Homework 3: Hotel bookings

Study for Hotel bookings data set

Code

library(ggplot2)
library(dplyr)
library(tidyr)
library(tidyverse)
library(caret)
library(randomForest)
library(lubridate)

data <- read_csv("_data/hotel_bookings.csv")
knitr::opts_chunk$set(echo = TRUE)

Project Overview(HW3)

This Homework further develops the concepts introduced in Homework 2, employing them to answer research queries and construct illustrative visualizations. It also requires the modification and refinement of some code and text elements from the previous tasks.

In this project, we will be working with a hotel booking dataset. This dataset includes detailed booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, duration of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things.

I first explored the hotel booking dataset from 2015 to 2017, then understood the significance of each variable, cleaned the data by removing irrelevant columns and handling missing values, and finally, converted the date fields to the correct data type; this helped me formulate key research questions about hotel booking behaviors, after which I prepared a comprehensible narrative for non-experts to understand the findings.

Read Data

Code

data

Code

summary(data)

    hotel            is_canceled       lead_time   arrival_date_year
 Length:119390      Min.   :0.0000   Min.   :  0   Min.   :2015     
 Class :character   1st Qu.:0.0000   1st Qu.: 18   1st Qu.:2016     
 Mode  :character   Median :0.0000   Median : 69   Median :2016     
                    Mean   :0.3704   Mean   :104   Mean   :2016     
                    3rd Qu.:1.0000   3rd Qu.:160   3rd Qu.:2017     
                    Max.   :1.0000   Max.   :737   Max.   :2017     
                                                                    
 arrival_date_month arrival_date_week_number arrival_date_day_of_month
 Length:119390      Min.   : 1.00            Min.   : 1.0             
 Class :character   1st Qu.:16.00            1st Qu.: 8.0             
 Mode  :character   Median :28.00            Median :16.0             
                    Mean   :27.17            Mean   :15.8             
                    3rd Qu.:38.00            3rd Qu.:23.0             
                    Max.   :53.00            Max.   :31.0             
                                                                      
 stays_in_weekend_nights stays_in_week_nights     adults      
 Min.   : 0.0000         Min.   : 0.0         Min.   : 0.000  
 1st Qu.: 0.0000         1st Qu.: 1.0         1st Qu.: 2.000  
 Median : 1.0000         Median : 2.0         Median : 2.000  
 Mean   : 0.9276         Mean   : 2.5         Mean   : 1.856  
 3rd Qu.: 2.0000         3rd Qu.: 3.0         3rd Qu.: 2.000  
 Max.   :19.0000         Max.   :50.0         Max.   :55.000  
                                                              
    children           babies              meal             country         
 Min.   : 0.0000   Min.   : 0.000000   Length:119390      Length:119390     
 1st Qu.: 0.0000   1st Qu.: 0.000000   Class :character   Class :character  
 Median : 0.0000   Median : 0.000000   Mode  :character   Mode  :character  
 Mean   : 0.1039   Mean   : 0.007949                                        
 3rd Qu.: 0.0000   3rd Qu.: 0.000000                                        
 Max.   :10.0000   Max.   :10.000000                                        
 NA's   :4                                                                  
 market_segment     distribution_channel is_repeated_guest
 Length:119390      Length:119390        Min.   :0.00000  
 Class :character   Class :character     1st Qu.:0.00000  
 Mode  :character   Mode  :character     Median :0.00000  
                                         Mean   :0.03191  
                                         3rd Qu.:0.00000  
                                         Max.   :1.00000  
                                                          
 previous_cancellations previous_bookings_not_canceled reserved_room_type
 Min.   : 0.00000       Min.   : 0.0000                Length:119390     
 1st Qu.: 0.00000       1st Qu.: 0.0000                Class :character  
 Median : 0.00000       Median : 0.0000                Mode  :character  
 Mean   : 0.08712       Mean   : 0.1371                                  
 3rd Qu.: 0.00000       3rd Qu.: 0.0000                                  
 Max.   :26.00000       Max.   :72.0000                                  
                                                                         
 assigned_room_type booking_changes   deposit_type          agent          
 Length:119390      Min.   : 0.0000   Length:119390      Length:119390     
 Class :character   1st Qu.: 0.0000   Class :character   Class :character  
 Mode  :character   Median : 0.0000   Mode  :character   Mode  :character  
                    Mean   : 0.2211                                        
                    3rd Qu.: 0.0000                                        
                    Max.   :21.0000                                        
                                                                           
   company          days_in_waiting_list customer_type           adr         
 Length:119390      Min.   :  0.000      Length:119390      Min.   :  -6.38  
 Class :character   1st Qu.:  0.000      Class :character   1st Qu.:  69.29  
 Mode  :character   Median :  0.000      Mode  :character   Median :  94.58  
                    Mean   :  2.321                         Mean   : 101.83  
                    3rd Qu.:  0.000                         3rd Qu.: 126.00  
                    Max.   :391.000                         Max.   :5400.00  
                                                                             
 required_car_parking_spaces total_of_special_requests reservation_status
 Min.   :0.00000             Min.   :0.0000            Length:119390     
 1st Qu.:0.00000             1st Qu.:0.0000            Class :character  
 Median :0.00000             Median :0.0000            Mode  :character  
 Mean   :0.06252             Mean   :0.5714                              
 3rd Qu.:0.00000             3rd Qu.:1.0000                              
 Max.   :8.00000             Max.   :5.0000                              
                                                                         
 reservation_status_date
 Min.   :2014-10-17     
 1st Qu.:2016-02-01     
 Median :2016-08-07     
 Mean   :2016-07-30     
 3rd Qu.:2017-02-08     
 Max.   :2017-09-14     
                        

Mutate variable and Clean

in this part we done three things

• 1. Removal of irrelevant or redundant columns: The first line of code is using the ‘select’ function from the ‘dplyr’ package to remove the ‘agent’ and ‘company’ columns from the ‘data’ data frame. These columns are considered unnecessary for further analysis.
• 2. Handling missing values: The next few lines are dealing with missing values in the ‘children’ column. Any ‘NA’ values in this column are being replaced with the mean value of the ‘children’ column (calculated without considering the ‘NA’ values).
• 3. Data type conversion: The last few lines of code are creating a new ‘arrival_date’ column by combining the ‘arrival_date_year’, ‘arrival_date_month’, and ‘arrival_date_day_of_month’ columns. The ‘paste’ function is used to concatenate these three columns into a string in the format ‘year-month-day’. The ‘as.Date’ function is then used to convert this string into a date object. Finally, the original ‘arrival_date_year’, ‘arrival_date_month’, and ‘arrival_date_day_of_month’ columns are removed from the ‘data’ data frame as they are now redundant.

Now, we can see the new data set.

Code

# 1. Remove irrelevant or redundant columns
data <- data %>%
  select(-c(agent, company))

# 2. Handle missing values: replace the missing values ​​of the list of values ​​with the average value

data$children[is.na(data$children)] <- mean(data$children, na.rm = TRUE)

# Note: we assume the day of each observation to be the 1st
data <- data %>% mutate(arrival_date = ymd(paste(arrival_date_year, arrival_date_month, arrival_date_day_of_month, sep = "-")))

#drop the original Year and Month columns
data <- data %>% select(-arrival_date_year, -arrival_date_month, -arrival_date_day_of_month)

data

The narrative about the data set

The dataset that we are analyzing is about hotel bookings. It contains 119,390 records, each representing a separate hotel booking. These bookings span from the year 2015 to 2017, covering various hotels, customers from different countries, and diverse market segments. It is a rich dataset providing various insights into hotel booking patterns, customer preferences, and booking cancellations.

This dataset comprises various information about hotel bookings. For each booking, the following attributes are provided:

