imports
library(tidyverse)
library(janitor)
library(skimr)
library(gtsummary)
library(summarytools)
library(kableExtra)
library(ggplot2)
library(dplyr)
library(readr)
library(summarytools)
library(kableExtra)
library(knitr)
library(gridExtra)
load data
df <- read_csv("data/train.csv", col_names = TRUE)
glimpse(df)
## Rows: 381,109
## Columns: 12
## $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
## $ Gender <chr> "Male", "Male", "Male", "Male", "Female", "Female…
## $ Age <dbl> 44, 76, 47, 21, 29, 24, 23, 56, 24, 32, 47, 24, 4…
## $ Driving_License <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ Region_Code <dbl> 28, 3, 28, 11, 41, 33, 11, 28, 3, 6, 35, 50, 15, …
## $ Previously_Insured <dbl> 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0…
## $ Vehicle_Age <chr> "> 2 Years", "1-2 Year", "> 2 Years", "< 1 Year",…
## $ Vehicle_Damage <chr> "Yes", "No", "Yes", "No", "No", "Yes", "Yes", "Ye…
## $ Annual_Premium <dbl> 40454, 33536, 38294, 28619, 27496, 2630, 23367, 3…
## $ Policy_Sales_Channel <dbl> 26, 26, 26, 152, 152, 160, 152, 26, 152, 152, 124…
## $ Vintage <dbl> 217, 183, 27, 203, 39, 176, 249, 72, 28, 80, 46, …
## $ Response <dbl> 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0…
data cleaning
df1 <- janitor::clean_names(df) %>%
rename(days_associated = vintage,
health_annual_paid = annual_premium) %>%
mutate(
across(where(is.character), tolower),
driving_license = ifelse(driving_license == 1, 'yes', 'no'),
previously_insured = ifelse(previously_insured == 1, 'yes', 'no'),
response = ifelse(response == 1, 'yes', 'no'),
vehicle_age = case_when(
vehicle_age == "< 1 year" ~ "below_1_year",
vehicle_age == "1-2 year" ~ "between_1_2_years",
vehicle_age == "> 2 years" ~ "over_2_years"
)
) %>%
mutate_if(is.character, as.factor) %>%
mutate(response = factor(response, levels = c('yes', 'no')),
driving_license = factor(driving_license, levels = c('yes', 'no')),
previously_insured = factor(previously_insured, levels = c('yes', 'no')),
vehicle_damage = factor(vehicle_damage, levels = c('yes', 'no')))
glimpse(df1)
## Rows: 381,109
## Columns: 12
## $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
## $ gender <fct> male, male, male, male, female, female, male, fem…
## $ age <dbl> 44, 76, 47, 21, 29, 24, 23, 56, 24, 32, 47, 24, 4…
## $ driving_license <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, yes,…
## $ region_code <dbl> 28, 3, 28, 11, 41, 33, 11, 28, 3, 6, 35, 50, 15, …
## $ previously_insured <fct> no, no, no, yes, yes, no, no, no, yes, yes, no, y…
## $ vehicle_age <fct> over_2_years, between_1_2_years, over_2_years, be…
## $ vehicle_damage <fct> yes, no, yes, no, no, yes, yes, yes, no, no, yes,…
## $ health_annual_paid <dbl> 40454, 33536, 38294, 28619, 27496, 2630, 23367, 3…
## $ policy_sales_channel <dbl> 26, 26, 26, 152, 152, 160, 152, 26, 152, 152, 124…
## $ days_associated <dbl> 217, 183, 27, 203, 39, 176, 249, 72, 28, 80, 46, …
## $ response <fct> yes, no, yes, no, no, no, no, yes, no, no, yes, n…
# save df_cleaned as RDS
saveRDS(df1, 'df_cleaned.rds')
data types
variable_classes <- tibble(variables = names(df1),
type = unlist(lapply(df1, class))
)
variable_classes
column description
variables <- df1 %>% names()
description <- c(
"Unique ID for the customer",
"Gender of the customer",
"Age of the customer",
"Customer has DL (yes/no)",
"Unique code for the region of the customer",
"Customer already has Vehicle Insurance (yes/no)",
"Age of the Vehicle",
"Customer got his/her vehicle damaged in the past (yes/no)",
"The amount customer needs to pay as premium in the year",
"Anonymized Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.",
"Number of Days, Customer has been associated with the company",
"Customer is interested (yes/no"
)
df_description <- tibble(variables = variables,
description = description)
kable(data.frame(df_description), format = 'html') %>%
kableExtra::kable_styling(bootstrap_options = 'striped',
full_width = FALSE)
|
variables
|
description
|
|
id
|
Unique ID for the customer
|
|
gender
|
Gender of the customer
|
|
age
|
Age of the customer
|
|
driving_license
|
Customer has DL (yes/no)
|
|
region_code
|
Unique code for the region of the customer
|
|
previously_insured
|
Customer already has Vehicle Insurance (yes/no)
|
|
vehicle_age
|
Age of the Vehicle
|
|
vehicle_damage
|
Customer got his/her vehicle damaged in the past (yes/no)
|
|
health_annual_paid
|
The amount customer needs to pay as premium in the year
|
|
policy_sales_channel
|
Anonymized Code for the channel of outreaching to the customer ie.
