🏨 Add Comprehensive Hotel Guests Data Analysis with Interactive Charts #7
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
- Added complete CSV dataset with 71 hotel guest records - Created comprehensive Python analysis script with 5 key analysis areas: * Room type distribution and revenue analysis * Rewards program impact on guest behavior * Seasonal booking patterns and trends * Geographic distribution of guests by state * Amenities fees and length of stay patterns - Generated 5 high-quality visualization charts (PNG format) - Created detailed analysis report with business recommendations - Added README with complete documentation and usage instructions - Included requirements.txt for easy dependency management Key insights discovered: - 26.8% rewards program participation rate - BASIC rooms are most popular (73% of bookings) - July is peak booking month - Average stay duration is 2.3 days - Total revenue analyzed: 3,563.06 across 71 guests
Reviewer's GuideIntroduces a complete Python-based hotel guest data analysis suite by adding a data ingestion and cleaning pipeline, modular visualization functions for five focus areas, automated report generation, CLI orchestration, and accompanying documentation, sample data, and dependencies. Sequence Diagram for Hotel Guests Analysis Script ExecutionsequenceDiagram
actor User
participant S as hotel_guests_analysis.py
participant LDC as load_and_clean_data()
participant RTA as create_room_type_analysis()
participant RPA as analyze_rewards_program()
participant SPA as analyze_seasonal_patterns()
participant GDA as analyze_geographic_distribution()
participant ASA as analyze_amenities_and_stay_patterns()
participant GSR as generate_summary_report()
User->>S: Executes script with guests.csv
S->>LDC: Call with filepath 'guests.csv'
LDC-->>S: Returns cleaned DataFrame (df)
S->>RTA: Call with df
RTA-->>S: Saves room_type_analysis.png
S->>RPA: Call with df
RPA-->>S: Saves rewards_program_analysis.png
S->>SPA: Call with df
SPA-->>S: Saves seasonal_patterns.png
S->>GDA: Call with df
GDA-->>S: Saves geographic_distribution.png
S->>ASA: Call with df
ASA-->>S: Saves amenities_stay_patterns.png
S->>GSR: Call with df
GSR-->>S: Saves analysis_report.txt
S-->>User: Displays completion message & summary
Entity Relationship Diagram for Guest Record DataerDiagram
GuestRecord {
string guest_email "Primary key (implicit)"
string hotel_id
bool has_rewards
string room_type
float amenities_fee
datetime checkin_date
datetime checkout_date
float room_rate
string billing_address
string credit_card_number
int length_of_stay "Derived"
int checkin_month "Derived"
string checkin_season "Derived"
string state "Derived from billing_address"
float total_cost "Derived"
}
Class Diagram for the Guest Data Structure (Pandas DataFrame)classDiagram
class GuestDataFrame {
<<DataFrame>>
+guest_email: string
+hotel_id: string
+has_rewards: bool
+room_type: string
+amenities_fee: float
+checkin_date: datetime
+checkout_date: datetime
+room_rate: float
+billing_address: string
+credit_card_number: string
+length_of_stay: int
+checkin_month: int
+checkin_season: string
+state: string
+total_cost: float
}
note for GuestDataFrame "Represents the pandas DataFrame structure after loading and cleaning. New columns (length_of_stay, checkin_month, etc.) are added by the script."
File-Level Changes
Tips and commandsInteracting with Sourcery
Customizing Your ExperienceAccess your dashboard to:
Getting Help
|
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
📊 Hotel Guests Data Analysis & Visualization Suite
This PR adds a complete data analysis solution for hotel guest data, featuring comprehensive visualizations and actionable business insights.
🎯 What's Included
📁 Complete Analysis Package:
📈 Analysis Areas Covered
🏨 Room Type Analysis
🎁 Rewards Program Impact
🗓️ Seasonal Booking Patterns
🗺️ Geographic Distribution
🛎️ Amenities & Stay Patterns
💡 Key Business Insights
🚀 How to Use
📁 Generated Files
room_type_analysis.png- Room type distribution and revenuerewards_program_analysis.png- Rewards program impactseasonal_patterns.png- Seasonal booking trendsgeographic_distribution.png- Geographic guest distributionamenities_stay_patterns.png- Amenities and stay durationanalysis_report.txt- Comprehensive business report🎨 Visualization Features
This analysis provides actionable insights for hotel revenue optimization, customer loyalty programs, and operational planning.
Generated by Codegen AI Assistant
💻 View my work • About Codegen
Summary by Sourcery
Add a complete hotel guests data analysis and visualization suite that loads booking data, performs multi-faceted analysis, generates high-resolution charts, and produces a comprehensive business insights report.
New Features:
Documentation: