{"id":27185342,"url":"https://github.com/hemangsharma/bookingdataanalysisreport","last_synced_at":"2026-05-14T22:48:46.810Z","repository":{"id":286212810,"uuid":"960732324","full_name":"hemangsharma/BookingDataAnalysisReport","owner":"hemangsharma","description":"The report helps understand key trends and insights around customer bookings, pricing, and other related attributes.","archived":false,"fork":false,"pushed_at":"2025-04-05T00:38:34.000Z","size":137,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-24T02:35:50.159Z","etag":null,"topics":["analysis","data","data-analysis","data-analytics","data-visualization","streamlit","streamlit-dashboard"],"latest_commit_sha":null,"homepage":"https://bookingdataanalysisreport-gqrv8gmmeqdwjbcrjovfk8.streamlit.app/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hemangsharma.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-04-05T00:36:26.000Z","updated_at":"2025-04-05T00:43:32.000Z","dependencies_parsed_at":"2025-04-09T17:46:28.187Z","dependency_job_id":null,"html_url":"https://github.com/hemangsharma/BookingDataAnalysisReport","commit_stats":null,"previous_names":["hemangsharma/bookingdataanalysisreport"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hemangsharma/BookingDataAnalysisReport","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemangsharma%2FBookingDataAnalysisReport","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemangsharma%2FBookingDataAnalysisReport/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemangsharma%2FBookingDataAnalysisReport/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemangsharma%2FBookingDataAnalysisReport/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hemangsharma","download_url":"https://codeload.github.com/hemangsharma/BookingDataAnalysisReport/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemangsharma%2FBookingDataAnalysisReport/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33046696,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-13T13:14:54.681Z","status":"online","status_checked_at":"2026-05-14T02:00:06.663Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","data","data-analysis","data-analytics","data-visualization","streamlit","streamlit-dashboard"],"created_at":"2025-04-09T17:46:25.998Z","updated_at":"2026-05-14T22:48:46.780Z","avatar_url":"https://github.com/hemangsharma.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Booking Data Analysis Report\n\nThis project is a data analysis script written in Python that processes booking data from a CSV file, performs cleaning, and generates a PDF report containing multiple visualizations. The report helps understand key trends and insights around customer bookings, pricing, and other related attributes.\n\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\n\n![](s1.png)\n\n\n---\n\n## 📂 Project Structure\n\n```\n.\n├── file.csv                  # Input booking data (replace with your CSV)\n├── data_analysis.py         # Python script for data cleaning and visualization\n├── data_analysis_report.pdf # Output: PDF report with plots\n├── app.py                   # Streamlit app\n└── README.md                # This file\n```\n\n---\n\n## 📊 Features Comparison: PDF Report vs. Streamlit Dashboard\n\n| Feature                    | PDF Report | Streamlit Dashboard     |\n|---------------------------|------------|--------------------------|\n| Bookings over time        | ✅         | ✅ + Interactivity       |\n| Price distribution        | ✅         | ✅                       |\n| Booking status            | ✅         | ✅                       |\n| Travel purpose            | ✅         | ✅ + Filter              |\n| Device usage              | ✅         | ✅                       |\n| Room count analysis       | ✅         | ✅                       |\n| Adults vs Price           | ✅         | ✅                       |\n| Country-wise bookings     | ✅         | ✅ + Filter              |\n| View raw data             | ❌         | ✅                       |\n| User input (filters)      | ❌         | ✅                       |\n\n---\n\n## 🛠 Features\n\n- Cleans and parses raw booking data, including:\n  - Date fields (`Check-in`, `Check-out`, `Booked on`, `Cancellation date`)\n  - Currency and numeric fields (`Price`, `Commission amount`)\n  - Categorical values (`Status`, `Device`, etc.)\n- Handles missing and malformed data\n- Generates a **multi-page PDF** report with the following plots:\n  1. Total bookings over time (monthly)\n  2. Distribution of booking prices\n  3. Bookings grouped by status\n  4. Average price by travel purpose\n  5. Bookings by device used\n  6. Bookings by number of rooms\n  7. Average price by number of adults\n  8. Bookings by country\n\n---\n\n## 📊 Sample CSV Header (Input Format)\n\nThe input file is expected to have the following columns:\n\n```\nBook number, Booked by, Guest name(s), Check-in, Check-out, Booked on, Status, Rooms, Persons, Adults, Children, Children's age(s), Price, Commission %, Commission amount, Payment status, Payment method, Remarks, Booker group, Booker country, Travel purpose, Device, Unit type, Duration (nights), Cancellation date, Address, Phone number\n```\n\nEnsure column names and formats match this header, especially for date and numeric fields.\n\n---\n\n## 🚀 Getting Started\n\n### Prerequisites\n\n- Python 3.7+\n- pandas\n- matplotlib\n- seaborn\n\nInstall dependencies:\n\n```bash\npip install pandas matplotlib seaborn\n```\n\n### Usage\n\n1. Replace `'file.csv'` in the script with the path to your own CSV file.\n2. Run the script:\n\n```bash\npython data_analysis.py\n```\n\n3. A PDF named `data_analysis_report.pdf` will be generated in the same directory.\n\n---\n\n## 📈 Sample Output (PDF)\n\nThe generated PDF includes multiple graphs for a visual overview of your booking data trends. These can be useful for business reporting, customer behavior analysis, or operational planning.\n\n---\n\n## 📝 Notes\n\n- Rows with missing `Price` or `Commission amount` are dropped during analysis.\n- The script tries to coerce errors in date parsing and numeric conversion.\n- Make sure your input data uses consistent formatting (e.g., `AUD` or `$` in price columns).\n\n\n## 📬 Contact\n\nFor questions or suggestions, feel free to reach out or open an issue.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhemangsharma%2Fbookingdataanalysisreport","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhemangsharma%2Fbookingdataanalysisreport","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhemangsharma%2Fbookingdataanalysisreport/lists"}