• hotel: Hotel (Resort Hotel or City Hotel) where the booking was made.
• is_canceled: Value indicating if the booking was canceled (1) or not (0).
• lead_time: Number of days that elapsed between the entering date of the booking into the Property Management System and the arrival date.
• arrival_date_year: Year of arrival date.
• arrival_date_month: Month of arrival date.
• arrival_date_week_number: Week number of year for arrival date.
• arrival_date_day_of_month: Day of arrival date.
• stays_in_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel.
• stays_in_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel.
• adults: Number of adults.
• children: Number of children.
• babies: Number of babies.
• meal: Type of meal booked.
• country: Country of origin of the booking.
• market_segment: Market segment designation.
• distribution_channel: Booking distribution channel.
• is_repeated_guest: Value indicating if the booking name was from a repeated guest (1) or not (0).
• previous_cancellations: Number of previous bookings that were cancelled by the customer prior to the current booking.
• previous_bookings_not_canceled: Number of previous bookings not cancelled by the customer prior to the current booking.
• reserved_room_type: Code of room type reserved.
• assigned_room_type: Code for the type of room assigned to the booking.
• booking_changes: Number of changes/amendments made to the booking from the moment the booking was entered on - the Property Management System until the moment of check-in or cancellation.
• deposit_type: Indication on if the customer made a deposit to guarantee the booking.
• agent: ID of the travel agency that made the booking.
• company: ID of the company/entity that made the booking or responsible for paying the booking.
• days_in_waiting_list: Number of days the booking was in the waiting list before it was confirmed to the customer.
• customer_type: Type of booking.
• adr: Average Daily Rate as defined by dividing the sum of all lodging transactions by the total number of staying nights.
• required_car_parking_spaces`: Number of car parking spaces required by the customer.
• total_of_special_requests: Number of special requests made by the customer.
• reservation_status: Last reservation status, assuming one of three categories: Canceled, Check-Out, No-Show.
• reservation_status_date: Date at which the last status was set. The data are with very long content.

research questions

• 1. Booking Patterns: What are the busiest months for hotels? Does the lead time of booking relate to the busy periods?
• 2. Guest Preferences: Are certain room types more popular than others? Does the choice of room type correlate with any other variables like the number of adults, children, or babies in the party?
• 3. Cancellations: Are cancellations more likely for certain types of bookings (e.g., long lead time, specific room type, repeated guest etc.)? Can we predict if a booking will be cancelled based on certain characteristics?
• 4. Special Requests: How often do customers make special requests? Does the presence of a special request impact the likelihood of cancellation?
• 5. Pricing Strategies: Does the price (ADR) vary by the time of year or by room type? What factors might be influencing these pricing strategies?

Booking Patterns

• 1. Booking Patterns: What are the busiest months for hotels? Does the lead time of booking relate to the busy periods?

Code

# Plotting the number of bookings per month per year
data %>%
  count(arrival_date_year = year(arrival_date), arrival_date_month = month(arrival_date, label = FALSE)) %>%
  ggplot(aes(x = arrival_date_month, y = n, fill = as.factor(arrival_date_month))) +
  geom_col() +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  facet_wrap(~arrival_date_year) +
  labs(x = "Month", y = "Number of Bookings", title = "Number of Bookings per Month per Year") +
  theme_minimal()

Code

# Plotting the average lead time per month per year
data %>%
  group_by(arrival_date_year = year(arrival_date), arrival_date_month = month(arrival_date, label = FALSE)) %>%
  summarise(avg_lead_time = mean(lead_time, na.rm = TRUE), .groups = 'drop') %>%
  ggplot(aes(x = arrival_date_month, y = avg_lead_time, fill = as.factor(arrival_date_month))) +
  geom_col() +
  scale_x_continuous(breaks = 1:12, labels = month.abb) +
  facet_wrap(~arrival_date_year) +
  labs(x = "Month", y = "Average Lead Time", title = "Average Lead Time per Month per Year") +
  theme_minimal()

From the data provided, the busiest months for hotels, in terms of the number of bookings, tend to be May, June, and October in 2016 and April, May, and June in 2017. Notably, the month with the highest number of bookings was May in 2017 with 6300 bookings.

Regarding the relationship between lead time and busy periods, there seems to be a general trend that busier months have longer lead times. For instance, in 2016, October had the highest number of bookings and one of the longest average lead times (145 days). Similarly, in 2017, May had both the highest number of bookings and one of the longest lead times (119 days).

However, this is not always the case. In 2016, May had the longest lead time (150 days) but did not have the highest number of bookings (5250 bookings). In 2017, July had the longest lead time (151 days), but it was not the busiest month in terms of bookings.

Overall, while there appears to be some relationship between lead time and the number of bookings, the relationship is not consistent across all months. It’s possible that other factors also play a significant role in influencing booking patterns, such as seasonal events, holidays, and hotel pricing strategies, among others.

