Dashboard Link - Link
- To view the dashboard, follow these steps:
- Clone this repository to your local machine or download the Tableau dashboard file or click the above dashboard link.
- Open the dashboard file using Tableau Desktop or Tableau Online.
- Connect the dashboard using the provided dataset given in repository.
- Explore the different pages and visuals within the dashboard to gain insights.
Our primary goal is to conduct EDA on the provided dataset and derive valuable conclusions about broad hotel booking trends and how various factors interact to affect hotel bookings.
We get a dataset of hotel reservations. A city hotel and a resort hotel's reservations are included in this dataset. It has the following features:
- hotel: Name of hotel ( City or Resort)
- is_canceled: Whether the booking is canceled or not (0 for no canceled and 1 for canceled)
- lead_time: time (in days) between booking transaction and actual arrival.
- arrival_date_year: Year of arrival
- arrival_date_month: month of arrival
- arrival_date_week_number: week number of arrival date.
- arrival_date_day_of_month: Day of month of arrival date
- stays_in_weekend_nights: No. of weekend nights spent in a hotel
- stays_in_week_nights: No. of weeknights spent in a hotel
- adults: No. of adults in single booking record.
- children: No. of children in single booking record.
- babies: No. of babies in single booking record.
- meal: Type of meal chosen
- country: Country of origin of customers
- market_segment: What segment via booking was made and for what purpose.
- distribution_channel: Via which medium booking was made.
- is_repeated_guest: Whether the customer has made any booking before(0 for No and 1 for
Yes)
- previous_cancellations: No. of previous canceled bookings.
- previous_bookings_not_canceled: No. of previous non-canceled bookings.
- reserved_room_type: Room type reserved by a customer.
- assigned_room_type: Room type assigned to the customer.
- booking_changes: No. of booking changes done by customers
- deposit_type: Type of deposit at the time of making a booking (No deposit/ Refundable/ No refund)
- agent: Id of agent for booking
- company: Id of the company making a booking
- days_in_waiting_list: No. of days on waiting list.
- customer_type: Type of customer(Transient, Group, etc.)
- adr: Average Daily rate.
- required_car_parking_spaces: No. of car parking asked in booking
- total_of_special_requests: total no. of special request.
- reservation_status: Whether a customer has checked out or canceled,or not showed
- reservation_status_date: Date of making reservation status.
Total 119390 rows and 32 columns in dataset
[1] Removing Duplicate Values
- Rows that were duplicates were removed.
[2] Handling Null / Missing Values
- Children, country, and agent are discrete numerical variables, so replaced null values with mode of it.
- Variable company had null values greater than 50%, so removed it.
[3] Removing Outliers
- Interquartile Range in the skew symmetric curve used to remove outliers found in the lead_time and adr variables.
[4] Converting Columns to Appropriate Data Types
- Datatypes of variables "children," "agent," "reservation_status_date," "total_people," and "total_children" were transformed from float64 datatypes to int64.
- Datatype of the variable "reservation_status_date" was transformed from object datatype to datetime64.
[5] Created New Columns
- The variable "total_stays" is created by adding the variables "stays_in_weekend_nights" and "stays_in_weeknights."
- The variable "total_people" is created by adding the variables "adults," "children," and "babies."
- The variable "reserved_room_assigned" is made up of the variables "reserved_room_type" and "assigned_room_type."
- From variables "children" and "babies," a new "total_children" variable is created by adding both of them.
- The variable "total_people" used to create "guest_category."
- The variable "lead_time_category" created from the variable "lead_time."
performed EDA and tried answering the following questions:
- 1] Is not having a reserved room assigned a reason for booking cancellations?
- 2] Is the high lead_time a reason for booking cancellations?
- 3] How many people are reservations made for?
- 4] Which hotel type has the most advanced reservations?
- 5] Which distribution channels have the most cancellations of bookings?
- 6] Which market segment is most used for booking hotels, and which market segment bookings are most canceled?
- 7] Which room generates a higher ADR?
- 8] Which hotel type is the busiest?
- 9] Which month is the busiest for hotels?
- 10] Which customer type generates more revenue in terms of hotel types and customer types?
- 11] In terms of hotel types, how many parking spaces are most frequently requested by customers?