Different Agents, Over Mail, Over Phone, In Person, etc.
|
|
days_associated
|
Number of Days, Customer has been associated with the company
|
|
response
|
Customer is interested (yes/no
|
descriptive
statistics
df_cleaned <- readRDS('df_cleaned.rds')
glimpse(df_cleaned)
## Rows: 381,109
## Columns: 12
## $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15…
## $ gender <fct> male, male, male, male, female, female, male, fem…
## $ age <dbl> 44, 76, 47, 21, 29, 24, 23, 56, 24, 32, 47, 24, 4…
## $ driving_license <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, yes,…
## $ region_code <dbl> 28, 3, 28, 11, 41, 33, 11, 28, 3, 6, 35, 50, 15, …
## $ previously_insured <fct> no, no, no, yes, yes, no, no, no, yes, yes, no, y…
## $ vehicle_age <fct> over_2_years, between_1_2_years, over_2_years, be…
## $ vehicle_damage <fct> yes, no, yes, no, no, yes, yes, yes, no, no, yes,…
## $ health_annual_paid <dbl> 40454, 33536, 38294, 28619, 27496, 2630, 23367, 3…
## $ policy_sales_channel <dbl> 26, 26, 26, 152, 152, 160, 152, 26, 152, 152, 124…
## $ days_associated <dbl> 217, 183, 27, 203, 39, 176, 249, 72, 28, 80, 46, …
## $ response <fct> yes, no, yes, no, no, no, no, yes, no, no, yes, n…
skimr::skim(df_cleaned)
Data summary
| Name |
df_cleaned |
| Number of rows |
381109 |
| Number of columns |
12 |
| _______________________ |
|
| Column type frequency: |
|
| factor |
6 |
| numeric |
6 |
| ________________________ |
|
| Group variables |
None |
Variable type: factor
| gender |
0 |
1 |
FALSE |
2 |
mal: 206089, fem: 175020 |
| driving_license |
0 |
1 |
FALSE |
2 |
yes: 380297, no: 812 |
| previously_insured |
0 |
1 |
FALSE |
2 |
no: 206481, yes: 174628 |
| vehicle_age |
0 |
1 |
FALSE |
3 |
bet: 200316, bel: 164786, ove: 16007 |
| vehicle_damage |
0 |
1 |
FALSE |
2 |
yes: 192413, no: 188696 |
| response |
0 |
1 |
FALSE |
2 |
no: 334399, yes: 46710 |
Variable type: numeric
| id |
0 |
1 |
190555.00 |
110016.84 |
1 |
95278 |
190555 |
285832 |
381109 |
▇▇▇▇▇ |
| age |
0 |
1 |
38.82 |
15.51 |
20 |
25 |
36 |
49 |
85 |
▇▃▃▂▁ |
| region_code |
0 |
1 |
26.39 |
13.23 |
0 |
15 |
28 |
35 |
52 |
▃▂▇▃▃ |
| health_annual_paid |
0 |
1 |
30564.39 |
17213.16 |
2630 |
24405 |
31669 |
39400 |
540165 |
▇▁▁▁▁ |
| policy_sales_channel |
0 |
1 |
112.03 |
54.20 |
1 |
29 |
133 |
152 |
163 |
▅▁▁▃▇ |
| days_associated |
0 |
1 |
154.35 |
83.67 |
10 |
82 |
154 |
227 |
299 |
▇▇▇▇▇ |
general overview
df_cleaned %>%
select(-id) %>%
tbl_summary(
type = list(response ~ 'categorical',
driving_license ~ 'categorical',
previously_insured ~ 'categorical',
vehicle_damage ~ 'categorical'),
digits = list(all_categorical() ~ c(0, 2))
)
| Characteristic |
N = 381,109 |
| gender |
|
| female |
175,020 (45.92%) |
| male |
206,089 (54.08%) |
| age |
36 (25, 49) |
| driving_license |
|
| yes |
380,297 (99.79%) |
| no |
812 (0.21%) |
| region_code |
28 (15, 35) |
| previously_insured |
|
| yes |
174,628 (45.82%) |
| no |
206,481 (54.18%) |
| vehicle_age |
|
| below_1_year |
164,786 (43.24%) |
| between_1_2_years |
200,316 (52.56%) |
| over_2_years |
16,007 (4.20%) |
| vehicle_damage |
|
| yes |
192,413 (50.49%) |
| no |
188,696 (49.51%) |
| health_annual_paid |
31,669 (24,405, 39,400) |
| policy_sales_channel |
133 (29, 152) |
| days_associated |
154 (82, 227) |
| response |
|
| yes |
46,710 (12.26%) |
| no |
334,399 (87.74%) |
more detailed
statistics
numerical <- df_cleaned %>%
select(age, health_annual_paid, days_associated)
descriptive_tab <- summarytools::descr(numerical, style = 'rmarkdown') %>% round(2)
## Error : Can't find summarytools
kable(data.frame(descriptive_tab), format = 'html') %>%