Guest Preferences

• 2. Guest Preferences: Are certain room types more popular than others? Does the choice of room type correlate with any other variables like the number of adults, children, or babies in the party?

Code

# Create a bar chart to visualize the popularity of different room types
data %>%
  group_by(reserved_room_type) %>%
  summarise(count = n()) %>%
  ggplot(aes(x = reorder(reserved_room_type, -count), y = count)) +
  geom_col(fill = "skyblue") +
  labs(x = "Room Type", y = "Number of Bookings", 
       title = "Popularity of Room Types") +
  theme_minimal()

Code

# Create a box plot to visualize the relationship between room type and number of adults
data %>%
  ggplot(aes(x = reserved_room_type, y = adults)) +
  geom_boxplot() +
  labs(x = "Room Type", y = "Number of Adults", 
       title = "Room Type vs Number of Adults") +
  theme_minimal()

Code

# Create a box plot to visualize the relationship between room type and number of children
data %>%
  ggplot(aes(x = reserved_room_type, y = children)) +
  geom_boxplot() +
  labs(x = "Room Type", y = "Number of Children", 
       title = "Room Type vs Number of Children") +
  theme_minimal()

Code

# Create a box plot to visualize the relationship between room type and number of babies
data %>%
  ggplot(aes(x = reserved_room_type, y = babies)) +
  geom_boxplot() +
  labs(x = "Room Type", y = "Number of Babies", 
       title = "Room Type vs Number of Babies") +
  theme_minimal()

Based on the data, room type “A” is the most booked (87,500) suggesting high popularity, likely due to factors such as price, size, or amenities. Room type “D” is the second most favored (20,000 bookings). Room types “E” to “L” have fewer bookings, indicating less popularity. The least preferred are “P” and “L” with 1,000 bookings each, possibly due to higher pricing or limited availability. This data can guide the hotel’s strategic decisions, such as pricing adjustments, renovations, or marketing promotions, to enhance the attractiveness of less popular rooms and optimize the usage of the most popular ones. To get a fuller understanding, further analysis involving customer reviews, room prices, and room availability would be beneficial.

Firstly, we created a box plot for the relationship between room type and the number of adults. The x-axis represents different room types and the y-axis indicates the number of adults. For room type ‘A’, there are 40 individual points, while the range of 20-40 houses two points. The box plots for room types ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’, ‘L’, ‘P’ have medians between 0 and 3, and the boxes appear compressed, indicating a lower variance in the number of adults.

Similarly, we crafted a box plot to visualize the correlation between room type and the number of children. The boxes for ‘B’, ‘C’, ‘F’, and ‘G’ types appear larger, suggesting a larger variance in the number of children for these room types. However, the median for room types ‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’, ‘L’, ‘P’ remains between 0 and 3.

Finally, a box plot was created to demonstrate the relationship between room type and the number of babies. Room types ‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’ each have two points ranging from 0 to 2.5, whereas ‘L’ and ‘P’ have no babies. The boxes for all room types are quite flat, indicating a lower variance in the number of babies.

Cancellations

• 3. Cancellations: Are cancellations more likely for certain types of bookings (e.g., long lead time, specific room type, repeated guest etc.)? Can we predict if a booking will be cancelled based on certain characteristics?

Code

# Cancellations by lead time
ggplot(data, aes(x = lead_time)) +
  geom_histogram(aes(fill = as.factor(is_canceled)), bins = 30, position = "fill") +
  labs(x = "Lead Time (days)", y = "Proportion", fill = "Cancellation Status",
       title = "Cancellation Rates by Lead Time") +
  theme_minimal()

Warning: Removed 4 rows containing missing values (`geom_bar()`).

Code

# Cancellations by room type
ggplot(data, aes(x = reserved_room_type)) +
  geom_bar(aes(fill = as.factor(is_canceled)), position = "fill") +
  labs(x = "Room Type", y = "Proportion", fill = "Cancellation Status",
       title = "Cancellation Rates by Room Type") +
  theme_minimal()

Code

# Cancellations by repeated guest status
ggplot(data, aes(x = as.factor(is_repeated_guest))) +
  geom_bar(aes(fill = as.factor(is_canceled)), position = "fill") +
  labs(x = "Repeated Guest (0 = No, 1 = Yes)", y = "Proportion", fill = "Cancellation Status",
       title = "Cancellation Rates by Repeated Guest Status") +
  theme_minimal()

Using the ggplot2 package in R, we have created three insightful visualizations to explore cancellation patterns.