- 12] What is the most common number of nights booked by customers?
- 13] What is the most common number of special requests made by customers, and what kind of customer are they?
- 14] Is the ADR affected by the hotel not giving a reserved room?
- 15] The majority of bookings were made for how many people, and the majority of cancellations of bookings were made for how many people?
- 16] Which country makes the most reservations, and which agent makes the most bookings?
- 17] Does a longer waiting period cause the cancellation of bookings?
These following graphs and plots were primarily created using Matplotlib and the Seaborn package.
- Count plot
- Bar plot
- Line plot
- Box plot
- Violin plot
- Kdeplot
- Heatmap
- Pairplot
performed univariate analysis and reached the following conclusions:
- A city hotel was most preferred by 61.07 percent of customers over a resort.
- 72.48% of bookings are not cancelled. Almost one-third of all reservations are canceled.
- Bookings increased by 33.28% in 2016 compared to 2015, but fell by 12.25% in 2017.
- Customers make the most reservations in August, followed by July. Customers made the fewest reservations in November, - December, and January. So we can make offers to customers in November, December, and January to maximise booking.
- BB is the most requested food.
- Most of the bookings are made through the online platform.
- The top distribution channel is TA/TO, which is used to make most of the bookings.
- The majority of hotel bookings are made by new customers. Very few customers (3.86%) visited again.
- The customer's top preference is for Room A to be reserved.
- Customers do not want to pay a pre-deposit for a reservation.
- Most customers (80%) preferred to book a hotel for a short stay.
- 90% of people do not require parking spaces for their vehicles.
- 70% chance that bookings will not be cancelled by customers.
- Reserved rooms were not assigned to 15% of customers. Ensure that customers receive the rooms they have reserved.
- Reservations were often made for two people. 10% or so of guests brought their families. Few bring their families with them. Offer family-friendly discounts to encourage reservations for family and business events.
performed bivariate analysis and reached the following conclusions:
- The inability to assign a reserved room to a customer is not grounds for cancellation
- Less lead time means fewer cancellations. Booking cancellations are not caused by a longer Lead time.
- Most customers book hotels for two people (couples). Customers prefer city hotels over resorts for family bookings.
- In comparison to city hotels, guests book resort hotels a little bit in advance.
- The majority of canceled bookings were made through the TA/TO distribution channel
- The majority of hotel reservations are made online, as are the majority of cancellations of reservations made by customers who made their reservations online.
- Room types G, followed by H, generate high ADR
- A city hotel is busier than a resort.
- City hotels generate more revenue (54.86%) than resort hotels (45.14%)
- Only a few customers (8.31%) requested parking. One parking space is most desirable to customers.
- The majority of the guests are staying at the hotel for three nights.
- Customers frequently make one special request. Couples make the majority of special requests.
- Not assigning a reserved room does not affect ADR.
- The majority of reservations are made through country PRT.
- Agent nummber 9 made most number of bookings.
- Longer waiting period is not a reason for booking cancellation.
- People were consistently interested in booking rooms in advance in 2015, 2016, and 2017.
- The top country with the most number of bookings is PRT, and the number one agent with the most number of bookings is 9.
- Customers favored city hotels more than resort hotels by a margin of 61.07 percent.
- One of the four reservations is canceled.
- The most popular food is BB.
- The Online (internet) platform is used to make the majority of bookings.
- The majority of the bookings are made using TA/TO, the leading distribution channel.
- The vast majority of hotel bookings are made by new guests. Almost no consumers (3.86%) returned.
- The customer wants Room A to be reserved the most.
- Customers do not wish to make a bookings with a pre-deposit.
- Customers (80%) favored making a hotel reservation for a short visit.
- Only 10% of people require space to park their cars.
- Most visitors are couples.
- The inability to assign a reserved room to a customer is not grounds for cancellation.
- Booking cancellations are not caused by a longer Lead time.
- A city hotel is busier than a resort.
- The busiest months for hotels are October and September. There isn't a lengthy wait for reservations in July.
- Not assigning a reserved room does not affect ADR.
- The data contained a large number of duplicates.
- The improper data type format was used for the data.
- It was challenging to select the best visualization techniques.
- The dataset contained a large number of null values.