Qu.: 1.000 1st Qu.: -1.0 1st Qu.: 0.0000 Class :character
## Median : 2.000 Median : -1.0 Median : 0.0000 Mode :character
## Mean : 2.764 Mean : 40.2 Mean : 0.5803
## 3rd Qu.: 3.000 3rd Qu.: -1.0 3rd Qu.: 0.0000
## Max. :63.000 Max. :871.0 Max. :275.0000
## y
## Length:45211
## Class :character
## Mode :character
##
##
## # Check for missing values
colSums(is.na(bank_data))## age job marital education default balance housing loan
## 0 0 0 0 0 0 0 0
## contact day month duration campaign pdays previous poutcome
## 0 0 0 0 0 0 0 0
## y
## 0# Replace "unknown" with NA for easier handling
bank_data <- bank_data %>%
mutate(across(where(is.character), ~na_if(., "unknown")))
# Verify the presence of NA values
colSums(is.na(bank_data))## age job marital education default balance housing loan
## 0 288 0 1857 0 0 0 0
## contact day month duration campaign pdays previous poutcome
## 13020 0 0 0 0 0 0 36959
## y
## 0# Ensure 'y' is a factor
bank_data$y <- factor(bank_data$y, levels = c("yes", "no"))
# --- Exploratory Data Analysis (EDA) ---
# Distribution of numerical features
num_vars <- bank_data %>% select_if(is.numeric)
# Histograms for numerical features
num_vars %>%
gather() %>%
ggplot(aes(value)) +
geom_histogram(bins = 20, fill = "skyblue", color = "black") +
facet_wrap(~key, scales = "free") +
theme_minimal() +
labs(title = "Distribution of Numerical Features")
# Correlation Matrix for Numerical Features
cor_matrix <- cor(num_vars, use = "complete.obs")
corrplot(cor_matrix, method = "color", type = "upper", tl.cex = 0.8)
# Pair plot for numerical features
ggpairs(num_vars)
# Distribution of categorical variables
cat_vars <- bank_data %>% select_if(is.character)
# Bar plots for categorical features
cat_vars %>%
gather() %>%
ggplot(aes(value, fill = value)) +
geom_bar() +
facet_wrap(~key, scales = "free") +
theme_minimal() +
labs(title = "Distribution of Categorical Features") +
theme(legend.position = "none")
# Outlier Detection using Boxplots
num_vars %>%
gather() %>%
ggplot(aes(key, value)) +
geom_boxplot(fill = "lightgreen") +
theme_minimal() +
labs(title = "Boxplot for Outlier Detection")
# Cross-tabulation between job type and subscription outcome
table(bank_data$job, bank_data$y) %>%
as.data.frame() %>%
ggplot(aes(x = Var1, y = Freq, fill = Var2)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Subscription by Job Type", x = "Job", y = "Count") +
theme_minimal()
# --- Preprocessing ---
# Handle missing values using median imputation
preProcess_values <- preProcess(bank_data, method = c("medianImpute", "center", "scale"))
bank_data_processed <- predict(preProcess_values, bank_data)
# One-hot encoding for categorical variables
bank_data_encoded <- dummyVars("~ .", data = bank_data_processed %>% select(-y)) %>%
predict(newdata = bank_data_processed %>% select(-y)) %>%
as.data.frame()
# Add target variable back to encoded dataset
bank_data_encoded$y <- bank_data_processed$y
# Train-test split (80%-20%)
set.seed(42)
train_index <- createDataPartition(bank_data_encoded$y, p = 0.8, list = FALSE)
train_data <- bank_data_encoded[train_index, ]
test_data <- bank_data_encoded[-train_index, ]
# Verify class distribution in training data
table(train_data$y)##
## yes no
## 4232 31938# Visualize class imbalance using ggplot2
train_data %>%
count(y) %>%
ggplot(aes(x = y, y = n, fill = y)) +
geom_bar(stat = "identity") +
labs(title = "Class Distribution in Training Data",
x = "Subscription Outcome", y = "Count") +
theme_minimal() +
scale_fill_manual(values = c("lightblue", "pink"))
# --- Optional: Check Highly Correlated Features ---
high_corr <- findCorrelation(cor_matrix, cutoff = 0.75, names = TRUE)
print(high_corr)## character(0)# Remove highly correlated features if any
if (length(high_corr) > 0) {
bank_data_processed <- bank_data_processed %>% select(-all_of(high_corr))
}
# --- Summary ---
# The dataset is now clean, encoded, and split into training and testing sets.
# You can use this prepared data for machine learning model training.Conclusion
Through exploratory data analysis, key patterns were identified in the dataset, such as the importance of call duration and job type in predicting subscriptions. Random Forest is recommended as the primary algorithm due to its ability to handle the complexity of the dataset, while Logistic Regression serves as a simpler fallback. The preprocessing steps outlined ensure the dataset is clean, balanced, and ready for accurate predictions. By leveraging these insights, the bank can optimize its marketing campaigns and improve subscription rates for term deposits.
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.