hotel_booking_analysis.py

# -*- coding: utf-8 -*-
"""Hotel Booking Analysis.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1JVhm-xVVuDz2vlbMqAoqA5AXS4w6srP7

# **Hotel Booking Analysis**    -

##### **Project Type**    - EDA/Regression/Classification/Unsupervised
##### **Contribution**    - Individual

# **Project Summary -**

The "Hotel Booking Analysis" project aims to analyze and derive insights from a dataset of hotel bookings. By applying data analysis and visualization techniques, the project seeks to provide valuable information for the hospitality industry to optimize their operations, improve customer experiences, and make informed decisions.

The hospitality industry relies on understanding booking patterns, guest preferences, and trends to effectively manage resources, enhance guest satisfaction, and maximize revenue. This project leverages a dataset containing information about hotel bookings, including guest demographics, booking channels, length of stay, and other relevant attributes.

# **GitHub Link -**

Provide your GitHub Link here.

# **Problem Statement**

**1.data-driven insights to optimize operational efficiency.**

**2.analyzing a comprehensive dataset of hotel bookings to uncover patterns, trends, and actionable insights that can drive strategic decision-making and improve overall business performance**

#### **Define Your Business Objective?**

Answer Here.

# **General Guidelines** : -

1.   Well-structured, formatted, and commented code is required.
2.   Exception Handling, Production Grade Code & Deployment Ready Code will be a plus. Those students will be awarded some additional credits.
     
     The additional credits will have advantages over other students during Star Student selection.
       
             [ Note: - Deployment Ready Code is defined as, the whole .ipynb notebook should be executable in one go
                       without a single error logged. ]

3.   Each and every logic should have proper comments.
4. You may add as many number of charts you want. Make Sure for each and every chart the following format should be answered.
        

```
# Chart visualization code
```
            

*   Why did you pick the specific chart?
*   What is/are the insight(s) found from the chart?
* Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

5. You have to create at least 20 logical & meaningful charts having important insights.


[ Hints : - Do the Vizualization in  a structured way while following "UBM" Rule.

U - Univariate Analysis,

B - Bivariate Analysis (Numerical - Categorical, Numerical - Numerical, Categorical - Categorical)

M - Multivariate Analysis
 ]

# ***Let's Begin !***

## ***1. Know Your Data***

### Import Libraries
"""

# Commented out IPython magic to ensure Python compatibility.
# Import Libraries
import numpy as np
import pandas as pd
from numpy import math
from numpy import loadtxt
import seaborn as sns
import matplotlib.pyplot as plt
# %matplotlib inline
from matplotlib import rcParams

import warnings
from google.colab import drive
drive.mount('/content/drive')

"""### Dataset Loading"""

# Load Dataset
filepath="/content/drive/MyDrive/Data Science /Hotel Bookings.csv"       #Make sure you've uploaded the imdb dataset onto your drive. Enter the file path of imdb dataset as a string here.
hotelbooking_df=pd.read_csv(filepath)

"""### Dataset First View"""

# Dataset First Look
hotelbooking_df.head()

"""### Dataset Rows & Columns count"""

# Dataset Rows & Columns count
hotelbooking_df.shape

"""### Dataset Information"""

# Dataset Info
hotelbooking_df.info()

"""#### Duplicate Values"""

# Dataset Duplicate Value Count
len(hotelbooking_df[hotelbooking_df.duplicated()])

"""#### Missing Values/Null Values"""

# Missing Values/Null Values Count
print(hotelbooking_df.isnull().sum())

# Visualizing the missing values
plt.figure(figsize=(12, 8))
sns.heatmap(hotelbooking_df.isnull(), cbar=True)

"""### What did you know about your dataset?

Dataset contain information about Hotel booking system. Dataset has all the history and detail information about booking process.Dataset has 119390 X 32 size. Dataset does not have nay duplicate rows.Dataset has complete information about the booking history like by year,month and also by week wise.

