{"id":26189566,"url":"https://github.com/gabor-gabor/ii.-data-science-competition","last_synced_at":"2025-07-23T00:34:04.861Z","repository":{"id":281909863,"uuid":"946840563","full_name":"gabor-gabor/II.-Data-Science-Competition","owner":"gabor-gabor","description":"Conducted Exploratory Data Analysis on a real-world but anonymized dataset from MORGENS hotel management system","archived":false,"fork":false,"pushed_at":"2025-06-21T08:20:27.000Z","size":6519,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-21T09:26:22.205Z","etag":null,"topics":["dashboard","data-modeling","dax-functions","m-language","pandas-python","powerbi","powerquery"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# II.-Data-Science-Competition\n# Conducted Exploratory Data Analysis on a real-world but anonymized dataset from [MORGENS](https://morgens.hu/) hotel management system\n\n## 1) Project Background\nThis project focuses on analyzing a comprehensive dataset from the hotel and accommodation industry, covering:\n- **Marketing effectiveness**\n- **Website traffic and user behavior**\n- **Booking engine activity**\n- **Reservation trends and occupancy patterns**\n\n### Objectives\nThe goal is to uncover actionable insights and develop data-driven strategies to optimize and enhance accommodation bookings by:\n- Enhancing marketing efficiency to drive higher returns on investment.\n- Improving website functionality and booking engine efficiency to increase user engagement.\n- Identifying key booking behavior trends to capitalize on high-value opportunities.\n- Providing a strategic framework for boosting hotel booking system performance and profitability in a competitive hospitality market.\n\n## 2) Project Goals and Outcomes\n### **Improving the marketing and booking efficiency of a hotel industry company, thereby contributing to increased profitability.**\n\n### **Key Contributions**\n- **Data preparation and cleansing**\n- **In-depth Exploratory Data Analysis (EDA):**\n  - Examination of search trends\n  - Conversion rate analysis\n  - Insights for revenue and yield management, as well as campaign optimization\n  - Assessment of advertising spend and PPC performance\n- **Strategic recommendations** based on insights to enhance business profitability\n\n### **Datasets**\n- **1 CSV file** – Website activity data\n- **1 CSV file** – Marketing channel data\n- **1 CSV file** – Occupancy data\n- **8 CSV files** – Search and booking data\n- **3 Jupyter Notebook (.ipynb) files** – Data preparation process\n- **1 Power BI (.pbix) file** – Data model\n- **1 PDF file** – Presentation\n\n### **Goals**\nTo process and analyze marketing and booking data across multiple hotel properties, aiming to optimize booking opportunities and enhance profitability.\n\n## 3) Appendix\n\n### **Methodologies**\n- Data Cleaning\n- Data Preparation and Exploration\n- Statistical Metrics Investigation\n- Data Visualization\n- Identifying Business Opportunities\n- Advertising Cost and Medium Performance Analysis\n- Presentation of Actionable Insights\n\n### **IT Tools**\n- Python\n- Power BI\n\n### **Data Preprocessing and Model Building**\n- Data cleaning and preparation using Python\n- Handling outliers and missing data using Power Query in Power BI\n- Joining multiple data tables in Power BI\n- Building a unified data model in Power BI\n- Implementing a **Star Schema** by creating an independent date dimension table and linking it to fact tables\n- Creating and optimizing **DAX formulas**, including complex functions in Power BI\n- Developing **interactive dashboards** in Power BI\n\n### **Current Capabilities \u0026 Key Features**\nThis model is designed to automatically perform all data manipulations, analyses, and reports with a single click as soon as the next month's source tables are added to the source data folder.\n\nThis analysis was initially developed for an internal competition organized by [data36.com](https://data36.com/) data science club, where it earned me the **Medior Special Prize** ranking.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgabor-gabor%2Fii.-data-science-competition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgabor-gabor%2Fii.-data-science-competition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgabor-gabor%2Fii.-data-science-competition/lists"}