{"id":26873599,"url":"https://github.com/deller23/hotel_booking_data_cleaning","last_synced_at":"2025-03-31T09:19:37.098Z","repository":{"id":285354822,"uuid":"957774636","full_name":"Deller23/hotel_booking_data_cleaning","owner":"Deller23","description":"Efficiently transforming raw hotel booking data into actionable insights! This project leverages Python and Pandas for advanced data cleaning—handling missing values, detecting outliers, and optimizing features—ensuring a high-quality dataset ready for analysis and modeling.","archived":false,"fork":false,"pushed_at":"2025-03-31T06:21:55.000Z","size":2142,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-31T08:41:21.250Z","etag":null,"topics":["data-analysis","data-cleaning","data-preprocessing","data-visualization","data-wrangling","pandas","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/Deller23.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":"2025-03-31T05:29:33.000Z","updated_at":"2025-03-31T06:28:12.000Z","dependencies_parsed_at":"2025-03-31T08:41:22.780Z","dependency_job_id":"e2a9f963-8e0d-4443-88d1-9c51f94bdd73","html_url":"https://github.com/Deller23/hotel_booking_data_cleaning","commit_stats":null,"previous_names":["deller23/hotel_booking_data_cleaning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deller23%2Fhotel_booking_data_cleaning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deller23%2Fhotel_booking_data_cleaning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deller23%2Fhotel_booking_data_cleaning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Deller23%2Fhotel_booking_data_cleaning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Deller23","download_url":"https://codeload.github.com/Deller23/hotel_booking_data_cleaning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246443534,"owners_count":20778252,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["data-analysis","data-cleaning","data-preprocessing","data-visualization","data-wrangling","pandas","python"],"created_at":"2025-03-31T09:19:36.486Z","updated_at":"2025-03-31T09:19:37.087Z","avatar_url":"https://github.com/Deller23.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Hotel Booking Data Cleaning\r\n\r\n## 📌 Project Overview\r\n\r\nThis project involves cleaning and preprocessing a hotel booking dataset. The dataset undergoes various data cleaning steps such as handling missing values, dealing with outliers, and transforming categorical features.\r\n\r\n## 📂 Files in This Repository\r\n\r\n- `hotel_bookings_cleaned.ipynb` → Jupyter Notebook containing the full data preprocessing steps.\r\n- `hotel_bookings.csv` → Raw dataset (Make sure to download it if not included).\r\n- `README.md` → This file, explaining the project and how to run it.\r\n\r\n## 📥 Dataset\r\n\r\nThe dataset can be downloaded from [Kaggle](https://www.kaggle.com/datasets) or another specified source. Make sure to place it in the **same directory** as the notebook before running the code.\r\n\r\n## 🛠️ Steps Performed in Data Cleaning\r\n\r\n1. **Handling Missing Values** → Filled or removed missing data in columns such as `agent`, `country`, etc.\r\n2. **Removing Outliers** → Identified and treated outliers in numerical columns like `adr`.\r\n3. **Encoding Categorical Features** → Converted categorical variables (`arrival_date_month`, etc.) into numerical format.\r\n4. **Dropping Unnecessary Columns** → Removed columns that are not useful for analysis.\r\n5. **Final Cleaned Dataset Output** → The cleaned data is saved as `hotel_bookings_cleaned.csv`.\r\n\r\n## 🚀 How to Run the Notebook\r\n\r\n### **Using Jupyter Notebook (Locally)**\r\n\r\n1. Install Jupyter if not installed:\r\n   ```bash\r\n   pip install notebook pandas numpy\r\n   ```\r\n2. Open the notebook:\r\n   ```bash\r\n   jupyter notebook\r\n   ```\r\n3. Navigate to `hotel_bookings_cleaned.ipynb` and run all cells.\r\n4. The cleaned dataset `hotel_bookings_cleaned.csv` will be generated in the same directory.\r\n\r\n### **Using Google Colab**\r\n\r\n1. Upload `hotel_bookings_cleaned.ipynb` and `hotel_bookings.csv` to Colab.\r\n2. Run all cells.\r\n3. Download the cleaned CSV using:\r\n   ```python\r\n   from google.colab import files\r\n   files.download(\"hotel_bookings_cleaned.csv\")\r\n   ```\r\n\r\n## 📌 Notes\r\n\r\n- If you encounter missing file errors, make sure `hotel_bookings.csv` is in the correct location.\r\n- You can modify the notebook to include additional data processing steps as needed.\r\n\r\n---\r\n\r\nLet me know if you have any questions or need further modifications! 🚀\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeller23%2Fhotel_booking_data_cleaning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeller23%2Fhotel_booking_data_cleaning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeller23%2Fhotel_booking_data_cleaning/lists"}