## ***2. Understanding Your Variables***
"""

# Dataset Columns
hotelbooking_df.columns

# Dataset Describe
hotelbooking_df.describe()

"""### Variables Description

***Hotel*** : H1= Resort Hotel H2=City Hotel<br>
***is_cancelled*** : If the booking was cancelled(l) or not(O)<br>
***lead_time*** : Number of days that elapsed between the entering date of the booking into the PMS and the arrival date<br>
***arrival_date_year*** : Year of arrival date<br>
***arrival_date_month*** : Month of arrival date<br>
***arrival_date_week_number*** : Week number for arrival date<br>
***arrival_dat_day*** : 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<br>
***stays_in_week_nights*** : Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel<br>
***adults***: Number of adults<br>
***children*** : Number of children<br>
***babies***: Number of babies<br>
***meal*** : Kind of mealopted for<br>
***country*** : Country code<br>
***market_segment*** : Which segment the customer belongs to<br>
***Distribution _channel*** : How the customer accessed the stay- corporate  booking/Direct/TA.TO<br>
***is_repeated_guest*** : Guest coming for first time or not<br>
***previous_cancellation*** : Was there a cancellation before<br>
***previous_bookings*** : Count of previous bookings<br>
***reserved_room_type*** : Type of room reserved<br>
***assigned_room_type*** : Type of room assigned<br>
***booking_changes*** : Count of changes made to booking<br>
***deposit_type*** : Deposit type<br>
***agent*** : Booked through agent<br>
***days_in_wa iting_list*** : Number of days in wa iting list<br>
***customer_type*** : Type of customer<br>
***required_car_parking*** : If car parking is required<br>
***total_of_special_req***: Number of additional special requirements<br>
***reservation_status*** : Reservation of status<br>
***reservation_status_date*** : Date of the specific status

### Check Unique Values for each variable.
"""

# Check Unique Values for each variable.
for i in hotelbooking_df.columns.tolist():
  print("No. of unique values in ",i,"is",hotelbooking_df[i].nunique(),".")

"""## 3. ***Data Wrangling***

### Data Wrangling Code
"""

# Write your code to make your dataset analysis ready.
# year wise booking information
yearly_counts = hotelbooking_df.groupby('arrival_date_year').size().reset_index(name='hotel_booking_count')
yearly_counts

# month wise hotel booking count
monthly_counts = hotelbooking_df.groupby(['arrival_date_year','arrival_date_month']).size().reset_index(name='hotel_booking_count')
monthly_counts

# customer type wise booking count
customer_type_wise=hotelbooking_df.groupby('customer_type').size().reset_index(name='hotel_booking_count')
customer_type_wise

# Registraion status wise count
status_wise_count=hotelbooking_df.groupby('reservation_status').size().reset_index(name='booking_count');
status_wise_count

#hotel wise booking information
hotel_wise_count=hotelbooking_df.groupby('hotel').size().reset_index(name='booking_count');
hotel_wise_count

pd.DataFrame(hotelbooking_df.groupby('deposit_type')['hotel'].value_counts().reset_index(name="Count"))

df_hotel_cancelled=hotelbooking_df[hotelbooking_df['is_canceled']==1]
# Assigning churn customers data with international plan
df_hotel_not_cancelled=hotelbooking_df[hotelbooking_df['is_canceled']==0]

hotelWise=pd.DataFrame(hotelbooking_df.groupby('hotel')['is_canceled'].value_counts().reset_index(name="Count"))

"""### What all manipulations have you done and insights you found?

1. I have check for duplicate values
2. checked for null or missing values
3. checked shape of Data frame
4. Year wise hotel booking count
5. Customer type wise booking count
6. Hotel wise booking count
7. I have separated out canceled booking and not canceled booking
8. retrived cancelation based booking count
9. retrived deposite
10. Booking status wise booking count

## ***4. Data Vizualization, Storytelling & Experimenting with charts : Understand the relationships between variables***

#### Chart - 1
"""

# Chart - 1 visualization code
plt.bar(customer_type_wise['customer_type'], customer_type_wise['hotel_booking_count'])
plt.xlabel('Customer Type')
plt.ylabel('Booking Count')
plt.title('Graph for Customer Type Hptel Booking')

# Display the graph
plt.show()

"""##### 1. Why did you pick the specific chart?

bar chart could be considered appropriate for this scenario:

Categorical vs. Numerical Data: Your data involves categorical data (customer types) and numerical data (booking counts). A bar chart is a suitable choice to represent this kind of data relationship.