kableExtra::kable_styling(bootstrap_options = 'striped',
full_width = FALSE)
|
|
age
|
days_associated
|
health_annual_paid
|
|
Mean
|
38.82
|
154.35
|
30564.39
|
|
Std.Dev
|
15.51
|
83.67
|
17213.16
|
|
Min
|
20.00
|
10.00
|
2630.00
|
|
Q1
|
25.00
|
82.00
|
24405.00
|
|
Median
|
36.00
|
154.00
|
31669.00
|
|
Q3
|
49.00
|
227.00
|
39400.00
|
|
Max
|
85.00
|
299.00
|
540165.00
|
|
MAD
|
17.79
|
108.23
|
11125.43
|
|
IQR
|
24.00
|
145.00
|
14995.00
|
|
CV
|
0.40
|
0.54
|
0.56
|
|
Skewness
|
0.67
|
0.00
|
1.77
|
|
SE.Skewness
|
0.00
|
0.00
|
0.00
|
|
Kurtosis
|
-0.57
|
-1.20
|
34.00
|
|
N.Valid
|
381109.00
|
381109.00
|
381109.00
|
|
Pct.Valid
|
100.00
|
100.00
|
100.00
|
visualization
# age
age_plt <- numerical %>%
ggplot(aes(x=age)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 1,
color = 'gray', fill = 'lightblue', alpha = 0.5) +
geom_density(color="blue") +
labs(x ='age', y='density', title= 'Customers Age Distribution') +
theme_minimal()
# health_annual_paid
paid_plt <- numerical %>%
ggplot(aes(x=health_annual_paid)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10000,
color = 'gray', fill = 'lightblue', alpha = 0.5) +
geom_density(color="blue") +
labs(x ='health_annual_paid', y='density', title= 'Customers Payments Distribution') +
theme_minimal()
# days_associated
days_plt <- numerical %>%
ggplot(aes(x=days_associated)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 10,
color = 'gray', fill = 'lightblue', alpha = 0.5) +
geom_density(color="blue") +
labs(x ='days_associated', y='density', title= 'Customers Days Associated Distribution') +
theme_minimal()
gridExtra::grid.arrange(age_plt, paid_plt, days_plt, ncol = 3)
categorical
attributes
num_names <- names(numerical)
categorical <- df_cleaned %>%
select(-id, -one_of(num_names))
gender_plt <- categorical %>%
ggplot(aes(x=gender)) +
geom_bar(aes(fill=gender)) +
labs(x = 'gender', y='#', title='Customers Gender') +
theme_minimal()
driving_license_plt <- categorical %>%
ggplot(aes(x=driving_license)) +
geom_bar(aes(fill=driving_license)) +
labs(x = 'driving_license', y='#', title='Customers Driving License') +
theme_minimal()
region_code_plt <- categorical %>%
ggplot(aes(x=region_code)) +
geom_bar(aes(fill=factor(region_code)),
show.legend=FALSE) +
labs(x = 'region_code', y='#', title='Customers Region Code') +
theme_minimal()
previously_insured_plt <- categorical %>%
ggplot(aes(x=previously_insured)) +
geom_bar(aes(fill=previously_insured)) +
labs(x = 'previously_insured', y='#', title='Customers Previously Insured') +
theme_minimal()
vehicle_age_plt <- categorical %>%
ggplot(aes(x=vehicle_age)) +
geom_bar(aes(fill=vehicle_age)) +
labs(x = 'vehicle_age', y='#', title='Customers Vehicle Age') +
theme_minimal()
vehicle_damage_plt <- categorical %>%
ggplot(aes(x=vehicle_damage)) +
geom_bar(aes(fill=vehicle_damage)) +
labs(x = 'vehicle_damage', y='#', title='Customers Vehicle Damage') +
theme_minimal()
policy_sales_channel_plt <- categorical %>%
ggplot(aes(x=policy_sales_channel)) +
geom_bar(aes(fill=policy_sales_channel),
show.legend = FALSE) +
labs(x = 'policy_sales_channel', y='#', title='Customers Policy Sales Channel') +
theme_minimal()
response_plt <- categorical %>%
ggplot(aes(x=response)) +
geom_bar(aes(fill=response)) +
labs(x = 'response', y='#', title='Customers Response') +
theme_minimal()
gridExtra::grid.arrange(gender_plt, driving_license_plt, region_code_plt, previously_insured_plt, vehicle_damage_plt, vehicle_age_plt, policy_sales_channel_plt, response_plt, ncol = 4, nrow = 2)
