Week 2 Data Dive- Summaries
Reshu Gupta
2024-01-30
- Initial setup and Configure the data set.
- Load the data set file in variable hotel_data files
- Data set - Hotels : This data comes from an open hotel booking demand dataset from Antonio, Almeida and Nunes.
This section is about the Numeric summary for two columns - lead_time and stays_in_weekend_nights.
numeric_summary <- summary(hotel_data[c('lead_time', 'stays_in_weekend_nights')])This section is about the display of Numeric summary of two columns - lead_time and stays_in_weekend_nights.
numeric_summary## lead_time stays_in_weekend_nights
## Min. : 0 Min. : 0.0000
## 1st Qu.: 18 1st Qu.: 0.0000
## Median : 69 Median : 1.0000
## Mean :104 Mean : 0.9276
## 3rd Qu.:160 3rd Qu.: 2.0000
## Max. :737 Max. :19.0000This section is about to calculate the unique values and counts for categorical columns -meal and market_segment.
categorical_summary1 <- table(hotel_data$meal)
categorical_summary2 <- table(hotel_data$market_segment)This section is about to display the unique values and counts for categorical columns -meal and market_segment.
categorical_summary1##
## BB FB HB SC Undefined
## 92310 798 14463 10650 1169categorical_summary2##
## Aviation Complementary Corporate Direct Groups
## 237 743 5295 12606 19811
## Offline TA/TO Online TA Undefined
## 24219 56477 2This section is about to calculate and display the aggregating lead time impact on cancellations
lead_time_cancellation_aggregate <- aggregate(is_canceled ~ lead_time, data = hotel_data, FUN = function(x) mean(x == 1))
lead_time_cancellation_aggregate## lead_time is_canceled
## 1 0 0.06776990
## 2 1 0.09277457
## 3 2 0.10294828
## 4 3 0.10022026
## 5 4 0.10262391
## 6 5 0.13162939
## 7 6 0.13979239
## 8 7 0.12922615
## 9 8 0.19595782
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## 13 12 0.25764597
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## 477 629 1.00000000
## 478 709 0.00000000
## 479 737 0.00000000This section is about to calculate and display the aggregating effect of meal type on customer satisfaction.
meal_satisfaction_aggregate <- aggregate(adr ~ meal, data = hotel_data, FUN = mean)
meal_satisfaction_aggregate## meal adr
## 1 BB 99.40704
## 2 FB 109.04048
## 3 HB 120.30704
## 4 SC 98.29587
## 5 Undefined 91.94831Including Plot #1
This section is about the graph for the ‘Lead Time Distribution’
## Including Plot #2
This section is about the graph for the ‘Correlation between Lead Time and Cancellations’
lead_time_cancellation_plot <- ggplot(hotel_data, aes(x = lead_time, fill = factor(is_canceled))) +
geom_density(alpha = 0.7) +
labs(title = "Correlation between Lead Time and Cancellations",
x = "Lead Time (days)",
y = "Density",
fill = "Cancellation") +
theme_minimal()
# Display the lead_time_cancellation_plot plot
lead_time_cancellation_plot
##Booking Pattern and Lead Time
# Question : How does
the lead time booking and arrival vary across diff type of meal (meal
column) and market segments ( market_segment column)
# Reasoning: By
having the lead time pattern, Meal and market segments can be associated
and marketting and operational planning can be strategiyes
accordingly.
##Lead Time Impact on cancellation
# How
does the lead time - number of days and arrival , correlate with the
likehood of cancellation
# Reasoning: By understanding the
relationship between lead time and cancellation , you can gain insights
into whether customer are more likehood to cencel reservation made well
in advance or closure to the avival date so that marketting and
operational planning can be strategiyes accordingly.
## Customer
preference across market segment
# How do cusomer prefernce like
booking changes, special request etc. vary across different market
segment like online travel agency and corporate.
#
Reasoning:Exploring how customer behaviour differs among market segment
can guide targets market segment and improves Services based on unique
needs and preference of each segment.
Thank You!!!