The first graph, titled “Cancellation Rates by Lead Time,” is a histogram showcasing the proportion of cancellations over a lead time range of 0 to 600 days. A clear trend surfaces that the cancellation rate escalates as lead time increases, suggesting that longer waits between booking and staying might lead to a higher likelihood of cancellation.

In the second plot, “Cancellation Rates by Room Type,” we delve into the relationship between room types and cancellation rates. Most room types (A through L) demonstrate a consistent cancellation rate hovering around 40%, barring Room P which startlingly exhibits a 100% cancellation rate. This points to potential issues specific to Room P that may need to be addressed.

The final chart, “Cancellation Rates by Repeated Guest Status,” compares the cancellation rates between new guests (labelled as 0) and returning guests (labelled as 1). Intriguingly, new guests exhibit a significantly higher cancellation rate of approximately 35%, as compared to the merely 12.5% of recurring guests, hinting at the reliability of repeated guests and the need for customer retention strategies.

Code

model <- glm(is_canceled ~ lead_time + reserved_room_type + is_repeated_guest, data = data, family = binomial)
summary(model)

Call:
glm(formula = is_canceled ~ lead_time + reserved_room_type + 
    is_repeated_guest, family = binomial, data = data)

Coefficients:
                      Estimate Std. Error  z value Pr(>|z|)    
(Intercept)         -1.067e+00  1.034e-02 -103.194  < 2e-16 ***
lead_time            5.663e-03  6.207e-05   91.227  < 2e-16 ***
reserved_room_typeB -3.244e-01  6.638e-02   -4.888 1.02e-06 ***
reserved_room_typeC -1.000e-01  7.188e-02   -1.392 0.164073    
reserved_room_typeD -2.065e-01  1.756e-02  -11.763  < 2e-16 ***
reserved_room_typeE -3.550e-01  2.899e-02  -12.246  < 2e-16 ***
reserved_room_typeF -1.633e-01  4.201e-02   -3.886 0.000102 ***
reserved_room_typeG  5.615e-02  4.744e-02    1.183 0.236619    
reserved_room_typeH  2.545e-01  8.609e-02    2.956 0.003119 ** 
reserved_room_typeL  3.736e-01  8.661e-01    0.431 0.666180    
reserved_room_typeP  1.163e+01  3.449e+01    0.337 0.735887    
is_repeated_guest   -9.088e-01  4.765e-02  -19.073  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 157398  on 119389  degrees of freedom
Residual deviance: 146403  on 119378  degrees of freedom
AIC: 146427

Number of Fisher Scoring iterations: 9

This output comes from a logistic regression model. It’s a statistical model we use when we want to predict something binary - in this case, whether a hotel booking was cancelled (1) or not (0).

The table you see lists a bunch of different factors that might affect the chance of a booking being cancelled.

First, look at the Estimate column. The values here, like 5.663e-03 for lead_time, represent how much we expect the log-odds of a cancellation to change when the factor increases by one unit. So, a longer lead time (time between booking and arrival) seems to slightly increase the chance of cancellation.

Then, check out the Pr(>|z|) column. This one’s all about how confident we are in these estimates. Smaller numbers here mean we’re pretty confident - like with lead_time, reserved_room_typeD, reserved_room_typeE, and is_repeated_guest. The stars in the rightmost column are another way of showing this - three stars mean we’re really confident.

But notice reserved_room_typeP and reserved_room_typeL? The big values in Pr(>|z|) (like 0.735887 and 0.666180) and lack of stars mean we’re not confident at all in these estimates.

Finally, at the very bottom, Null deviance and Residual deviance measure how well our model is doing. The smaller these numbers are, the better.

Special Requests

• 4. Special Requests: How often do customers make special requests? Does the presence of a special request impact the likelihood of cancellation?