Comparison: A bar chart allows for easy comparison of booking counts between different customer types. The length of each bar directly represents the booking count, making it visually simple to compare.

Clear Labels: You've added clear labels to the x-axis (Customer Type) and y-axis (Booking Count), making it easy for viewers to understand the data being presented.

Simplicity: The chart is straightforward and easy to interpret. It effectively conveys the difference in booking counts across various customer types.

Single Variable Relationship: Since you're focusing on one variable (booking count) in relation to another (customer type), a bar chart is a concise way to show this relationship.

##### 2. What is/are the insight(s) found from the chart?

From the bar chart depicting hotel booking counts by customer type, you can extract several insights:

Customer Type Preferences: You can observe which customer type contributes the most to hotel bookings. This can give you insights into the types of customers that the hotel attracts.

Market Segmentation: If one customer type dominates the booking counts, it suggests a significant market segment for the hotel. Understanding this segment's preferences and needs can guide marketing and service strategies.

Loyalty Programs: If there's a noticeable difference in booking counts between customer types, it might indicate the effectiveness of loyalty programs or special offers targeting specific customer segments.

Operational Planning: The chart can inform resource allocation and staff planning based on the dominant customer type. For example, if business travelers are a significant segment, the hotel can ensure amenities for work-related needs.

Service Customization: Insights into the booking preferences of different customer types can help tailor services and amenities to match their needs, leading to enhanced guest satisfaction.

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

The insights gained from the bar chart depicting hotel booking counts by customer type can indeed help create a positive business impact, but there are also insights that could potentially lead to negative growth. Let's explore both scenarios:

Positive Business Impact:

Targeted Marketing: Insights into customer type preferences can guide targeted marketing efforts. By tailoring marketing campaigns to attract the customer types that are already booking, the hotel can increase bookings from those segments.

Service Customization: Understanding the dominant customer types allows the hotel to customize its services and amenities to better meet the needs and expectations of those customers. This can lead to higher guest satisfaction and repeat business.

Loyalty Programs: If certain customer types contribute significantly to bookings, implementing or enhancing loyalty programs for those segments can foster repeat business and positive word-of-mouth recommendations.

Operational Efficiency: Resource allocation and staffing can be optimized based on the dominant customer types. This can prevent overallocation or underallocation of resources and improve overall operational efficiency.

Negative Growth Insights:

Overdependence on a Single Segment: If the booking counts are heavily skewed towards a single customer type, it might lead to negative growth if that segment faces economic challenges or changes in preferences. The hotel becomes vulnerable if this segment declines.

#### Chart - 2
"""

# Chart - 2 visualization code
plt.figure(figsize=(8, 6))
sns.barplot(x='hotel', y='Count', hue='is_canceled', data=hotelWise)

# Adding labels and title
plt.xlabel('Hotel')
plt.ylabel('Count')
plt.title('Cancellation Counts by Hotel Type')

# Show the legend
plt.legend(title='is_canceled')

# Display the graph
plt.show()

"""##### 1. Why did you pick the specific chart?

a grouped bar plot with hue differentiation based on the assumption that you are interested in comparing cancellation counts between two different hotel types (City Hotel and Resort Hotel) while also distinguishing between the cancellation statuses ('0' and '1') within each hotel type. Here's why this specific chart could be suitable:

Comparison of Multiple Categories: The grouped bar plot with hue differentiation allows you to compare cancellation counts across multiple categories (hotel types and cancellation statuses) in a single visualization.

Categorical vs. Numerical Data: The chart is effective when you have categorical data (hotel types and cancellation statuses) and numerical data (cancellation counts) that you want to compare.

Clear Representation: Grouped bars with distinct colors for each cancellation status provide a clear visual separation between the different cancellation statuses within each hotel type.

Hotel Type Comparison: The grouped bars make it easy to compare cancellation counts between the two hotel types side by side, allowing you to quickly identify if one hotel type has a higher or lower cancellation rate than the other.

##### 2. What is/are the insight(s) found from the chart?

From the grouped bar plot depicting cancellation counts by hotel type and cancellation status, you can derive several insights:

Hotel Type Comparison: You can directly compare cancellation counts between the two hotel types (City Hotel and Resort Hotel) for each cancellation status. This comparison can help identify if there are differences in cancellation rates between the two types.

Cancellation Patterns: By analyzing the bars within each hotel type, you can identify which cancellation status ('0' or '1') dominates. This can provide insights into the predominant cancellation behavior for each hotel type.

Relative Cancellation Rates: Comparing the heights of the bars within each hotel type allows you to determine the relative cancellation rates. If one hotel type consistently has taller bars for '1' (canceled) status, it suggests a higher cancellation rate for that type.

Cancellation Impact: The chart can help understand the potential impact of cancellations on each hotel type. If one type has a significantly higher number of canceled bookings, it might imply challenges in terms of revenue loss and resource allocation.

Cancellation Behavior Differences: If the pattern of cancellation counts differs between the two hotel types, it could indicate different guest behaviors, policies, or market dynamics affecting cancellations.

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

The insights gained from the grouped bar plot depicting cancellation counts by hotel type and cancellation status can indeed lead to both positive business impacts and insights that might raise concerns related to negative growth. Let's explore both scenarios:

Positive Business Impact:

Operational Efficiency: If one hotel type consistently shows lower cancellation counts for the '1' (canceled) status, it indicates efficient operations and effective guest management strategies. This can lead to positive guest experiences, repeat business, and positive word-of-mouth referrals.

Strategic Decisions: If insights reveal that one hotel type has a higher '0' (not canceled) status, it suggests that this type is managing bookings effectively. Strategic decisions can be made based on this knowledge to optimize resources and enhance revenue generation.

Guest Communication: Insights into cancellation patterns can guide improvements in guest communication strategies, such as offering incentives for early bookings or sending reminders to reduce last-minute cancellations.

Revenue Management: Understanding differences in cancellation patterns between hotel types can inform revenue management strategies. For instance, one hotel type might implement policies that lead to better revenue protection during peak seasons.

Negative Growth Insights:

Impact of Cancellations: If one hotel type consistently experiences higher cancellation counts for the '1' (canceled) status, it might lead to negative growth due to potential revenue loss and resource inefficiencies. Frequent cancellations can disrupt revenue projections and affect overall performance.

#### Chart - 3
"""

# Chart - 3 visualization code

plt.pie(status_wise_count['booking_count'], labels=status_wise_count['reservation_status'], autopct='%1.1f%%', startangle=140)

# Equal aspect ratio ensures that pie is drawn as a circle
plt.axis('equal')

# Title
plt.title('Reservation Status Distribution')

# Display the chart
plt.show()

"""##### 1. Why did you pick the specific chart?

pie chart based on the assumption that you are visualizing the distribution of reservation statuses (reservation_status) based on their booking counts. Here's why a pie chart could be considered in this context:

Percentage Distribution: A pie chart is effective when you want to show the percentage distribution of different categories within a whole. In your case, you are displaying the distribution of reservation statuses, and a pie chart can easily communicate the proportion of each status relative to the total.

Limited Categories: If you have a relatively small number of reservation statuses (e.g., Canceled, Check-Out, No-Show), a pie chart can help viewers quickly grasp the relative proportions of each status.

Visual Representation: The circular nature of the pie chart allows for a visual representation of how each category contributes to the whole, making it easy to see which status has the highest and lowest percentages.

##### 2. What is/are the insight(s) found from the chart?

From the pie chart visualizing the distribution of reservation statuses based on booking counts, you can gather several insights:

Check-Out Dominance: If one slice of the pie is significantly larger than the others, it suggests that the most common reservation status is "Check-Out." This might indicate that a majority of bookings result in successful stays without cancellations or no-shows.

Cancellation Rate: The size of the "Canceled" slice relative to the whole pie can give you an idea of the proportion of bookings that end up being canceled. This insight is valuable for understanding the potential impact of cancellations on revenue and resource allocation.

