hotel_chain_analysis

Overview

This project leverages Power BI to analyze various aspects of a hotel chain's performance and operations. The analysis includes key metrics such as occupancy rates, revenue per available room (RevPAR), available daily rooms (ADR) , and more. The goal is to provide actionable insights to help improve overall performance, customer experience, and profitability.

Table of Contents

• Overview
• Features
• Datasets
• Installation
• Usage
• Report Structure
• Contributing

Features

• Dynamic Dashboards: Interactive and visually appealing dashboards that provide real-time insights into key performance metrics.
• Comprehensive Analysis: Detailed analysis of occupancy rates, revenue, customer satisfaction, and other critical parameters.
• Trend Analysis: Track performance over time to identify trends and make data-driven decisions.
• Geographic Insights: Visualizations that highlight performance across different regions and locations.
• Customizable Reports: Ability to tailor reports to specific needs and preferences.

Datasets

The following datasets are used in this project:

1. dim_date.csv: includes information about week no and day type like weekend or weekday.
2. dim_hotels.csv: Includes information about the different hotels in the chain, such as location and capacity.
3. dim_rooms.csv: Contains data on room types.
4. fact_aggregated_bookings.csv: Aggregated booking data providing insights into overall booking trends and patterns.
5. fact_bookings.csv: Detailed booking data, including individual reservations, cancellations, and customer details.

Installation

To set up the project locally, follow these steps:

1. Clone the repository: bash git clone https://github.com/yourusername/hotel-chain-powerbi.git

2. Navigate to the project directory: bash cd hotel-chain-powerbi

3. Ensure you have Power BI Desktop installed. You can download it from the official Power BI website.

Usage

1. Open Power BI Desktop.
2. Load the datasets: Connect to the data sources provided in the datasets folder within this repository.
   • dim_date.csv
   • dim_hotels.csv
   • dim_rooms.csv
   • fact_aggregated_bookings.csv
   • fact_bookings.csv
3. Open the Power BI report: Import the .pbix file included in the repository to see the pre-built dashboards and reports.
4. Customize the report: Modify the existing visualizations or create new ones to suit your analysis needs.

Report Structure

The Power BI report is structured into several key sections:

• Overview Dashboard: A high-level view of key performance indicators (KPIs) such as occupancy rate, average daily rate (ADR), and RevPAR.
• Revenue Analysis: Detailed revenue breakdown by room type, booking source, and time period.
• Occupancy Analysis: Insights into occupancy trends, peak periods, and room utilization.
• Customer Feedback: Analysis of customer satisfaction scores and feedback trends.
• Operational Efficiency: Data on staff performance, service quality, and operational metrics.

Contributing

I welcome contributions to enhance the functionality and insights provided by this project. To contribute, follow these steps:

1. Fork the repository.

2. Create a new branch: bash git checkout -b feature/your-feature-name

3. Make your changes and commit them: bash git commit -m 'Add new feature'

4. Push to the branch: bash git push origin feature/your-feature-name

5. Open a pull request describing your changes.