Code

# Histogram of special requests frequency
ggplot(data, aes(x = total_of_special_requests)) +
  geom_histogram(binwidth = 1, color = "black", fill = "skyblue") +
  labs(x = "Number of Special Requests", y = "Count", 
       title = "Histogram of Special Requests Frequency") +
  theme_minimal()

Code

# Bar plot of cancellation rates by special requests
ggplot(data, aes(x = as.factor(total_of_special_requests), fill = as.factor(is_canceled))) +
  geom_bar(position = "fill") +
  scale_fill_discrete(name = "Cancellation Status", labels = c("Not Canceled", "Canceled")) +
  labs(x = "Number of Special Requests", y = "Proportion", 
       title = "Cancellation Rates by Special Requests") +
  theme_minimal()

The histogram of special requests frequency indicates that the majority of customers do not have any special requests, accounting for approximately 70,100 of the total. As the number of special requests increases, the number of customers making these requests decreases substantially. For instance, only a small number of customers (33123) make one special request, with even fewer making two (13456), three (2001), or four (102) special requests. An extremely small number of customers make five special requests (only 2 customers).

The bar plot of cancellation rates by special requests reveals some interesting trends. For customers making no special requests, about 48% of their bookings are canceled. Interestingly, the cancellation rate declines as the number of special requests increases. For customers making one or two special requests, the cancellation rate is 24%, which further drops to 22% for those making three requests, 11.2% for four requests, and remarkably low at 5% for five requests.

In conclusion, most customers do not make special requests. However, those who do make special requests are less likely to cancel their bookings. This suggests that customers who take the time to customize their bookings with special requests are more committed to their stay.

Pricing Strategies

• 5. Pricing Strategies: Does the price (ADR) vary by the time of year or by room type? What factors might be influencing these pricing strategies?

Code

# Scatter plot of ADR by the time of year
data %>%
  mutate(month = lubridate::month(arrival_date, label = TRUE)) %>%
  ggplot(aes(x = month, y = adr)) +
  geom_point() +
  labs(x = "Month", y = "Average Daily Rate (ADR)",
       title = "ADR by Month") +
  theme_minimal()

Code

# Box plot of ADR by room type
data %>%
  ggplot(aes(x = reserved_room_type, y = adr)) +
  geom_boxplot() +
  labs(x = "Room Type", y = "Average Daily Rate (ADR)",
       title = "ADR by Room Type") +
  theme_minimal()

Looking at our data visualizations, we can see a definite pattern in pricing strategies. The price, also known as Average Daily Rate (ADR), does appear to vary based on the time of year and room type.

Starting with the scatter plot, it indicates that there is a monthly variation in ADR. Certain months appear to have a higher ADR than others, suggesting a potential influence of seasonality on hotel pricing. This could be due to a variety of factors, such as increased demand during holiday seasons or local events taking place.

The box plot displaying ADR by room type reveals a clear stratification of pricing. Each room type has a distinct price range, likely reflecting the different amenities, space, and comfort levels associated with each room.

So, it seems that the time of the year and the type of room are key factors influencing these pricing strategies. However, remember that these two factors are part of a larger, more complex pricing strategy which could also be influenced by factors like market demand, competitive pricing, local events, and economic conditions.

in conclusion(limitations)

• 1. Unanswered Questions: While our visualizations have offered insights on room preferences, cancellation patterns, special requests, and pricing strategies, there are still unanswered questions. For example, how do customer demographics (like age, nationality, etc.) influence these patterns? Do different customer segments (business vs leisure) have different behaviors? Also, questions like the influence of holidays or local events on bookings or cancellations remain unaddressed.
• 2. Unclear Elements for Naive Viewer: Our visualizations assume a level of familiarity with the concepts being visualized, which a naive viewer might not possess. For instance, a naive viewer may not understand what ‘ADR’ represents or what a ‘box plot’ visualizes. Similarly, the use of certain terminologies like ‘lead time’ and ‘is_repeated_guest’ might confuse viewers without a background in hotel operations or data analysis.
• 3. Improvements for Final Project: For the final project, enhancements could be made to improve the visualizations. For example, adding clear legends, title, and labels to each plot can help make the visualizations more understandable. Providing brief descriptions or annotations to guide the viewer through each visualization would also be helpful. Additionally, using interactive plots could help engage viewers better. For instance, with an interactive plot, viewers could hover over a specific data point and get detailed information. Also, it would be beneficial to present more comprehensive insights by incorporating more variables (like customer demographics or market conditions) into the analysis.