No-Show Proportion: The "No-Show" slice indicates the percentage of reservations where guests didn't show up. This insight can help identify if there's a significant issue with guests failing to arrive despite making reservations.

Operational Efficiency: The pie chart can provide insights into the efficiency of hotel operations. A high percentage of "Check-Out" statuses might indicate that guests are satisfied and staying for the intended duration.

Booking Management: If the "Canceled" slice is large, it suggests that cancellations are a common occurrence. This might lead to investigating the reasons for cancellations and implementing strategies to reduce them.

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

The insights gained from the pie chart representing the distribution of reservation statuses can indeed help create a positive business impact, as well as highlight areas that might lead to negative growth. Let's explore both scenarios:

Positive Business Impact:

Operational Efficiency: Understanding that a significant portion of reservations result in successful check-outs can indicate that the hotel's operational processes are effective. This can lead to positive guest experiences, repeat business, and positive word-of-mouth referrals.

Booking Management: Recognizing that the "Canceled" slice is relatively small can indicate efficient booking management. This can help the hotel maintain stable revenue and occupancy levels without the disruption caused by frequent cancellations.

#### Chart - 4
"""

# Chart - 4 visualization code
#Numerical data

plt.figure(figsize=(8, 6))
sns.barplot(x='arrival_date_year', y='hotel_booking_count', data=yearly_counts)

# Adding labels and title
plt.xlabel('Booking Count')
plt.ylabel('Year')
plt.title('Hotel Booking Count by Arrival Year')

# Display the graph
plt.show()

"""##### 1. Why did you pick the specific chart?

Categorical vs. Numerical Data: You have categorical data (arrival years) and numerical data (booking counts) to compare. A bar plot is well-suited for displaying this type of data relationship.

Comparison: A bar plot allows for easy comparison of booking counts between different years. The length of each bar visually represents the magnitude of the booking count, making comparisons intuitive.

Simple Interpretation: The plot is easy to interpret. Each bar corresponds to a specific year, making it straightforward to see which year had the highest or lowest booking counts.

Labeling: The x-axis can be labeled with the years, and the y-axis can be labeled with the booking counts. This labeling makes it clear what the data represents.

##### 2. What is/are the insight(s) found from the chart?

Based on the bar plot depicting the hotel booking counts by arrival year, several insights can be derived:

Yearly Booking Trends: The chart clearly shows the variation in hotel booking counts across different years. For example, it's evident that the year 2016 had the highest booking count, followed by 2017 and then 2015.

Growth or Decline: The comparison between years allows you to identify trends in booking growth or decline. In this case, there seems to be an upward trend from 2015 to 2016, with a subsequent decrease in 2017.

Seasonal Patterns: While the chart focuses on the entire year, it might also reveal patterns within each year. You could investigate whether there are specific months within each year that contribute more to the higher booking counts.

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

The gained insights from the chart depicting hotel booking counts by arrival year can potentially lead to both positive business impacts and insights that might raise concerns related to negative growth. Let's explore both scenarios:

Positive Business Impact:

Strategic Resource Allocation: If the chart shows an increase in booking counts over the years, the hotel can allocate resources more effectively during peak seasons to provide exceptional customer service, leading to positive guest experiences and potential repeat business.

Optimized Marketing Efforts: Recognizing trends in booking counts allows the hotel to tailor marketing campaigns more strategically. For example, during the months with historically lower booking counts, the hotel can run promotions to attract guests and increase occupancy

#### Chart - 5
"""

# Chart - 5 visualization
# Pivot the data for the heatmap
pivot_table = monthly_counts.pivot_table(index='arrival_date_month', columns='arrival_date_year', values='hotel_booking_count')

# Create a heatmap using Seaborn
plt.figure(figsize=(10, 8))
sns.heatmap(pivot_table, annot=True, cmap='YlGnBu')

# Adding labels and title
plt.xlabel('Year')
plt.ylabel('Month')
plt.title('Hotel Booking Count Heatmap')

# Display the graph
plt.show()

"""##### 1. Why did you pick the specific chart?

In this heatmap, each cell represents the hotel booking count for a specific month and year. The color intensity indicates the booking count value. Annotations within the cells display the exact count values. I have three types of data based on year monthly counts of booking

##### 2. What is/are the insight(s) found from the chart?

From the heatmap visualization of the hotel booking count data across different months and years, you can potentially derive several insights:

Seasonal Patterns: You can observe if there are any consistent seasonal patterns in hotel bookings across the years. For example, certain months might show higher booking counts, indicating popular travel seasons.

Yearly Trends: By comparing rows (months) across different columns (years), you can identify trends in booking counts from one year to another. This can help you understand if there has been an overall increase or decrease in bookings over time.

Peak Booking Months: The heatmap can highlight the months with the highest booking counts. If specific months consistently stand out across the years, it might suggest that those months are popular for travel.

Off-Peak Months: On the flip side, you can identify months with lower booking counts. These months might indicate off-peak travel periods or times when the hotel industry experiences reduced demand.

Year-to-Year Comparisons: By looking at the diagonal from the bottom left to the top right, you can compare booking counts for the same months across different years. This comparison can reveal trends and anomalies, such as a sudden increase in bookings for a certain month in a specific year.

Identifying Outliers: You might spot outlier values that don't fit the general pattern. These outliers could indicate special events or anomalies affecting booking counts.

Seasonal Variations by Year: Comparing the columns for each year, you can analyze how booking counts vary for each

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

The insights gained from the heatmap can indeed have a positive business impact if used strategically and in combination with domain knowledge. However, it's important to note that the insights are interpretative and require further analysis and action to result in concrete benefits. Let's evaluate both positive and potentially negative impacts based on the provided heatmap insights:

Positive Business Impact:

Optimized Marketing Campaigns: By identifying peak booking months and seasonal trends, the hotel can allocate marketing efforts and resources more effectively, targeting potential customers during periods of high demand.

Strategic Pricing: The insights about high and low booking months can guide pricing strategies. Higher demand months might justify slightly higher prices, while lower demand months might benefit from promotions to boost occupancy.

Resource Allocation: The heatmap can help with staffing and inventory management. For example, during peak months, the hotel can ensure sufficient staff and amenities are available to provide a better guest experience.

Special Events Planning: If certain months have consistently high or low booking counts due to local events or holidays, the hotel can plan special packages, offers, or events to capitalize on these patterns.

Negative Growth Insights:

Negative Impact during Off-Peak Months: If the heatmap reveals a consistent pattern of low booking counts during certain months across multiple years, it might indicate a challenge during those periods. The hotel could experience reduced revenue and occupancy during these times, potentially leading to negative growth.

#### Chart - 6
"""

# Chart - 6 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 7
"""

# Chart - 7 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 8
"""

# Chart - 8 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 9
"""

# Chart - 9 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 10
"""

# Chart - 10 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 11
"""

# Chart - 11 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 12
"""

# Chart - 12 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 13
"""

# Chart - 13 visualization code

"""##### 1. Why did you pick the specific chart?

Answer Here.

##### 2. What is/are the insight(s) found from the chart?

Answer Here

##### 3. Will the gained insights help creating a positive business impact?
Are there any insights that lead to negative growth? Justify with specific reason.

Answer Here

#### Chart - 14 - Correlation Heatmap
"""

# Correlation Heatmap visualization
correlation_matrix = hotelbooking_df.corr()

# Create a heatmap using Seaborn
plt.figure(figsize=(15, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Heatmap')

# Display the heatmap
plt.show()

"""##### 1. Why did you pick the specific chart?

heatmap to visualize the correlation matrix for several reasons:

Multiple Variables: A heatmap is effective when you have multiple variables (columns) and want to visualize the correlations between all possible pairs of variables in a single chart.

Matrix Representation: A correlation matrix is a 2D matrix, and a heatmap is a suitable visualization for displaying the matrix's values using color gradients.

Color Representation: Heatmaps use color gradients to represent the strength and direction of correlations. Positive correlations can be displayed in one color, negative correlations in another, and a gradient of colors in between.

At-a-Glance Interpretation: A heatmap allows for quick and intuitive interpretation. Strong correlations (positive or negative) are immediately noticeable as they stand out in distinct colors.

Annotations: You can annotate the heatmap with actual correlation coefficient values, providing quantitative information to complement the visual representation.

Visualizing Patterns: Heatmaps make it easy to identify patterns and relationships between variables. Clusters of high positive or negative correlations can be identified visually.

Simplicity: Heatmaps condense complex information into a simple visual representation, making it easier to identify relationships among variables.

Complex Data Exploration: For datasets with many variables, a heatmap provides a visual summary of how variables relate to each other, aiding in the exploration of data patterns.

##### 2. What is/are the insight(s) found from the chart?

Insights from a correlation heatmap can provide valuable information about relationships between variables in your dataset. Here are the types of insights you can gain:

Strong Positive Correlations: Areas of the heatmap with bright colors (usually towards the upper right and lower left corners) indicate strong positive correlations. This suggests that as one variable increases, the other tends to increase as well. These insights can identify variables that move together and might be related in some way.

Strong Negative Correlations: Dark-colored areas (often in the upper left corner) represent strong negative correlations. This indicates that as one variable increases, the other tends to decrease. Negative correlations can reveal relationships where changes in one variable are associated with changes in the opposite direction in another variable.

Weak or No Correlations: Light-colored or neutral areas suggest weak or no correlations between variables. These insights indicate that changes in one variable are not strongly associated with changes in the other variable.

Clusters of Correlations: Clusters of high positive or negative correlations can reveal groups of variables that are interrelated. This might suggest common underlying factors or shared influences.

Identifying Patterns: The arrangement of colors and values in the heatmap can help you identify patterns in your data. For instance, you might observe that certain variables are strongly correlated with each other, while others show no clear relationship.

Variable Selection: Correlation heatmaps can help you select variables for further analysis. Variables with strong correlations might be candidates for inclusion in regression models or other analyses

#### Chart - 15 - Pair Plot
"""

# Pair Plot visualization
sns.pairplot(hotelbooking_df)
plt.show()

"""##### 1. Why did you pick the specific chart?

A heatmap is a more concise and direct way to visualize the correlations between variables. It provides a clear visual representation of how variables correlate with each other using color gradients. On the other hand, a pair plot displays scatter plots and histograms for each pair of variables, which might not effectively convey the complete correlation matrix.

##### 2. What is/are the insight(s) found from the chart?

Correlations: You can visually identify positive, negative, or weak correlations between pairs of variables. Scatter plots that exhibit trends (upward or downward) suggest a linear relationship between the variables.

Clusters and Patterns: Clusters of points in scatter plots might indicate groups of variables that are related to each other. Identifying clusters can suggest underlying themes or patterns within your data.

Outliers: Outliers, which are data points that deviate significantly from the rest of the data, can be observed as points far away from the main cloud of data in scatter plots.

Distributions: Histograms along the diagonal of the pair plot show the distribution of individual variables. You can observe whether variables are normally distributed or skewed.

Multicollinearity: If you're working with predictive modeling, a pair plot can help you identify multicollinearity – the presence of high correlations between independent variables. This can impact the accuracy and stability of your model.

## **5. Solution to Business Objective**

#### What do you suggest the client to achieve Business Objective ?
To achieve the business objectives for hotel booking analysis, I would suggest the following steps and strategies to the client:

1. **Data Collection and Preparation:**
   - Collect comprehensive data on bookings, cancellations, guest profiles, and preferences.
   - Clean and preprocess the data to ensure accuracy and consistency.

2. **Customer Segmentation:**
   - Analyze booking data to identify distinct customer segments based on demographics, booking behavior, and preferences.
   - Tailor marketing strategies and services to meet the specific needs of each segment.

3. **Demand Forecasting and Pricing Optimization:**
   - Analyze historical booking trends to predict future demand patterns.
   - Implement dynamic pricing strategies that consider demand fluctuations and lead times.

4. **Enhanced Customer Experience:**
   - Analyze guest feedback and reviews to identify areas for improvement.
   - Implement measures to enhance guest experience, such as personalized services and prompt issue resolution.

5. **Cancellation Reduction Strategies:**
   - Analyze reasons for cancellations and no-shows to identify common trends.
   - Implement policies that encourage early bookings and offer incentives to reduce cancellations.

6. **Effective Marketing and Advertising:**
   - Analyze booking sources and channels that yield the highest conversions.
   - Allocate marketing budget to platforms that bring in the most valuable bookings.

7. **Competitor Analysis:**
   - Analyze competitor offerings, pricing, and guest reviews to identify strengths and weaknesses.
   - Leverage competitive insights to differentiate the hotel's offerings and marketing campaigns.

8. **Resource Optimization:**
   - Analyze peak booking periods and occupancy rates to optimize staffing and facility allocation.
   - Ensure adequate resources are available during high-demand periods.

9. **Trend Adaptation:**
   - Continuously monitor industry trends and guest preferences.
   - Adapt offerings and services to align with emerging trends, enhancing the hotel's appeal.

10. **Guest Loyalty Programs:**
    - Identify repeat guests and create a loyalty program that rewards frequent bookings.
    - Provide special incentives to returning guests to encourage repeat business.

11. **Regular Data Analysis and Feedback Loop:**
    - Establish a feedback loop for continuous improvement based on data insights.
    - Regularly review and analyze data to identify areas for optimization and innovation.

12. **Investment in Technology:**
    - Consider investing in technology solutions for online bookings, guest communication, and data analytics.
    - Use technology to streamline operations and enhance guest experience.

13. **Employee Training and Engagement:**
    - Train staff to provide exceptional guest service and handle guest interactions professionally.
    - Employee satisfaction contributes to positive guest experiences.

14. **Collaboration with Partners:**
    - Collaborate with travel agencies, tour operators, and other partners to expand the hotel's reach.
    - Offer attractive packages and deals through partnerships.

15. **Feedback Incorporation:**
    - Collect feedback from guests and use it to implement improvements.
    - Demonstrate responsiveness to guest needs and preferences.

Remember, achieving the business objectives requires a comprehensive and integrated approach. Regular monitoring of performance metrics and continuous refinement of strategies based on data-driven insights will contribute to the hotel's success in optimizing bookings and overall business performance.

# **Conclusion**

In conclusion, the analysis of hotel booking data presents a wealth of opportunities for enhancing business operations, guest satisfaction, and overall revenue. By leveraging data-driven insights and implementing strategic initiatives, hotels can achieve their business objectives more effectively. Here are the key takeaways:

1. **Data as a Strategic Asset:** Hotel booking data is a valuable asset that can guide decision-making and strategy formulation. Extract insights from this data to drive informed actions.

2. **Customer-Centric Approach:** Understanding customer segments, preferences, and behaviors is crucial. Tailor marketing, services, and experiences to cater to diverse guest needs.

3. **Operational Efficiency:** Demand forecasting, pricing optimization, and resource allocation contribute to efficient operations, improved occupancy rates, and reduced revenue leakage.

4. **Guest Experience Enhancement:** Act on guest feedback, adapt to trends, and provide personalized services to elevate the guest experience and drive loyalty.

5. **Marketing Effectiveness:** Data analysis helps identify successful marketing channels and campaigns, leading to better allocation of resources and increased bookings.

6. **Competitive Edge:** Analyzing competitors and differentiating offerings based on data insights can position the hotel favorably in the market.

7. **Continuous Improvement:** Regular data analysis and a feedback loop ensure the continuous enhancement of operations, services, and guest interactions.

8. **Technology Integration:** Embrace technology solutions for seamless online booking, communication, and analytics, streamlining processes and enhancing guest convenience.

9. **Employee Empowerment:** Well-trained and engaged staff contribute to positive guest experiences, reinforcing the hotel's reputation.

10. **Loyalty and Growth:** Implement guest loyalty programs and strategies to incentivize repeat bookings, driving long-term growth.

Ultimately, the effective utilization of hotel booking data enables data-driven decision-making, optimized operations, and the delivery of exceptional guest experiences. By consistently monitoring performance metrics, adapting to changing trends, and embracing innovation, hotels can stay competitive and achieve sustained growth in the dynamic hospitality industry.

### ***Hurrah! You have successfully completed your EDA Capstone Project !!!***
"""